Head to head: DeepSeek-V4.1-Flash vs muse-spark-1.3

DeepSeek-V4.1-Flash: C

muse-spark-1.3: C

DeepSeek-V4.1-Flash: # 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.

muse-spark-1.3: # 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-V4.1-Flash: # The Last Cohort at Foundry Hall Foundry Hall, a nonprofit startup accelerator in Milwaukee, will close on October 31. It opened in 2018 and grew into a modest but vital address for founders who needed a desk, a mentor, and a reason to keep going. Since then, it has supported 212 startups. Those companies collectively employ 1,140 people—numbers that sound institutional until you picture the payrolls, rent checks, and first hires behind them. The end came from a funding gap, not a sudden collapse. Blank River Bank, the accelerator’s largest corporate sponsor, supplied 38 percent of its annual budget. The bank declined to renew its sponsorship. Foundry Hall has enough money to operate through October, but not enough to run another full program. Six employees will lose their jobs. Twelve startups currently using its workspace must leave. Executive director Camille Ross described the months of uncertainty with a plainness that made it worse. “We kept waiting for the replacement check that never came,” she said. The waiting shaped staffing, programming, and every conversation about the future. By the time the calendar turned toward fall, the math was no longer a warning. It was a deadline. For founders, the loss is personal as well as practical. Dev Malik, a founder in the accelerator’s network, remembered where the work began. “My first investor meeting happened in that building,” he said. “Before Foundry Hall, I was pitching from the back table of a coffee shop.” The quote could belong to many alumni. Foundry Hall’s value was not only capital. It was proximity: to advisers, to other founders, to the possibility that a half-formed idea might become a company. Blank River Bank’s spokesperson said the decision reflected a change in its community investment priorities and was unrelated to Foundry Hall’s performance. The distinction may be technically true, but it does little for the twelve companies now looking for new space. Deputy mayor Susan Cole said the city is speaking with building owners and local universities about space for the current companies. Those conversations are welcome. They are also a scramble, not a replacement. The closure will not erase what was built. The 212 startups remain part of Milwaukee’s economy, and the 1,140 jobs they support remain on someone’s ledger. But an ecosystem is not just a list of companies. It is the room where they meet, the desk they borrow, the person who answers a question before it becomes a crisis. On October 31, Foundry Hall will go quiet. The keys will be returned. The last cohort will pack up. What happens next will depend on whether the city can keep even a fraction of that room open—not as nostalgia, but as infrastructure.

muse-spark-1.3: # The Light Goes Out at Foundry Hall **MILWAUKEE** — For six years, the third floor of the old brass foundry on Canal Street smelled of coffee and fresh paint, and hummed late into the evening. That hum will stop Oct. 31, when Foundry Hall, the nonprofit startup accelerator that opened in 2018, closes its doors for good. Executive director Camille Ross said the decision came down to math and timing. The accelerator’s largest corporate sponsor, Blank River Bank, which provided 38% of its annual budget, declined to renew. Without that support, Foundry Hall can keep the lights on through October, but cannot fund another full program cycle. “We kept waiting for the replacement check that never came,” Ross said. Since its founding, Foundry Hall has supported 212 startups, companies that now collectively employ 1,140 people across the region. Its workshops on pricing, hiring, and pitching became a rite of passage for first-time Milwaukee founders, many of whom arrived without connections to capital. Founder Dev Malik was one of them. He recalled holding his first investor meeting inside the building after months of pitching from the back table of a coffee shop. He credits the accelerator with giving him not just desk space, but credibility. The closure will displace six staff members, who will lose their jobs, and twelve startups currently working out of the building, who must find new space by the end of next month. A Blank River Bank spokesperson said the move reflected a shift in its community investment priorities, not dissatisfaction with the accelerator’s work. Deputy Mayor Susan Cole said the city was speaking with building owners and local universities about space for the displaced companies. On Tuesday afternoon, sunlight fell across empty desks where founders once traded advice over lukewarm pizza. No one was pitching, but no one seemed eager to leave either. The coffee machine, famously temperamental, was still on. For now, though, the calendar at Foundry Hall is thinning. Mentorship sessions have ended. The whiteboards have been wiped clean, save for a few phone numbers and half-erased diagrams. A final demo night is planned for mid-October, less a showcase than a goodbye. Ross has spent the past week calling alumni, landlords, and donors. She does not describe the closure as a failure, but as a gap that will be felt quietly — in leases not signed, in introductions not made, in the particular confidence that comes from having somewhere to go to work on an uncertain idea. After October, the foundry building will go dark. What it built, Ross hopes, will not.

DeepSeek-V4.1-Flash: # HelioDock Launches Remote Robot Monitoring Software, Raises $24 Million *Night Shift, which lets warehouse operators supervise robot fleets from anywhere, emerges from a 14-month beta as the company closes a Series B led by North Coast Ventures.* HelioDock released Night Shift on Thursday, software that allows warehouse operators to monitor autonomous robot fleets remotely, ending a private beta that ran for 14 months across nine logistics companies and 310 robots. The launch arrived alongside a $24 million Series B led by North Coast Ventures, which brings HelioDock's total funding to $39 million. Night Shift is aimed at a specific and persistent problem in warehouse automation: robots that stop working at inconvenient hours and require a human to intervene in person. The software routes alerts and status information to operators wherever they are, an arrangement HelioDock says reduces the frequency of unplanned stoppages. Beta customers recorded 17% fewer such stoppages, according to the company. The figure has not been independently verified. Chief executive Mara Velez framed the product around the cost of physical presence. "A warehouse manager should not have to drive across town at two in the morning because one robot is confused by a pallet," she said. That scenario has shaped warehouse robotics for years. As fleets grow, so does the volume of routine interruptions that demand attention, and much of the existing tooling assumes an operator is already on site. Night Shift is HelioDock's attempt to uncouple supervision from proximity. The beta period, however, surfaced a boundary the software cannot cross. Aaron Pike, a beta customer at Fulcrum Logistics, said the system performed well in facilities with strong connectivity but became less useful during rural LTE outages. When connectivity fails, robots fall back to locally configured behavior and remote control becomes unavailable. For operators in areas with unreliable coverage, that limitation defines the practical reach of remote monitoring: the software is only as available as the network carrying it. HelioDock is not alone in the category. Relay Robotics and YardOS both offer competing fleet-management products, and the field has grown more crowded as logistics operators look to extract more capacity from equipment they have already deployed. Night Shift's pricing — $90 per robot per month — positions it as an operating expense that scales with fleet size, a structure that may suit larger sites but invites scrutiny at smaller ones. General availability begins September 15. The Series B funding gives HelioDock room to expand sales and engineering around the launch, though the company has not detailed how it will allocate the capital. For beta participants, the value proposition rests on a straightforward trade: fewer trips to the warehouse floor, in exchange for dependence on a network connection that is not always there. How broadly that trade appeals — and how quickly HelioDock can address the connectivity gap its own customers identified — will determine whether Night Shift becomes standard equipment for remote fleet management or a useful tool with a clearly marked ceiling.

muse-spark-1.3: # HelioDock Launches Night Shift for Remote Supervision of Warehouse Robots *Fourteen-month beta with nine operators precedes September release as company adds $24 million in new funding* HelioDock on Thursday launched Night Shift, software that lets operators monitor fleets of its autonomous warehouse robots remotely, a move aimed at reducing the need for on-site intervention when machines encounter problems during overnight hours. The launch positions remote supervision as a central part of HelioDock's warehouse offering. The company, which makes autonomous warehouse robots, is asking managers to watch and intervene in fleet operations from elsewhere rather than keep staff on the floor or respond in person to every stoppage. The software will become generally available September 15 at $90 per robot per month. Chief executive Mara Velez framed the problem in terms of after-hours calls. “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. Unplanned stoppages of that kind — a robot pausing when it cannot interpret its surroundings — require someone to assess what happened and get the machine moving again. Night Shift is intended to allow that assessment to happen remotely. The company tested Night Shift for 14 months in a private beta involving nine logistics companies and 310 robots. That test period covered sustained warehouse use rather than a short trial, giving the participating operators time to incorporate remote monitoring into regular shifts and to observe how the system performed across different facilities and operating conditions. HelioDock says beta customers experienced 17% fewer unplanned robot stoppages while using Night Shift. The company has not provided independent verification of that figure, and it did not detail how the reduction was measured across the nine participants or whether results varied by site. The claim points to fewer interruptions requiring hands-on attention, rather than elimination of stoppages altogether. Beta customer Aaron Pike of Fulcrum Logistics described performance that depended on network conditions. Pike said the system worked well in facilities with strong connectivity but became less useful during rural LTE outages. In those periods, the ability to check in on robots from a distance diminished, leaving local staff with more of the response burden that remote monitoring is designed to ease. HelioDock said that when connectivity fails, robots return to their locally configured behavior and remote control becomes unavailable. The fallback means machines follow locally configured behavior, but operators lose the ability to monitor fleets or step in from elsewhere until the connection is restored. Night Shift will cost $90 per robot per month. For operators running large fleets, that charge scales directly with fleet size, making the monthly total a function of how many machines are enrolled. HelioDock plans general availability September 15, moving the product beyond the beta group to any customer willing to add the subscription to its robots. The software debut coincided with a financing announcement. HelioDock announced a $24 million Series B led by North Coast Ventures, bringing its total funding to $39 million, according to the company. The round provides capital as HelioDock prepares to sell and support Night Shift more broadly after more than a year of limited testing. HelioDock is entering a segment where fleet-management options already exist. Relay Robotics and YardOS make competing fleet-management products, giving warehouse operators a choice of suppliers for tools that oversee robot activity. How buyers weigh monthly per-robot pricing, measured stoppage reductions and behavior during outages is likely to shape adoption once Night Shift is widely available.

DeepSeek-V4.1-Flash: 7

muse-spark-1.3: 6

DeepSeek-V4.1-Flash: # Cloudnote Says Debug Page Exposed Excerpts of 1,842 User Prompts for Four Hours *The note-taking startup says a misconfigured internal tool was reachable without a login, but that full documents, passwords and payment data were never on the page.* Cloudnote disclosed that an internal debugging page containing excerpts of customer prompts was accessible without authentication for roughly four hours on Monday, exposing fragments from 1,842 user sessions before the page was taken down. According to the company, the page could display up to 200 characters from a given prompt—enough to reveal a sentence or two, but not necessarily a complete request. Cloudnote has 3.2 million registered users, meaning the affected sessions represent a small fraction of its customer base. The page was removed 19 minutes after a security researcher notified the company. Cloudnote said it has found no evidence that anyone other than that researcher accessed the page, though it cautioned that its investigation is not complete. The company also said full documents, passwords and payment information were stored on separate systems and never appeared on the debug page. What remains unclear is how long the page had been reachable before Monday and whether the configuration error affected other internal tools. Cloudnote declined to answer those questions, saying it would share more when its review concludes.

muse-spark-1.3: # Cloudnote Left Prompt Excerpts Exposed for Four Hours, Affecting 1,842 User Sessions *The company shut down an unauthenticated debugging page minutes after a researcher's alert and says it has seen no sign of wider access, but its investigation is still incomplete.* Cloudnote left a debugging page containing excerpts from customer prompts accessible on the public internet without authentication for four hours on Monday. The company said it removed the page 19 minutes after an outside researcher reported the problem. Company logs reviewed after the incident show the page displayed snippets from 1,842 user sessions, each up to 200 characters long, a small fraction of its 3.2 million registered users. Cloudnote said full documents, passwords and payment information are stored separately and did not appear on the exposed page. The incident matters because prompts entered into productivity and AI-assisted note tools often contain sensitive personal, business or creative information, and even a brief excerpt can be revealing depending on what a user typed. Security specialists say temporary debugging interfaces are a frequent source of accidental exposure when left connected to live systems. What is not yet known is whether anyone other than the researcher who flagged the issue saw or saved the material during the four-hour window. Cloudnote said it has found no evidence of additional access to date, but cautioned that its investigation is still underway and that conclusion could change as more log data is reviewed.

DeepSeek-V4.1-Flash: # Startup Spotlight: LedgerLark ## The pitch Post-trade operations rarely make headlines, which is partly why the mistakes there can linger. LedgerLark, a small startup, is building AI agents that help investment firms investigate and resolve trade breaks — the mismatches that occur when two sides of a transaction don't agree on the details. The company's agents compare trade confirmations, custodian records and internal ledgers, then propose corrections for a human to approve. LedgerLark is explicit that its software does not execute trades or move money. It sits in the review layer, aiming to shorten the path from "something doesn't match" to "here's what likely happened." ## The origin Co-founder Mina Batra spent seven years in operations at a regional brokerage, where she saw the same pattern repeat. In one case that stuck with her, a six-person team spent three days resolving a single trade mismatch — a copied account number that rippled through several systems and generated a trail of exceptions along the way. The error itself was mundane. The time spent tracing it was not. Her co-founder, Owen Carr, comes from the other direction: he built reconciliation systems at a large bank, where the tooling was more sophisticated but also more bespoke. The two concluded that smaller firms — those with real volumes but without dedicated engineering teams — were caught in between, relying on spreadsheets, email threads and institutional memory. ## How it works LedgerLark's agents ingest records from multiple sources, match them, and flag discrepancies with a proposed resolution. The framing matters: the product is designed as a recommendation engine, not an autonomous operator. Humans remain in the loop, and the company has been careful to describe it that way, which may help in a sector where "AI agent" can sound like a liability rather than a feature. ## Traction Five firms are paying customers, according to the company, with annual contracts ranging from $36,000 to $110,000 — a range that suggests a mix of smaller independents and mid-sized shops. LedgerLark says one customer reduced its unresolved daily exceptions from 70 to 18. That figure is the company's own and hasn't been independently verified, but if it holds across deployments, it points to the kind of operational relief that buyers in this segment tend to notice. The company has raised a $3.5 million seed round led by Motive Row. ## What's uncertain Several things remain open. Five customers is early, and post-trade operations is a conservative buyer category: financial institutions often require lengthy security and procurement reviews, which can stretch sales cycles well beyond what a seed-stage company might prefer. The pilot-to-paid conversion rate — and how long that conversion takes — will matter more than the headline number. Competition is another question. Established reconciliation vendors already sit inside many firms, and large banks have built internal software that does much of this work, albeit at a cost smaller firms can't easily replicate. LedgerLark's bet is that its agents handle messy, unstructured exceptions better than rules-based tools. That's plausible but unproven at scale, and incumbents have every incentive to add similar features. There's also the question of scope. Staying out of execution and money movement keeps regulatory exposure lower, but it also caps how deeply the product can embed itself in a customer's workflow. At some point, firms may ask for more. For now, LedgerLark is a focused tool for a stubborn problem. Whether "focused" becomes "too narrow" — or the foundation for something larger — is the question the next year or two should answer.

muse-spark-1.3: # Startup Spotlight: LedgerLark Aims at the Mess After the Trade Most market attention goes to what happens before and at the moment of a trade. LedgerLark is focused on what comes after: the confirmations, records, and ledgers that have to agree before a trade is considered settled. The startup builds AI agents for post-trade operations at small investment firms, a segment where back-office teams are often thin. ## The insight from the operations desk Co-founder Mina Batra spent seven years in operations at a regional brokerage. Co-founder Owen Carr built reconciliation systems at a large bank. The idea for LedgerLark traces back to an incident Batra observed, in which a six-person team spent three days resolving a trade mismatch caused by a copied account number. That kind of delay is familiar to operations staff. A small data entry error can cascade across systems, creating exceptions that require manual comparison across documents. For larger firms, there may be dedicated teams and custom internal software to work through the queue. For smaller firms, the same volume of exceptions can tie up a large share of staff time. Batra and Carr's bet is that smaller firms feel this pain most acutely, and may be more open to outside help if the tool fits into existing checks. ## What the product does — and doesn't do LedgerLark's agents compare three sources: trade confirmations, custodian records, and internal ledgers. When the figures don't line up, the system flags the mismatch and proposes a correction for a person to review and approve. Importantly, the product does not execute trades or move money. That scoping decision keeps the agents in an assistive role, rather than giving them authority over funds or market actions. In practice, that means the value depends on two things: whether the comparisons are accurate enough to save time, and whether operations staff trust the suggested corrections enough to work through them faster than they would manually. Those are open questions for any early-stage automation product in financial services, where errors carry regulatory and financial consequences. ## Early customers and results LedgerLark reports five paying customers, with annual contracts ranging from $36,000 to $110,000. That price range suggests the company is selling team-level tools rather than enterprise-wide infrastructure at this stage. On outcomes, the company says one customer reduced its unresolved daily exceptions from 70 to 18 after adopting the product. That figure comes from the company and has not been independently verified, and it represents a single example rather than an average across users. It will matter whether similar results show up at other firms, with different workflows and data quality, and whether reductions persist once initial backlogs are cleared. ## Funding, competition, and the long sales cycle The company has raised a $3.5 million seed round led by Motive Row. For a company selling into financial services, that capital will need to cover not just product development but the cost of meeting institutional expectations around security, audit trails, and reliability. Competition is another factor. Established reconciliation vendors already serve this market, and many larger institutions rely on internal bank software built over years. LedgerLark is not competing purely on whether reconciliation can be automated, but on whether a lighter-weight agent that sits alongside existing systems is easier for small firms to adopt and maintain. Financial institutions may also require lengthy security and procurement reviews, which can stretch sales cycles even when initial interest is strong. That process can be especially demanding for startups working with sensitive data, and may shape how quickly five early customers can become 15 or 50. For now, LedgerLark offers a narrow, testable proposition: fewer open exceptions without handing control to software. Whether that holds across more firms will be the story to watch.

DeepSeek-V4.1-Flash: # QueueZero’s New AI Assistant Takes Meeting Remark Literally, Assigns CEO Two Tasks QueueZero launched Proxy, an AI meeting assistant, on Wednesday with a live demonstration that showed both the product’s promise and its literal-mindedness. Proxy is designed to attend meetings, take notes, and assign follow-up work to software agents. According to the company, it can also route tasks to human participants, a feature that became the centerpiece of the launch event when a customer in the audience said, “Someone should tell Evan to stop talking and send us the pricing sheet.” Proxy treated the aside as an instruction. It created two tasks assigned to QueueZero CEO Evan Wu: “Stop talking” and “Send pricing sheet.” Wu laughed, completed the second task, and deleted the first. The company confirmed the sequence of events and said the moment reflected how the system handles conversational input rather than a flaw in the demonstration. QueueZero says Proxy requires confirmation before sending any external message, so remarks made in a meeting do not automatically leave the room. Internal tasks, however, can be created automatically. Administrators can turn off automatic task creation, a control the company said is intended for teams that want tighter oversight of how meeting remarks become action items. The launch places QueueZero in a growing market of AI assistants built for workplace meetings. Competitors have focused on transcription, summaries, and searchable archives. QueueZero’s pitch is that Proxy goes further by turning discussion into assigned work, including tasks handled by software agents that can execute follow-ups without a human operator. For now, the most visible example of that capability is a CEO receiving a to-do list generated from a single sentence spoken by a stranger. Proxy costs $30 per user per month. It enters public beta next week. The pricing sheet, meanwhile, has been sent.

muse-spark-1.3: # QueueZero Launches Proxy, an AI Meeting Assistant for Automated Follow-Up **SAN FRANCISCO —** QueueZero launched an AI meeting assistant called Proxy on Wednesday, entering a crowded market for tools that promise to reduce the administrative work surrounding meetings. Proxy can attend a meeting, produce notes and assign follow-up work to software agents, the company said. QueueZero positioned the product as more than a transcription service, describing a system that identifies decisions, requests and next steps and converts them into tasks that agents can execute or track. That behavior was illustrated during the launch demonstration, when a remark from the audience was treated as a directive. During the demonstration, a customer in the audience said, “Someone should tell Evan to stop talking and send us the pricing sheet.” Proxy interpreted the remark as an instruction. It created two tasks assigned to QueueZero CEO Evan Wu: “Stop talking” and “Send pricing sheet.” Wu laughed, completed the second task and deleted the first. QueueZero says Proxy requires confirmation before an external message is sent. Internal tasks can be created automatically, allowing the system to log action items without interrupting the meeting. Administrators can disable automatic task creation, the company said, a control intended for organizations that want human review before any task is recorded. The incident highlights a broader challenge for agent-based assistants that act on conversational cues. Systems designed to be responsive must distinguish between casual remarks, suggestions and explicit instructions, particularly in multi-speaker environments where context shifts quickly. QueueZero says its permission model is designed to limit the impact of misinterpretation. By requiring confirmation for outbound communication and allowing administrators to restrict automation, the company said it aims to keep humans in control of external actions while preserving speed for internal documentation. Proxy costs $30 per user per month. It enters public beta next week. Public betas typically allow organizations to test such tools in live workflows before a wider rollout, providing feedback on reliability and usability. Meeting assistants have become a testing ground for agentic software, as vendors move from summarization toward systems that can act on what is discussed. The shift raises questions about accuracy, permissioning and oversight when spoken language becomes input for automated workflows. Control and reliability remain central concerns as these systems gain broader access to workplace communication.

DeepSeek-V4.1-Flash: # The Cheap Model Nearly Won: What a 200-Ticket Support Benchmark Actually Shows When five language models were put through the same customer-support workload, the headline wasn't the winner. It was the runner-up's price tag. In a blind evaluation of 200 anonymized support tickets, Model Vale scored 83.8 out of 100 — just 0.4 points behind Model North's 84.2. That gap was not statistically significant, meaning the two are best understood as tied on quality. But Vale cost $0.74 per 1,000 tickets against North's $1.90, answered 18% faster, and beat North outright on technical troubleshooting questions. North's advantage showed up on billing disputes. For teams choosing a support model, the practical takeaway is uncomfortable but useful: the most expensive option was not meaningfully better than one costing roughly 40% as much. And both were outperformed on cost-effectiveness by cheaper models that still cleared a respectable bar. ## How the test worked Each model produced one proposed answer per ticket. Three experienced support managers then graded those answers blindly — they didn't know which model wrote what — using a combined score for factual correctness, usefulness, and tone. That matters because tone and usefulness are exactly what generic accuracy benchmarks miss. A response can be factually flawless and still useless to an angry customer. The spread across all five models was wide: North at 84.2, Vale at 83.8, Ember at 79.1, Kite at 76.4, and Harbor at 71.0. Costs ranged from $1.90 per 1,000 tickets down to $0.08 — a 24-fold difference between the top scorer and the cheapest. It's tempting to read that as a simple quality-versus-price curve. It isn't quite. The top two models cluster tightly, then there's a real drop to Ember, and another to Kite and Harbor. Harbor's $0.08 per 1,000 tickets looks like a bargain until you consider that its answers scored 13 points below North's — a gap large enough to translate into visibly worse customer interactions, more escalations, and more rework. ## Where every model struggled One finding cut across the entire field: all five models performed poorly when a ticket depended on an image attachment. This is a limitation of the test setup as much as the models — the benchmark fed text, and any visual context in those tickets was effectively lost. But it's also a warning. Real support queues are full of screenshots, error photos, and annotated diagrams. If your ticket volume leans on images, none of these scores tell you much. Similarly, the benchmark only covered English-language tickets from a single software company, and it explicitly excluded agentic actions, tool use, and multilingual support. So these numbers say nothing about whether a model can actually resolve a ticket end-to-end — issue a refund, reset an account, update a record — or handle a customer writing in Portuguese. They measure drafted answers, not completed work. ## What the results don't settle The 0.4-point gap between North and Vale is the kind of difference that would likely flip if you reran the test with different tickets, different graders, or a different scoring rubric. Anyone citing North as "the best model" from this data is overreading it. The honest framing is that North and Vale tied, and Vale did it cheaper and faster. The more durable signals are the shape of the distribution: a tight top tier, a meaningful middle, and a cheap tier that costs little but gives up real quality. North's edge on billing disputes and Vale's on technical troubleshooting hint at genuine specialization, but with only 200 tickets split across categories, those per-category results are directional, not definitive. Don't reorganize your support stack around them yet. ## The decision this benchmark actually supports The most defensible conclusion is also the least dramatic: for English-language software support tickets that don't hinge on images, a mid-priced model like Vale can match the quality of a premium one while cutting cost per thousand tickets by more than half and responding faster. If your workload skews toward technical troubleshooting, that case gets stronger. What the benchmark can't tell you is whether any of these models should be trusted to act — to process the refund rather than just draft the apology. That's a different test, and nobody has run it here.

muse-spark-1.3: # The Best Support Bot Wasn't the Winner For customer-support teams considering AI, the question is rarely whether a model can answer, but whether it can answer well enough, cheaply enough, and fast enough to matter. A new benchmark built around 200 real, anonymized support tickets offers an unusually practical answer: the two best models were essentially indistinguishable on quality, but one was dramatically cheaper and quicker. That gap, not the leaderboard itself, is the story. ## How the test worked Researchers had each of five models draft a single response to each ticket, then asked three experienced support managers to grade the responses without knowing which model wrote what. Scores blended factual correctness, usefulness, and tone — the three things that determine whether a draft actually saves an agent time or creates more cleanup work. ## The tie that matters more than the trophy That's important context for the headline result. Model North earned the highest average at 84.2, just ahead of Model Vale at 83.8. Statistically, that difference means nothing — the researchers found it was not significant. In operational terms, it means nothing either. Yet the cost difference is enormous: about $1.90 per 1,000 tickets for North versus $0.74 for Vale. Vale was also 18% faster, a margin customers would feel in queue times. This is the classic good-enough tradeoff made concrete. If you pay more than twice as much for no measurable gain in quality and slower responses, you need a very specific reason to do so. Otherwise the rational buy is Vale, not North. The rest of the field reinforces that cost and quality do trade off, just not linearly. The cheaper models scored noticeably lower overall — roughly five to thirteen points behind the leaders — while offering progressively lower running costs down to just eight cents per 1,000 tickets for the least expensive option. Nothing in the results suggests a hidden bargain that matches the leaders for pennies. Instead, there is a middle tier that might make sense for low-risk tickets or assisted drafts where a human will heavily edit, and a bottom tier where savings likely evaporate in corrections and customer frustration. ## Averages hide what managers need The leaderboard also hides useful specialization. North handled billing disputes best, while Vale led on technical troubleshooting. That split matters because support queues are not generic. A company drowning in how-do-I-fix-it tickets and a company drowning in refund-confusions should not necessarily buy the same model, even if their average scores look identical. Buyers should weight performance by their own ticket mix, not by an overall average built from someone else's. More revealing than who won is where everyone lost. All five models performed poorly when a ticket depended on an image attachment — a screenshot of an error, a photo of a broken setup, a marked-up invoice. In those cases, text-only drafting breaks down regardless of price or vendor. For real deployments, that is a warning against full automation. If roughly any share of your queue is “see attached,” you still need humans in the loop, or a different system that can actually see. ## What this doesn't prove Those caveats point to the study’s limits, which are substantial. The test used only English-language tickets from a single software company, graded on draft quality alone. It did not test agentic actions like issuing refunds or changing passwords, tool use like checking order status, or multilingual support. In other words, it measures a narrow but common job: write a good first draft for a human agent. It does not tell you which model will safely resolve tickets on its own. For managers, the practical takeaway is threefold. First, treat tiny score gaps as ties until proven otherwise; demand significance testing, not just rankings. Second, pilot the value leader on your own tickets, especially if your queue skews technical, and measure edit time, reopen rate, and customer sentiment — not just grader scores. Reserve the premium option for segments where it demonstrably wins, like billing sensitivity, if the math justifies it. Third, design explicitly for image-dependent tickets rather than hoping a better language model will solve them. Done right, this benchmark is not an argument that one model is smarter. It is an argument that smart buying beats raw scoring.

DeepSeek-V4.1-Flash: # Raincheck Raises $8 Million to Help Cities Prepare for Floods Before the Storm *The Austin startup's software maps drainage systems and pairs them with rainfall forecasts, giving local governments a way to prioritize repairs and maintenance ahead of severe weather.* Raincheck, an Austin-based company that makes flood-risk software for local governments, has raised $8 million in seed funding. The company maps drainage systems and combines those maps with rainfall forecasts. Cities use the software to decide where to send maintenance crews before a storm. The seed round was led by Basin Ventures, with participation from existing investor Long Mile. Raincheck has raised $10.5 million in total. CEO Ana Flores said the company's customers typically already know which streets flood. > "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." The company's [forecasting methodology](https://example.com/raincheck-methodology) underpins the product. 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. Other flood-planning vendors include HydroMap and municipal engineering contractors. Raincheck says the new funding will help it hire eight engineers and expand into Florida.

muse-spark-1.3: # Raincheck raises $8 million to help cities get ahead of floods *The Austin startup maps drainage systems and rainfall forecasts to help local governments prioritize repairs before storms* Austin-based Raincheck, which makes flood-risk software for local governments, announced today it has raised $8 million in seed funding. The company maps drainage systems and combines those maps with rainfall forecasts, according to [its methodology overview](https://example.com/raincheck-methodology). Cities use the software to decide where to send maintenance crews before a storm. The seed round was led by Basin Ventures, with participation from existing investor Long Mile. The new financing brings Raincheck's total raised to $10.5 million. CEO Ana Flores said: > “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.” 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. Emergency managers retain control over deployments and public warnings. Other flood-planning vendors include HydroMap and municipal engineering contractors. Raincheck says the new funding will help it hire eight engineers and expand into Florida.

DeepSeek-V4.1-Flash: # KiteBridge’s ‘Exactly-Once’ Payment Promise Failed—and $2.8 Million Went Out Twice **A routine software update on Tuesday morning triggered thousands of duplicate employee reimbursements at corporate expense, forcing KiteBridge to shut down automated payments and spend the next two days clawing the money back.** KiteBridge, which sells expense-management software to companies, deployed version 4.2 at 8:45 a.m. Tuesday. The update was routine. What followed was not. The first customer complaint arrived at 9:12 a.m. Behind it lay a chain of failures that KiteBridge would later trace to a payment-processor timeout. When the processor failed to respond in time, KiteBridge’s fallback system misinterpreted retries as fresh reimbursement requests. Rather than recognizing that a payment was already in flight, the system treated each retry as a new claim and paid it. The result: 13,400 duplicate reimbursements, totaling $2.8 million, across 91 corporate customers in the span of roughly an hour. At 10:03 a.m., KiteBridge suspended automated payments, halting the outflow about 78 minutes after the faulty version went live. By Wednesday evening, the company had recovered $2.3 million. The remaining $500,000 was still pending, meaning some employers had not yet received the returned funds. Crucially, no employees had money withdrawn from their accounts. The duplicates were funded by employers—the companies that use KiteBridge to reimburse staff—not by workers themselves. That distinction limited the personal financial damage, though it left corporate finance teams scrambling to reconcile payments they never authorized. The failure carried a particular sting because KiteBridge had advertised protection against exactly this kind of error. An April changelog said version 4.0 had introduced “exactly-once payment protection,” a standard guarantee in payment systems that a transaction is processed only one time even when networks falter or requests are retried. Version 4.2 appears to have undermined that safeguard: when the processor timed out, the fallback logic bypassed the safeguard’s intent, converting retries into new payments instead of recognizing them as attempts to complete an existing one. For a general reader, the distinction matters. Payment systems frequently retry requests when a processor is slow or unresponsive, because a missing response does not necessarily mean a payment failed—it may simply mean the confirmation was lost. Robust systems use unique identifiers and idempotency keys to ensure a retried request is matched to its original, preventing double payment. KiteBridge’s fallback, in this case, lacked that matching step. Communication compounded the problem. KiteBridge’s public incident page initially described the issue as delayed reimbursements—a materially different and less alarming characterization than duplicate payments. The page did not disclose the duplicates until six hours after automation was suspended. For customers checking the page during that window, the information suggested money was late, not that it had been paid twice. CEO Lena Ortiz apologized and said the company would publish a technical report. That report will be scrutinized not only for what went wrong in version 4.2, but for how a feature marketed as “exactly-once” protection failed on its first widely visible test—and how KiteBridge described the failure to the customers bearing its cost.

muse-spark-1.3: # KiteBridge Software Bug Sent $2.8 Million in Duplicate Reimbursements **91 corporate customers hit after Tuesday update; $2.3 million recovered by Wednesday** Ninety-one companies were left trying to reclaim thousands of overpayments to their own workers this week after a failed software update from expense-management provider KiteBridge generated 13,400 duplicate reimbursements totaling $2.8 million. The mistake put extra money into employees' pockets, not less. KiteBridge confirmed that no workers had funds withdrawn from their accounts. Instead, the duplicate payments were funded by employers, who overpaid expense claims and are now waiting for the money to be returned. By Wednesday evening, $2.3 million had been recovered, with the rest still pending through banks. The sequence began on Tuesday morning, when KiteBridge deployed version 4.2 of its platform. The expense-management software is used by corporate finance teams to approve employee expense reports and automatically send reimbursement payments. Shortly after the deployment, the company's payment processor — the outside service that actually moves money between bank accounts — began timing out. In plain terms, KiteBridge would send a payment instruction and then wait for confirmation, but the confirmation never arrived. KiteBridge's software responded the way it was programmed to do when a payment is uncertain: it tried again through a fallback system. The problem was that the fallback treated each retry not as a second attempt at the same payment, but as a brand-new reimbursement request. Employees who should have been paid once were paid twice, three times, or more, with each retry creating fresh money movement. That outcome is striking because KiteBridge had previously promised protection against exactly this kind of failure. An April changelog for version 4.0 said the company had introduced "exactly-once payment protection," an industry term for safeguards designed to ensure that even if a system retries a payment after an error, the recipient is only paid a single time. The first customer complaint about abnormal payments arrived less than half an hour after the update went live. KiteBridge suspended all automated payments a little over an hour later, halting further duplicates. Its public communications were slower to catch up. The company's incident page initially described the issue only as delayed reimbursements, suggesting workers would be paid late rather than paid extra. It was six hours after automated payments were suspended before the page was updated to disclose that duplicate payments had been sent. CEO Lena Ortiz has since apologized to affected customers and said KiteBridge will publish a full technical report explaining how the safeguard failed and what will prevent a recurrence. For now, finance teams at the 91 affected companies are reconciling expense ledgers and confirming recoveries, a reminder of how quickly a small breakdown in back-office automation can turn into millions of dollars moving to the wrong place.

DeepSeek-V4.1-Flash: # Arcwell's New AI Tool Aims to Speed Up Customer Support Arcwell today launched Relay, an AI platform that reads customer support conversations and suggests responses to agents in real time. The company says the tool, tested by 12 companies over six months, helped agents close tickets 14 percent faster. Arcwell did not release customer-retention results, though Relay is designed to flag customers who may cancel and to generate conversation summaries. The platform costs $65 per agent per month. "Support agents spend too much time searching for information that their companies already have," CEO Priya Shah said in a statement. Relay's pitch rests on a familiar promise: that AI can surface the right answer without an agent digging through knowledge bases or past tickets. For Arcwell, the launch marks a bid to move beyond the crowded market for basic chatbots and into the daily workflow of human support teams. The approach carries privacy trade-offs. Relay needs access to support transcripts and internal help documents to function. Arcwell says customer data is encrypted and is not used to train shared models. Clients can choose a 30-day or 90-day data retention period. Those assurances may not resolve every concern. Support transcripts often contain sensitive customer information, and companies evaluating Relay will need to weigh faster ticket resolution against the work of vetting how their data is handled. Arcwell's encryption and retention options give buyers some control, but the burden of compliance still falls on the businesses that adopt the tool. The six-month pilot offers an early signal, not a guarantee. Ticket-closing speed is one measure of success; whether Relay reduces churn—the outcome the platform also promises to predict—remains unproven. Arcwell has not said how many of the 12 pilot companies plan to continue using the product. For now, Relay enters a customer service market where AI tools are proliferating and differentiation is difficult. Arcwell is betting that speed, integration with existing help documents and flexible data controls will set it apart.

muse-spark-1.3: # Arcwell Bets on AI to Lighten the Load for Support Teams Arcwell is launching Relay, a platform that uses artificial intelligence to read customer-support conversations and nudge agents toward answers pulled from a company’s own help documents. The tool is designed to sit alongside agents rather than replace them. As a chat or ticket unfolds, Relay suggests replies, flags customers who look likely to cancel, and drafts summaries once the exchange is over. CEO Priya Shah said the idea came from a familiar bottleneck: “Support agents spend too much time searching for information that their companies already have.” Relay, she said, is meant to close that gap by surfacing the right passage at the right moment. Arcwell tested the system with 12 companies over six months. Agents using Relay closed tickets 14% faster, according to the company. Arcwell did not release customer-retention results, leaving open the question of whether faster resolutions translate into fewer cancellations — the other problem Relay claims to tackle. Relay will cost $65 per agent per month. That approach keeps a human in the loop at a time when many support leaders remain wary of letting automated systems answer customers directly. The bet is that trimming the time agents spend hunting for information will pay off in shorter queues without handing the conversation entirely to software. The trade-off is access. To work, Relay needs a feed of support transcripts and a connection to internal help documents — the same repositories it draws suggested answers from. Arcwell says customer data is encrypted and is not used to train shared models. Companies can choose a 30-day or 90-day retention period. How companies weigh that exchange will likely determine uptake. Shorter storage means less exposure if something goes wrong, but also less history to learn from when a complicated issue resurfaces weeks later. Support leaders will have to decide what level of retention they are comfortable with. For Arcwell, Relay is a push deeper into day-to-day agent workflows. Whether it sticks will depend less on the promise of artificial intelligence than on the unglamorous math every support chief knows: time saved per ticket, agents retained, and customers kept.