Head to head: Meta: Muse Spark Open vs DeepSeek-V4-Pro

Meta: Muse Spark Open: # Arcwell Launches Relay to Automate Customer Support Work *The AI platform suggests answers, flags at-risk customers and summarizes tickets — but early results are narrow.* Arcwell has launched Relay, an artificial intelligence platform that analyzes customer support conversations and suggests responses for agents to use. The company says Relay is designed to cut the time agents spend hunting through internal knowledge bases. In addition to drafting suggested answers, the system flags customers who appear likely to cancel and generates summaries of support interactions. Relay was tested with 12 companies over six months ahead of its wider release. Arcwell said agents using the platform closed tickets 14% faster than those who did not. It did not release results on whether the tool helped with customer retention, one of its stated capabilities. Relay will cost $65 per agent per month. "Support agents spend too much time searching for information that their companies already have," CEO Priya Shah said. The approach comes with trade-offs. To function, Relay needs access to support transcripts and internal help documents, a requirement that may give some buyers pause. Arcwell said customer data handled by Relay is encrypted and is not used to train shared models. Customers can select either a 30-day or 90-day retention period for their data. The pricing puts Relay in the middle of a crowded field of AI assistants for support teams, where vendors are competing to show they can lift productivity without adding errors or robotic replies. For many companies, the calculation will come down to whether time saved on tickets outweighs the oversight needed to catch AI mistakes and the privacy review required to hand over conversation histories. That pitch lands as support teams face pressure to resolve tickets faster without adding headcount, even as employers and customers remain wary of AI-generated answers. How well Relay balances speed with accuracy — and whether its churn predictions prove useful in practice — will become clearer once it moves beyond early testing. Until broader deployment data appears, those trade-offs remain largely theoretical.

DeepSeek-V4-Pro: # Arcwell Launches Relay, an AI Layer That Reads Customer Conversations in Real Time Arcwell is betting that the next breakthrough in customer support won’t come from faster chatbots but from software that sits quietly beside human agents and tells them exactly what to say. That vision arrived Tuesday in the form of Relay, an AI platform that scans live support conversations, surfaces suggested replies, flags at-risk customers, and generates post-chat summaries automatically. The launch marks a strategic pivot for Arcwell, which until now had concentrated on backend knowledge management. Relay instead works inside the flow of a conversation, ingesting support transcripts and internal help documents to serve agents plain-language prompts while a customer is still on the line. “Support agents spend too much time searching for information that their companies already have,” CEO Priya Shah said in a statement accompanying the release. Twelve companies tested Relay over a six-month pilot. According to Arcwell, agents using the platform closed tickets 14% faster than their peers. The company did not publish customer-retention figures from the trial, leaving an open question about whether faster resolutions translate into fewer cancellations—a metric the platform is explicitly designed to move. Relay is priced at $65 per agent per month, a structure that puts it within reach of mid-market support teams but could add significant cost at enterprise scale. The platform’s reliance on internal data will force prospective buyers to weigh speed against security. Relay requires access to full support transcripts and company-curated help content. Arcwell said customer data is encrypted and is never used to train shared models. Companies can configure a retention window of either 30 or 90 days, a choice that will matter to legal teams in heavily regulated industries. Arcwell did not frame Relay as a replacement for human agents. Instead, the product is pitched as a real-time co-pilot that collapses the distance between an agent’s question and the institutional knowledge their company already holds. Whether that pitch resonates at scale will depend on the retention numbers the company isn’t yet ready to share.

Meta: Muse Spark Open: # Cloudnote Says Debugging Page Left Customer Prompt Excerpts Accessible for Four Hours *Company says snippets from 1,842 sessions were visible Monday and were removed after researcher alert; full review is still underway.* A debugging page at Cloudnote that displayed excerpts of customer prompts was left accessible without authentication for four hours on Monday, the company said. According to logs reviewed by the company, the page showed snippets from 1,842 user sessions during that window, each containing up to 200 characters of prompt text. Cloudnote reports 3.2 million registered users, meaning the visible sessions represent a fraction of its user base, though the company has not said how many individuals were affected. Cloudnote said it took the page offline 19 minutes after being alerted by an outside researcher and that full documents, passwords and payment information were stored separately and did not appear on the debugging page. The company said it has so far found no evidence that anyone other than the researcher viewed the page, but cautioned that its investigation is not yet complete. Even brief excerpts can contain sensitive personal, business or creative material that users entrust to AI tools, raising questions about handling and oversight. With key questions unresolved — including whether the excerpts were indexed or copied elsewhere — Cloudnote's next findings will determine the incident's reach.

DeepSeek-V4-Pro: # Cloudnote Left Internal Debugging Page Unprotected, Exposing Snippets of 1,842 Customer Prompts *A misconfigured diagnostics tool briefly made prompt excerpts visible to anyone who knew where to look.* Cloudnote confirmed Monday that a passwordless debugging endpoint containing fragments of customer conversations was accessible on the open internet for roughly four hours, potentially revealing portions of 1,842 user sessions. The company said the page was removed 19 minutes after an independent security researcher disclosed the finding, and that it has launched an investigation to map the full exposure. The endpoint displayed an unauthenticated log of recent application events and included prompt excerpts of up to 200 characters. According to a company statement, full notes, attached files, passwords, billing details and any other account-level information were stored in isolated systems and were not present on the affected page. Cloudnote, which counts 3.2 million registered users, has not yet determined whether those text fragments were indexed by search engines or captured by other unauthorized parties. “At this stage we have verified that the researcher accessed the dashboard, but we have found no evidence of additional access,” the company wrote in an advisory published late Monday evening. It cautioned that forensic review of access logs is still underway and that its assessment could change. The incident underscores the cascading risks that accompany the AI note-taking boom, where providers eager to add summarization and chat features increasingly log raw user inputs for product refinement. Privacy researchers have long warned that debugging interfaces—often shielded only by obscurity—can become inadvertent windows into deeply personal user data when internal safeguards are relaxed, even momentarily. Cloudnote did not say whether the affected sessions belonged to free or enterprise-tier accounts, nor whether any healthcare, financial or legal prompts were present in the exposed excerpts. The company plans to notify directly impacted users once the audit concludes.

Meta: Muse Spark Open: 6

DeepSeek-V4-Pro: 4

Meta: Muse Spark Open: C

DeepSeek-V4-Pro: C

Meta: Muse Spark Open: # The Final Run at Foundry Hall Foundry Hall, the nonprofit startup accelerator in Milwaukee, will close on October 31, ending a seven-year run that began in 2018. The organization has supported 212 startups since it opened, companies that now collectively employ 1,140 people. Its exposed-brick workstations and glass-walled conference rooms became a first headquarters for founders who had nowhere else to go. In its early years, the converted warehouse on West St. Paul Avenue offered subsidized desks, mentorship circles, and a shared kitchen where coders worked alongside food entrepreneurs. It did not promise unicorns, only a foothold. For first-time founders without connections, that foothold was enough. That work unraveled after its largest corporate backer pulled out. Blank River Bank, which provided 38 percent of Foundry Hall's annual budget, declined to renew its sponsorship. The bank said the move was part of a shift in its community investment priorities and was not a reflection of the accelerator's performance. Executive director Camille Ross said staff spent months searching for a replacement. > “We kept waiting for the replacement check that never came.” Without it, Foundry Hall has enough cash to keep the lights on through October, but not enough to run another full cohort. The closure will cost six employees their jobs. Another 12 startups that currently rely on the building for workspace will have to leave by the end of October. Staff learned the timeline this week. There will be no fall recruitment, no winter demo day. Instead, the final months will be spent unwinding leases, returning equipment, and helping the last twelve teams find other places to work. For some who passed through, the loss is personal as well as logistical. Dev Malik founded his logistics software company in Foundry Hall's second cohort. He recalled holding his first investor meeting there after months of working elsewhere. > “My first investor meeting happened in that building. Before Foundry Hall, I was pitching from the back table of a coffee shop.” The uncertainty has settled quietly. Founders are backing up servers, forwarding mail, measuring spaces that are smaller or more expensive. No one has described it as a collapse, just an erosion — a steady narrowing of options until closing became inevitable. Others are left looking for what comes next. Deputy Mayor Susan Cole said the city is talking with landlords and local universities about finding interim space for the displaced companies, though no plan has been announced. By October, desks will be cleared, key cards deactivated. What remains is a network of alumni companies scattered across Milwaukee, still hiring, still operating — now without the place that brought them together.

DeepSeek-V4-Pro: The last desk lamp in Foundry Hall will switch off on Halloween, but the final ghost story was told months earlier. It arrived as a form email, not a knock at the door. Blank River Bank, the institution whose name hung on the lobby wall and whose money filled 38 cents of every dollar in the operating budget, was stepping away. The renewal wasn’t coming. Camille Ross, the executive director who had steered the nonprofit startup accelerator since its birth in 2018, watched her small team absorb the news in the sunlit kitchen that had witnessed six years of nervous founders reheating coffee and reworking pitch decks. She described the period that followed as a prolonged state of waiting for a check that was simply never going to arrive. The math, once the hope burned off, was unsparing. Enough cash remained to keep the lights on through October, to wind down gracefully instead of catastrophically. But not enough to seed another cohort of founders, to staff another mentor night, or to risk a program that might stall halfway through. The six employees who kept the machinery of possibility running will lose their positions. The twelve startups currently hunched over laptops in the building’s open floor plan have been told to pack their prototypes by month’s end. The end of Foundry Hall cuts a specific kind of wound in Milwaukee, one measured in square footage a young company can afford and the intangible insulation against despair. The aggregate numbers tell a story of scale—212 startups launched, 1,140 jobs spun into existence—but they miss the smaller choreography of creation. Dev Malik, a founder who came through the program, remembered a time before the building gave him legitimacy. His first real investor meeting happened inside those walls; before that, his office was the back table of a coffee shop, where the Wi-Fi cut out and there was no receptionist to greet a visiting partner with a clean smile. For its part, Blank River Bank deflected any narrative of failure, framing the departure as a pivot, a clinical reallocation of charitable geography. A spokesperson said the decision reflected changing community investment priorities and had nothing to do with the accelerator’s record. In a statement stripped of sentiment, performance was not the issue; attention simply drifted elsewhere. Now the cleanup begins. Deputy Mayor Susan Cole has started working the phones, trying to suture the sudden gap. Conversations are underway with building owners and university innovation centers, a scramble to find temporary roof space for companies that suddenly look homeless. She spoke of the bridge-building efforts in cautious, deliberate terms. The fall air will soon carry the sound of server racks being wheeled onto freight elevators. A building that taught founders how to ask confidently for large sums of money will go quiet, proof that the ask itself never truly ends.

Meta: Muse Spark Open: # Raincheck Raises $8 Million to Help Cities Prioritize Flood Repairs *The Austin startup makes software that maps drainage systems and forecasts where maintenance crews are needed most before storms hit* Raincheck, an Austin-based company that makes flood-risk software for local governments, has raised $8 million in seed funding, the company announced today. The company maps drainage systems and combines those maps with rainfall forecasts to help cities decide where to send maintenance crews before a storm. Additional detail on [how Raincheck builds its forecasts](https://example.com/raincheck-methodology) is available from the company. The seed round was led by Basin Ventures, with participation from existing investor Long Mile. The new investment brings Raincheck's total funding to $10.5 million. > “Our customers usually know which streets flood. The harder problem is deciding which blocked drain, broken pump or unfinished repair should receive attention before the rain arrives,” said CEO Ana Flores. Raincheck currently works with seven cities in Texas and Louisiana. The company declined to identify them because some contracts have not been publicly announced. Its forecasts are recommendations, and emergency managers retain control over deployments and public warnings. The company faces competition from other flood-planning vendors, including HydroMap and municipal engineering contractors. Raincheck said the new funding will help it hire eight engineers and expand into Florida.

DeepSeek-V4-Pro: # Raincheck Raises $8 Million to Help Cities Pinpoint Flood Risks Before the Storm *A seed round led by Basin Ventures will fund the Austin startup’s expansion into Florida and the hiring of new engineers.* Raincheck, an Austin-based company that builds flood-risk software for local governments, announced an $8 million seed round today. Basin Ventures led the round, with participation from existing investor Long Mile. The new funding brings the company’s total raised to $10.5 million. The startup maps drainage systems and combines those maps with rainfall forecasts, giving cities a tool to decide where to send maintenance crews before a storm arrives. “Our customers usually know which streets flood,” said CEO Ana Flores. “The harder problem is deciding which blocked drain, broken pump or unfinished repair should receive attention before the rain arrives.” The company’s forecasts are recommendations; emergency managers retain full control over deployments and public warnings. Raincheck currently works with seven cities in Texas and Louisiana, though the company declined to name them because some contracts have not yet been made public. Other players in the flood-planning space include HydroMap and various municipal engineering contractors. With the new capital, Raincheck plans to hire eight engineers and expand into Florida. A detailed explanation of the company’s approach is available on its [methodology page](https://example.com/raincheck-methodology).

Meta: Muse Spark Open: # HelioDock Rolls Out Remote Software to Oversee Warehouse Robots Overnight *Night Shift lets off-site operators monitor fleets after hours as the robotics startup adds $24 million in new funding* HelioDock on Thursday launched Night Shift, software that allows operators to monitor fleets of its autonomous warehouse robots remotely instead of traveling to a facility to intervene in person. The release introduces a subscription layer on top of the company's robots aimed at reducing overnight call-outs and unplanned downtime. The move extends HelioDock beyond hardware sales into fleet-management software, a segment where operators are increasingly looking to manage large numbers of autonomous systems across multiple sites without adding overnight staffing. HelioDock, which makes autonomous warehouse robots, is positioning Night Shift as a way to keep fleets running when warehouses are lightly staffed or closed. Night Shift was tested for 14 months in a private beta that included nine logistics companies and 310 robots, according to the company. HelioDock says participants in that beta experienced 17% fewer unplanned robot stoppages, a figure that has not been independently verified. The company has not disclosed detailed methodology behind the measurement or how stoppages were defined across different warehouse environments. Chief executive Mara Velez said the product was designed to address a persistent operational burden for managers. “A warehouse manager should not have to drive across town at two in the morning because one robot is confused by a pallet,” Velez said. ## Pricing, Availability and Funding Night Shift will be sold for $90 per robot per month and will become generally available on September 15, the company said. The Thursday launch was paired with the announcement of a $24 million Series B financing round led by North Coast Ventures, bringing HelioDock’s total funding to $39 million. HelioDock enters a fleet-management market where similar products are already offered by Relay Robotics and YardOS, among other providers. Those companies provide software that lets customers oversee and re-task robots working in warehouses and yards, though HelioDock’s offering is specific to its own autonomous warehouse robots. For beta users, performance tracked closely with network conditions. Aaron Pike of Fulcrum Logistics, a beta customer, said the system worked well in facilities with strong connectivity but became less useful during rural LTE outages. In those situations, direct advantages of remote oversight were limited. When connectivity fails, HelioDock said, its robots return to their locally configured behavior and remote control becomes unavailable until a connection is restored. That means operators cannot issue new instructions or redirect robots from afar during an outage, and fleets continue operating only on pre-set local routines. The design reflects a constraint common to cloud-dependent fleet tools, where remote access depends on stable links between the warehouse floor and an off-site dashboard. For sites with intermittent coverage, that dependence determines how much active management Night Shift can actually provide at any given moment. HelioDock said it will continue to offer Night Shift as an add-on to its existing robots, with pricing tied to fleet size rather than number of users monitoring the dashboard.

DeepSeek-V4-Pro: # HelioDock Hops the Fence from Hardware to Remote Ops with Night Shift Launch *The robotics firm bets that warehouse managers want fewer 2 a.m. commutes, even if rural cell towers still have other plans.* HelioDock, a developer of autonomous warehouse robots, is stepping outside the machine with its first major software play. On Thursday the company launched Night Shift, a remote fleet-monitoring platform that lets operators supervise robots without standing on a concrete floor, and simultaneously closed a $24 million Series B funding round. The dual announcement signals that HelioDock is no longer content selling steel and sensors. After 14 months of private beta testing involving nine logistics companies and a combined 310 robots, the company is productizing the layer that sits between a warehouse manager and an unruly pallet. The motivation, according to CEO Mara Velez, is disarmingly simple. “A warehouse manager should not have to drive across town at two in the morning because one robot is confused by a pallet,” Velez said. Night Shift addresses that scenario by giving operators a remote view into their fleets, allowing them to triage problems from a laptop rather than a forklift aisle. Users of the software will pay $90 per robot per month when it becomes generally available on September 15. That pricing model pushes HelioDock toward recurring revenue at a moment when investors are rewarding robotics companies that look less like equipment manufacturers and more like service platforms. The Series B, led by North Coast Ventures, brings the company’s total funding to $39 million. HelioDock claims that beta customers saw a 17 percent reduction in unplanned robot stoppages while running Night Shift. The figure has not been independently verified, and the company did not disclose baseline stoppage rates, making it difficult to assess the real-world impact. Still, Aaron Pike of Fulcrum Logistics, one of the early testers, described the system as effective under the right conditions. In facilities with strong connectivity, Pike said, Night Shift performed reliably. The experience changed when LTE signals dropped in more remote locations. That limitation is baked into the architecture. When connectivity fails, HelioDock’s robots fall back to their locally configured behavior and remote control becomes unavailable. Operators who were mid-diagnosis in a dead zone would need to revert to on-site troubleshooting—the very scenario the software was designed to avoid. The company has not disclosed whether satellite backup or edge-computing mitigations are on the roadmap, leaving the product tethered to the quality of a customer’s cellular infrastructure. The launch places HelioDock in a competitive set that already includes Relay Robotics and YardOS, both of which offer fleet-management products aimed at overlapping buyers. Where Night Shift attempts to differentiate is in its singular focus on remote intervention rather than broader fleet orchestration, but the distinction may blur as incumbents add remote-access features of their own. Velez framed the software not as an operations dashboard but as a labor tool. The pitch implies that a single remote operator could oversee robots spread across multiple sites, compressing what had been a geographically scattered workforce into a single queue of exceptions. Whether the 17 percent stoppage reduction holds up across a broader general-availability install base will determine if the economics of $90 per robot pencil out for operators who have so far run their fleets without a dedicated remote triage layer. Night Shift represents the company’s first step beyond the robot itself. With fresh capital and a tight general-availability date, HelioDock now has to prove that the software layer can keep pace with the hardware that built its name—and that warehouse connectivity catches up before customer patience wears thin.

Meta: Muse Spark Open: # QueueZero Launches Proxy to Turn Meeting Talk Into Action **San Francisco —** QueueZero on Wednesday launched Proxy, an AI meeting assistant that can attend meetings on behalf of users, produce detailed notes and assign follow-up work to software agents. The company unveiled the tool during a live demonstration in San Francisco ahead of a public beta scheduled for next week. Proxy is designed to listen to meeting conversations, summarize key points and translate action items into tasks for integrated software agents. QueueZero said the system is intended to reduce manual note-taking and automate routine follow-ups without requiring direct user prompts for each action. The launch places QueueZero among a growing number of productivity startups offering AI notetakers and meeting agents. Unlike tools that only transcribe, QueueZero said Proxy connects to project management and customer relationship software to execute next steps. The company, founded in 2022, has raised $40 million in venture funding and said it has been testing Proxy with select enterprise customers since January. The capability was illustrated during the demonstration when a customer in the audience remarked, “Someone should tell Evan to stop talking and send us the pricing sheet.” Proxy interpreted the remark as an instruction and automatically generated two tasks assigned to QueueZero chief executive Evan Wu: “Stop talking” and “Send pricing sheet.” Wu laughed, completed the second task by sending the pricing information and deleted the first task from the queue. For an assistant built to make meetings more efficient, it showed an early aptitude for prioritization. QueueZero said Proxy is governed by safeguards that distinguish between internal and external actions. The company said the assistant requires explicit user confirmation before an external message is sent on a user's behalf. Internal tasks, such as creating to-do items or assigning work to agents, can be created automatically based on the conversation. Administrators can disable automatic task creation entirely or configure approval workflows for specific types of actions, the company said. The controls are intended to prevent unintended assignments while preserving automation for teams that want it. Proxy will be available for $30 per user per month. QueueZero said it will enter public beta next week, with general availability expected later this year following feedback from early testers.

DeepSeek-V4-Pro: # QueueZero’s New AI Meeting Assistant Attempts to Muzzle Its Own CEO During Live Demo **SAN FRANCISCO** — QueueZero launched its AI meeting assistant, Proxy, on Wednesday with a carefully choreographed demonstration that was politely upended when the software interpreted a heckler’s remark as a binding executive order. Proxy is designed to attend meetings on behalf of human users, generate structured notes, and dispatch follow-up work to a growing ecosystem of software agents. The pitch is straightforward: reduce the time knowledge workers spend in synchronous meetings by sending a machine-learning proxy that can listen, transcribe, and act. The unscripted moment arrived midway through the presentation. As CEO Evan Wu walked the audience through Proxy’s natural-language processing capabilities, a customer in the second row spoke to a colleague just loudly enough to be heard throughout the room. “Someone should tell Evan to stop talking and send us the pricing sheet,” the attendee said. Proxy’s onboard microphone array captured the remark. Following its design specification to generate actionable items from meeting dialogue, the assistant created two internal tasks and assigned both to Wu: “Stop talking” and “Send pricing sheet.” The tasks appeared on the projection screen behind him, visible to approximately 200 attendees. Wu paused, examined the display, and laughed. He marked the first task as complete with a theatrical gesture and deleted it. He then genuinely completed the second task by advancing the presentation to a slide containing QueueZero’s pricing structure. The room applauded. Whether the applause was for the pricing or Proxy’s candor remained ambiguous. QueueZero’s engineering team later clarified that the incident illustrated a deliberate architectural choice rather than a failure mode. The product ships with a mandatory confirmation gate that requires human approval before any external communication — an email, a calendar invitation, or a Slack message — is dispatched beyond the organization. Internal task creation, by contrast, operates without a confirmation step by default, a design decision the company says reflects the low cost of correcting an errant to-do item versus the reputational damage of an errant client email. Administrators can disable automatic internal task creation entirely for organizations that prefer an additional layer of control. The broader technical context is a market increasingly crowded with AI agents that promise autonomous action. QueueZero’s bet is that a measured approach — one that allows internal execution while sandboxing external communication — offers a defensible middle ground between fully autonomous systems and passive transcription tools. Analysts note that enterprise adoption of agentic AI has been slowed by governance concerns, and QueueZero’s consent architecture may address procurement objections that have stalled competing products. Proxy pricing was confirmed at $30 per user per month. The platform enters public beta next week, with general availability planned for the fourth quarter. Integration support at launch covers Zoom, Microsoft Teams, and Google Meet. QueueZero declined to comment on whether the “Stop talking” task would be added to Wu’s quarterly performance review.

Meta: Muse Spark Open: # The Best Support AI Wasn't the Most Expensive One. It Was the One That Tied It. For customer-support teams looking to add AI, the instinct is simple: pay more, get better answers. A new benchmark of five language models on real support tickets suggests that logic is already outdated. In a blind test on 200 anonymized tickets from a software company, the highest-priced model didn't pull away from the pack. It tied. And the model it tied with costs less than half as much and answers faster — a finding that reframes the conversation from raw quality to value, deployment fit, and where human help is still needed. ### How the test was actually run That conclusion only matters if the methodology holds up, and here it's straightforward but limited. Researchers had each of the five models — called North, Vale, Ember, Kite, and Harbor in the study — write a single proposed answer for every ticket. Three experienced support managers then graded those answers without knowing which model wrote which. The grades were not just a vibe check on tone; the composite score combined three things support leaders care about: factual correctness, usefulness in resolving the issue, and tone. The lineup covered a realistic spread of options, from a premium-priced model to budget alternatives. Estimated cost was calculated per 1,000 tickets, ranging from $1.90 at the top end to just $0.08 at the bottom. That range is the point: this wasn't a test of five similar models, but of distinct cost tiers. ### The headline is a tie at the top On quality alone, two models separated themselves from the rest. North averaged 84.2 and Vale averaged 83.8. That 0.4-point gap is noise. The researchers found no statistically significant difference between them, meaning buyers should not treat North as the "winner" on quality. For practical purposes, they performed identically. Where they did differ was on cost and speed. North's estimated cost was $1.90 per 1,000 tickets, while Vale came in at $0.74 — roughly 61% cheaper. Vale was also 18% faster in generating answers. In a live support operation, those two factors compound quickly. At 50,000 tickets a month, choosing the equally good but cheaper and faster model would cut direct inference costs by more than half while reducing customer wait time. That's the clearest finding of the study: top-tier answer quality is now available without paying a top-tier premium. This is also why the rest of the field matters as context, not as a leaderboard to obsess over. The other three models formed a clear lower tier on quality, averaging in the 70s. Ember was notably inexpensive at $0.31 per 1,000 tickets, but its average score dropped to 79.1. Kite and Harbor were even cheaper — $0.22 and $0.08 respectively — and their scores fell further to the mid-70s and low 70s. That creates two different purchasing stories. If you need the best answers this test could produce, you have two essentially equal options, one far more efficient. If you are willing to accept a meaningful drop in correctness and usefulness to save money, much cheaper models exist, but they are not close substitutes. The study also hints that "best" depends on ticket type. North looked strongest on billing disputes, while Vale led on technical troubleshooting. For companies whose queue skews heavily one way, that specialization could be more important than the overall average. ### Where every model still fails The benchmark's most useful warning has nothing to do with which model scored highest. When a ticket depended on an image attachment — a screenshot of an error, a photo of an invoice, a annotated screen capture — all five models performed poorly. Answer quality collapsed across price points. For teams where 20% or 30% of tickets include images, that limitation alone will determine whether AI can be deployed safely without a human in the loop. The study's scope is narrow in other ways readers should keep in mind before generalizing. All 200 tickets were in English and came from a single software company. The test measured one-shot answers, not agentic workflows where a model might look up an account, issue a refund, or escalate. Tool use and multilingual support were not tested at all. That doesn't invalidate the results; it defines where they apply: English-language, text-only triage and drafting for a B2B software support context. ### What to do with a tie The takeaway for support leaders is not that one model "won." It's that the price-performance curve has bent. If you were budgeting for the most expensive option because you assumed it was necessary for quality, this data says to pilot the efficient peer instead — especially if your tickets skew technical and speed matters. Put the savings into the place every model struggled: building a reliable handoff for image-based tickets, or keeping a manager in the loop for billing disputes where factual errors carry financial risk. In support, the most expensive answer is rarely the best strategy. This benchmark suggests it isn't even the best answer.

DeepSeek-V4-Pro: Here is a 600–750-word Markdown article built around the benchmark findings. --- # The hidden champion of enterprise AI: Why the best model rarely wins In the race to deploy generative AI inside the enterprise, it is tempting to treat model selection as a simple leaderboard exercise. Pick the model with the highest benchmark score and move on. But when a team of researchers ran a controlled test on real customer-support tickets, the numbers told a messier, more interesting story. The clearest finding was not that one model is objectively “the best,” but that the difference between the top performers is now so small that procurement decisions should flip from chasing marginal quality gains to optimizing for speed and cost. The experiment was designed to mirror a genuine support workflow. Researchers drew 200 anonymized English-language tickets from a single software company. Five language models — given the pseudonyms North, Vale, Ember, Kite and Harbor — each generated one proposed answer per ticket. Three experienced support managers then graded every answer blindly, combining factual correctness, usefulness and tone into a single score. The researchers also tracked response latency and estimated the per-ticket cost based on API pricing at the time of testing. **The top-tier tie** Model North led the field with an average score of 84.2. Model Vale came second at 83.8. That 0.4-point gap is so small that the researchers flagged it as statistically insignificant. In practice, a customer reading a North answer and a Vale answer side by side would be hard-pressed to say which one came from the higher-scoring model. Both were rated as polite, accurate and helpful far more often than not. But while quality was neck-and-neck, the operational differences were dramatic. Vale generated its responses 18 % faster than North, and its estimated cost came in at $0.74 per 1,000 tickets — a 61 % discount compared with North’s $1.90. Multiply that across the millions of tickets a large software company handles each year, and the choice between the two top models starts to look less like a scientific judgement and more like a business no-brainer. This pattern cascaded down the table. Model Ember (79.1), Kite (76.4) and Harbor (71.0) each posted progressively lower scores, but their costs fell even faster. Harbor, the cheapest at $0.08 per 1,000 tickets, still delivered answers that were rated above 70 on average — a level that several managers described as “acceptable for internal drafts.” Whether an organization chooses Ember’s balanced profile or Harbor’s ultra-low cost depends entirely on whether the use case requires a customer-facing reply or a rough suggestion an agent will polish anyway. **Where domain strength upsets the podium** Aggregate scores flatten nuance, and the test surfaced one that matters a great deal: the best model depends on what you are asking. North held a clear edge on billing disputes — emotionally charged tickets where tone and precision are paramount. Vale, by contrast, performed best on technical troubleshooting, where step-by-step logic and code-block formatting carry the day. A support operation that routes all billing tickets to North and all technical tickets to Vale could, in theory, beat any single model’s overall score. This points toward a future where model selection is dynamic and task-specific rather than a one-time procurement decision. **The image-shaped hole** One finding was both stark and uniform. All five models performed poorly when a ticket depended on an image attachment. Scores dipped so far that the researchers described the answers as “often unusable.” This is not surprising given the text-only nature of the models tested, but it is a crucial reminder that many real support tickets do not arrive as neat paragraphs. Screenshots of error messages, photos of damaged hardware and scanned invoices are standard artifacts in enterprise queues. Until multimodal processing is tested with the same rigor, any benchmark that relies solely on text will overstate field performance. **What the test did not measure — and why it matters** The methodology was deliberately narrow to guarantee clean comparisons. That narrowness is also the study’s chief limitation. The researchers did not test agentic actions — the ability to actually issue a refund, reset a password or update a shipping address. Great answer text that fails to resolve the customer’s problem creates a repeated contact, which is often more expensive than a slightly suboptimal answer that closes the ticket. Tool use, retrieval-augmented generation over a live knowledge base and multilingual support were also outside the scope. A model that shines in an English-only, text-only sandbox might struggle when it has to decide whether to call a billing API or enquire about a tracking number in German. Readers should therefore treat these results as a snapshot of language fluency and instruction-following, not a complete measure of support capability. **The story the data is telling** If you strip away the decimal points, the message is clear. The performance gap at the top of the market has collapsed. For the first wave of enterprise adoption, the sensible play was to pay a premium for the highest-scoring model because the spread between first and fourth place was wide enough to matter. That spread is now nearly gone among the leaders. In its place, speed and cost-efficiency have become the real differentiators. Model Vale’s combination of second-place quality, fastest latency and a 61 % cost saving makes it the rational default for any high-volume, customer-facing support use case — at least until the next round of models reshuffles the board.

Meta: Muse Spark Open: # Startup Spotlight: LedgerLark Post-trade operations rarely make headlines, but they determine whether a trade settles correctly. For small investment firms without large operations teams, that work can be slow and error-prone. LedgerLark is betting that AI agents can help, without taking control of money or trades. ## From operations desk to product The idea traces back to co-founder Mina Batra, who spent seven years in operations at a regional brokerage. According to the company, Batra began exploring the concept after watching a six-person team spend three days resolving a trade mismatch caused by a copied account number. She teamed with co-founder Owen Carr, who previously built reconciliation systems at a large bank. Their experience is in the back-office plumbing of finance: confirming details, matching records, and clearing exceptions when things don't line up. Their insight is that smaller firms face the same post-trade complexity as larger institutions, but with fewer tools and people. Many rely on manual comparisons across systems not designed to communicate. LedgerLark is aiming at that gap. ## What the product does — and doesn't do LedgerLark's agents compare confirmations, custodian records, and internal ledgers. When records disagree, the agents propose corrections for a human to review and approve. That human-in-the-loop design is central to how the company positions the product. The agents do not execute trades or move money. In a regulated environment where an automated error could be costly, the limitation is intentional: the system suggests, a person decides. It is a narrow wedge, which raises practical questions. How well do the agents handle the variety of formats — from structured files to scanned confirmations — common at small firms? The company will need to show its comparisons reliably reduce work rather than shift it. ## Early customers and early signals LedgerLark says it has five paying customers, all small investment firms, with annual contracts ranging from $36,000 to $110,000. That range suggests flexible, firm-by-firm pricing typical at the seed stage, though it leaves open how pricing scales with volume or users. For traction, the company points to one customer example: a reported drop in unresolved daily exceptions from 70 to 18. The figure is promising but limited — a single, company-reported case without a disclosed time frame or independent verification. It doesn't yet show whether similar results hold across different custodians and workflows. The company has raised a $3.5 million seed round led by Motive Row, funds that could support product development and the long sales cycles typical in finance. ## What remains uncertain Competition is real. LedgerLark is entering a market with established reconciliation vendors and internal software built by banks and custodians over many years. Incumbents may be slower to adopt newer techniques, but they benefit from existing integrations and procurement relationships. Procurement itself may be the steeper challenge. Even small financial institutions often require lengthy security and procurement reviews before any tool touches operational data. For a startup with five customers today, those reviews can stretch sales timelines and limit live testing. Demonstrating security and auditability will matter as much as accuracy. There are also broader questions about agents in operations. Will firms be comfortable letting software propose ledger corrections, even with human approval? And who owns a mistake that slips through? LedgerLark does not claim to replace operations teams, and that modesty helps. If its agents can consistently shrink daily exception queues without creating new risks, small firms could reclaim hours lost to manual reconciliation. Whether that proves repeatable — and defensible against entrenched vendors — is what the next year of deployment will need to clarify.

DeepSeek-V4-Pro: ```markdown # Startup Spotlight: LedgerLark wants to take a gnawing, $100-billion problem off the desks of small investment firms Most of the financial world’s attention is fixed on the moment of transaction—the bid, the execution, the price. But in the back offices of boutique investment firms, the real anxiety often begins the moment the trade is done. That’s the world of post-trade operations, where a single transposed digit can unspool into three days of forensic drudgery. Mina Batra knows this territory intimately. Before co-founding LedgerLark, she spent seven years in operations at a regional brokerage, a tenure that served as an extended field study in what she calls “quiet chaos.” The seed of the company was planted one Tuesday when a junior analyst on her team noticed a $2.4 million cash shortfall in a client’s model portfolio. The culprit wasn’t a rogue algorithm or a volatile market; it was a copied account number. A six-person team spent three full business days hunting down the mismatch across custodian statements, internal ledgers, and trade confirmations. “The trade itself took microseconds,” Batra recalled. “The fix took nearly 4,000 person-minutes. I remember thinking the ratio between execution and assurance has gotten completely absurd.” That gap is what she and co-founder Owen Carr are trying to close. Carr built reconciliation systems at a large bank, so he understands the sophistication available to institutions with nine-figure technology budgets. What Batra and Carr saw was a missing middle: small and mid-sized investment firms that can’t afford a bespoke, server-hugging reconciliation stack but are too complex for a spreadsheet to hold together. LedgerLark’s answer is a set of AI agents focused narrowly on post-trade reconciliation. Their software compares trade confirmations, custodian records, and internal ledgers, flags breaks, and proposes journal corrections—all without executing a single trade or moving a dollar. A human still has to hit approve. The product’s earliest traction doesn’t come with the theatrical round numbers typical of pre-revenue startups, which is what makes it worth examining. Five firms are paying customers, with annual contracts running between $36,000 and $110,000 depending on account volume and complexity. The company says one of these customers has already seen its count of unresolved daily exceptions drop from 70 to 18. Whether that improvement is durable and whether it holds during periods of extreme volatility are open questions the company is actively testing, not proclaiming. Still, the early signal seems legible enough to investors who underwrite unglamorous infrastructure. LedgerLark recently closed a $3.5 million seed round led by Motive Row. In a funding environment where back-office AI can blend into a blur of undifferentiated platforms, the raise suggests a bet on specificity: a tool for a defined user in a defined workflow, rather than a horizontal promise. The uncertainty ahead isn’t theoretical. The company is stepping into a landscape already occupied by established reconciliation vendors that have deep support relationships and by internal tools at larger institutions that may eventually trickle downmarket. Then there is the pace of procurement itself. Financial institutions, even small ones, can subject new software to security reviews that stretch past nine months. A startup with five customers and a fresh seed round will need a runway and a resolve that outlasts a compliance checklist. Batra acknowledges this without performing false confidence. “If someone tells you they’ve solved the bank procurement timeline,” she said, “you should check whether they’ve ever actually sold into a bank.” LedgerLark’s path will depend on whether it can turn a few relieved operations teams into a category of its own, rather than a feature waiting to be absorbed. For now, the company is carving its niche in a place most people never see: the silent hours after a trade, when a mismatch sits unnoticed and a deadline approaches. Where Batra once found the absurd, she now seems to see leverage. ```

Meta: Muse Spark Open: # Raincheck raises $8 million for flood-risk software *The Austin startup helps cities decide which drainage problems to address before storms.* Raincheck has raised an $8 million seed round led by Basin Ventures. ## Expansion plans The company plans to hire eight engineers.

DeepSeek-V4-Pro: # Raincheck raises $8 million for flood-risk software *Raincheck helps cities decide which drainage problems to address before storms.* Raincheck has raised an $8 million seed round led by Basin Ventures. ## Expansion plans The company plans to hire eight engineers.

Meta: Muse Spark Open: # KiteBridge Software Failure Sends $2.8 Million in Duplicate Payments to Employees **91 employers were charged twice for reimbursements after an update undermined the expense-management firm's own payment safeguards.** A routine software update from expense-management firm KiteBridge resulted in 13,400 duplicate reimbursements totaling $2.8 million on Tuesday, leaving dozens of corporate customers to fund double payments to their employees and scrambling to claw back the money. The company said employers, not employees, bore the cost of the error. No money was withdrawn from employees who received the extra payments, but by Wednesday evening only $2.3 million had been recovered, with the remainder still pending return. KiteBridge, whose platform automates expense reporting and reimbursements for businesses, deployed version 4.2 of its software on Tuesday morning. Shortly after the release went live, customers began reporting irregularities. Within about an hour, the company suspended all automated payments after confirming its system was creating duplicate transactions. The failure stemmed from how KiteBridge handled interruptions with its external payment processor, the third-party service that actually moves money between accounts. When the processor timed out and did not immediately confirm a transaction, KiteBridge's fallback system — designed to retry failed payments — mistakenly treated each retry as a brand new reimbursement request. For a general audience, that distinction matters: instead of checking whether the money had already been sent and simply trying again to complete the same payment, the system created an entirely separate, second payment for the same expense report. That behavior directly contradicts a protection KiteBridge had previously advertised. According to its April changelog for version 4.0, the company introduced "exactly-once payment protection," a common safeguard in financial software intended to guarantee that even if a network request is retried multiple times, the payment itself is only executed once. In Tuesday's incident, that safeguard failed. The result was a wave of duplicate payouts spread across 91 corporate customers. KiteBridge's initial public communication drew scrutiny for understating the issue. Its incident status page first described the problem as delayed reimbursements, language that suggested employees were waiting for money rather than receiving too much. The page was not updated to disclose that duplicate payments had been issued until about six hours after the company had already halted automated payments. Chief Executive Lena Ortiz later apologized for the incident and said KiteBridge would publish a full technical report detailing the root cause and the steps it will take to prevent a recurrence. She did not provide a timeline for the report. The company has not said how it is recovering the outstanding funds or whether affected employers will be compensated for administrative costs, though it confirmed the recovery effort was ongoing through Wednesday. For employers that rely on automated finance tools to handle thousands of routine transactions, the incident underscores the risk of silent failures in retry logic — the unseen code that decides what to do when a payment system does not answer right away. When that logic is flawed, a temporary timeout can quickly become a large-scale financial error before the first customer complaint even arrives.

DeepSeek-V4-Pro: # Expense Startup’s ‘Exactly-Once’ Guarantee Fails, Unleashing $2.8 Million in Duplicate Payments KiteBridge’s promise that every reimbursement would be processed exactly once crumbled in a matter of minutes Tuesday morning, forcing the expense-management startup to admit that 13,400 employees received double payments totaling $2.8 million after a routine software deployment went awry. The failure—which struck 91 corporate customers—gutted a safeguard the company had publicly touted just months earlier and left its engineering team scrambling to recover millions of dollars that its own systems had mistakenly dispensed. The cascade began at 8:45 a.m., when KiteBridge pushed version 4.2 of its platform to production. The update was described internally as a minor refinement to the company’s transaction pipeline, and engineers expected no disruption to normal operations. For 27 minutes, nothing appeared unusual. Then, at 9:12 a.m., the first customer email landed in the support queue: an employee at a mid-market logistics firm noticed two identical reimbursement deposits in her bank account and wondered whether the duplicate was an accounting error on her employer’s side. It was not. Behind the scenes, KiteBridge’s payment processor had begun timing out on a portion of its outbound requests. The processor was technically accepting the reimbursements and settling them with the banking network, but its confirmation responses were arriving too slowly for KiteBridge’s software to register them. That detail proved catastrophic under the rules the platform had been given. When a transaction appeared to go unanswered, the system’s fallback logic treated the silent outcome as a failure and dutifully resubmitted the request—not as a retry of the original, but as a fresh reimbursement entirely. Every timeout therefore spawned a second payment that the ledger believed was a separate, legitimate transaction. The design flaw sat in direct contradiction to a changelog entry from April, when version 4.0 shipped with a feature KiteBridge called “exactly-once payment protection.” That mechanism was supposed to attach a unique idempotency key to each reimbursement instruction, ensuring that repeated submissions of the same request would be recognized and discarded by the payment processor’s own duplicate-detection layer. But version 4.2 had subtly altered the way those keys were generated under certain retry conditions, effectively stripping them when the fallback path took over. The timeout problem, which on its own would have caused only brief delays, became a force multiplier. By the time KiteBridge’s on-call team identified the scope of the damage, the platform had minted 13,400 duplicate reimbursements. Employers—not their workers—funded the excess payments, meaning no individual woke up to find money clawed back from a personal account. Still, the aggregate exposure was $2.8 million spread across dozens of corporate treasury accounts, with some customers seeing six-figure sums leave their coffers unexpectedly. At 10:03 a.m., the company suspended all automated payments, freezing the pipeline while engineers worked to silence the faulty retry loop. That stoppage stopped new duplicates but left the original mess untouched, and KiteBridge’s public communications trailed the reality for hours. Its status page initially characterized the incident as “delayed reimbursements,” a description that remained live while finance teams at affected companies were already documenting the twin deposits they could see in their bank portals. The page was not updated to acknowledge the duplicate payments until six hours after the payment suspension, a lag that infuriated comptrollers trying to reconcile their cash positions in real time. CEO Lena Ortiz issued a written apology late Tuesday, confirming that no employee funds had been withdrawn. She said KiteBridge would publish a full technical postmortem and had begun working directly with the payment processor to harden the confirmation layer. Recovery efforts advanced quickly: by Wednesday evening, $2.3 million of the duplicate funds had been reversed or returned, though roughly $500,000 remained in transit or tied up with banks that require multi-day recall procedures. Ortiz pledged that any residual shortfalls would be covered by KiteBridge while the engineering team re-architected the retry module to guarantee that idempotency keys can never again be shed during a fallback. For the 91 companies involved, the episode served as an expensive reminder that “exactly once” is a warranty that demands relentless testing—not just a confident line in a changelog.