Head to head: Meta: Muse Spark Open vs MiniMax: MiniMax M3

Meta: Muse Spark Open: # HelioDock Will Let Warehouse Managers Watch Robot Fleets From a Distance *Its Night Shift software, tested for 14 months with nine logistics firms, is set for wide release in September alongside a $24 million funding round* HelioDock will let warehouse managers monitor their robots without being in the building. The company, which makes autonomous warehouse robots, said Thursday it has launched Night Shift, software that allows operators to supervise fleets remotely and intervene when a machine hesitates on the floor. The introduction is aimed at the hours when buildings run with the fewest people on site. Facilities that keep robots moving overnight often do so with reduced crews, so a single unit stopped by a misplaced pallet can require a call-in. “A warehouse manager should not have to drive across town at two in the morning because one robot is confused by a pallet,” Chief Executive Mara Velez said. Night Shift is HelioDock’s effort to handle that moment from a distance. According to HelioDock, the software was shaped by extended use in live operations. The company said Night Shift spent 14 months in private beta with nine logistics companies and 310 robots. During that period, beta customers experienced 17% fewer unplanned robot stoppages, HelioDock said. That figure has not been independently verified. The company paired the product introduction with a new financing. HelioDock said it has raised a $24 million Series B led by North Coast Ventures, bringing its total funding to $39 million. The capital arrives as Night Shift heads toward broader sale. The company said the software will cost $90 per robot per month and become generally available September 15. Early use pointed to where remote oversight proved most valuable and where its limits appeared. 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. HelioDock said when connectivity fails, robots return to their locally configured behavior and remote control becomes unavailable. That boundary defines what Night Shift can and cannot do from afar. While the connection holds, operators can watch and direct the fleet; when it drops, the machines fall back to on-board programming. The length of that trial gave HelioDock time to collect data on how remote intervention aligns with daily operations. The nine logistics companies in the beta operated a combined 310 robots, providing the basis for the 17% figure the company cites. The company attributed the decline to remote oversight. HelioDock is entering a market where that capability is already offered elsewhere. Relay Robotics and YardOS both sell competing fleet-management products, making remote visibility an expected feature rather than an outlier. Both firms were identified as existing alternatives in fleet management. For HelioDock, the September release will move Night Shift beyond the scope of its beta. The nine companies in the trial together ran 310 robots, a base the company is using to support its claim of fewer interruptions. Wider deployment will place the software in a broader set of floor plans and network conditions, including the rural sites Pike described. That expansion will take the product from controlled beta conditions to the varied connectivity found in general use. The $90 per robot per month price will apply at general availability on September 15. The figure ties ongoing cost to fleet size, distinct from the robots that HelioDock already sells. Whether the reduction in unplanned stoppages cited by HelioDock holds across that larger footprint remains to be seen, and the company’s figure has not been independently verified. The performance of off-site control when LTE service weakens will also face closer attention once Night Shift is no longer limited to the beta sites.

MiniMax: MiniMax M3: # HelioDock Rolls Out Remote Robot-Fleet Monitoring Software After 14-Month Beta *Warehouse robotics maker raises $24 million Series B as Night Shift moves to general availability on September 15.* Warehouse robotics company HelioDock on Thursday launched Night Shift, a software product that lets warehouse operators monitor autonomous robot fleets from off-site locations. The release closes out a 14-month private beta that involved nine logistics companies and roughly 310 robots operating across commercial facilities, according to the company. HelioDock, which builds the autonomous robots used in those facilities, has positioned Night Shift as an operations layer on top of its hardware business. The software is designed to consolidate live alerts, diagnostics and fleet status into a single dashboard accessible from a laptop or phone. For managers accustomed to dispatching technicians to the floor after hours, the appeal is direct. "A warehouse manager should not have to drive across town at two in the morning because one robot is confused by a pallet," CEO Mara Velez said in remarks accompanying the launch. The dashboard is meant to let staff identify problems remotely and decide whether an in-person visit is needed at all. HelioDock said beta customers experienced a 17% reduction in unplanned robot stoppages during the trial. The figure has not been independently verified, and the company declined to share its underlying methodology or to explain how it separated product-driven improvements from normal operational gains over the same window. Alongside the product launch, HelioDock announced a $24 million Series B round led by North Coast Ventures, bringing total funding to $39 million. HelioDock did not specify how the new capital will be deployed, though expanding the engineering and customer-success teams behind Night Shift is a likely priority given the September general availability date. Beta feedback has highlighted both the software's strengths and its constraints. Aaron Pike of Fulcrum Logistics, one of the nine participating operators, said the platform worked well in facilities with strong connectivity but offered less value during rural LTE outages. "It worked great at our main DC, but at the rural sites we sometimes lost the dashboard for hours at a time," Pike said in comments shared by HelioDock. Fulcrum is among the operators expected to expand its use of the software. That limitation is built into the product's design rather than treated as a defect to be patched in a later release. When network connectivity fails, individual robots revert to their locally configured autonomous behavior, and remote monitoring becomes unavailable. HelioDock has not announced an offline mode or a satellite-based fallback. Operators running fleets in low-coverage regions will need to weigh that trade-off as they evaluate the subscription. Night Shift enters a market that already includes fleet-management offerings from Relay Robotics and YardOS, both of which compete for the same warehouse-operations budgets. HelioDock is pricing the software at $90 per robot per month, framing Night Shift as a recurring revenue line on top of its hardware business. The company did not address how its pricing compares to competitors' rates. HelioDock also declined to disclose revenue, customer retention figures or the identities of the nine beta participants. It did not say whether the participating companies have committed as paying customers once general availability begins on September 15, or whether they will be billed under the new per-robot pricing. The company said it expects to onboard additional logistics operators in the run-up to the launch. Pike said Fulcrum Logistics plans to extend the deployment to two more sites next quarter, contingent on improving connectivity at those locations first.

Meta: Muse Spark Open: # QueueZero Unveils Proxy, an AI Assistant to Automate Meeting Follow-Ups QueueZero on Wednesday launched Proxy, its new AI meeting assistant capable of attending meetings, producing notes and assigning follow-up work to software agents. The announcement was made during a live launch demonstration attended by customers, partners and media. According to the company, Proxy is designed to listen to meeting discussions, summarize key decisions and automatically delegate action items without requiring manual input after the call ends. QueueZero said Proxy builds on recent advances in speech recognition and large language models to identify speakers, extract decisions and route work to connected applications. The company said the goal is to shorten the gap between discussion and execution for distributed teams. The demonstration included an unscripted moment that illustrated how the system interprets conversational language. During the event, 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. The system created two tasks and assigned them to QueueZero Chief Executive Evan Wu: “Stop talking” and “Send pricing sheet.” Wu laughed, completed the second task and deleted the first, the company said. QueueZero said the incident did not disrupt the presentation and was resolved in real time. The company said the episode highlights both the capabilities and safeguards built into Proxy. While internal tasks can be created automatically based on what the assistant hears, the system requires user confirmation before any external message is sent on behalf of a user. Administrators also have the option to disable automatic task creation entirely, QueueZero said, allowing organizations to require manual review of all suggested follow-ups before they are assigned. Proxy will be priced at $30 per user per month, according to QueueZero. The company said the product will enter public beta next week, with general availability to follow based on feedback from the beta period. QueueZero said Proxy is intended to integrate with existing calendars and workplace tools, allowing the assistant to join scheduled meetings and distribute notes and tasks immediately afterward. The company did not disclose additional pricing tiers or enterprise features. The launch places QueueZero among a growing number of firms offering AI-driven productivity tools aimed at automating routine workplace coordination. Analysts say such tools are increasingly common as vendors compete to embed generative AI into everyday workflows.

MiniMax: MiniMax M3: # QueueZero's Proxy AI Assistant Turns Heckle Into CEO's To-Do List QueueZero introduced an artificial intelligence meeting assistant called Proxy on Wednesday, a tool designed to attend virtual meetings, take notes and dispatch follow-up work to software agents across an organization. The product enters public beta next week at $30 per user per month. It arrives in a crowded field of AI assistants from vendors including Otter, Zoom and Microsoft that aim to convert meeting talk into structured records and assigned action items. At a launch demonstration on Wednesday, a member of the audience grew impatient with the length of the presentation and called out that someone should tell CEO Evan Wu to stop talking and send the pricing sheet. Proxy, which was listening to the demonstration in real time, parsed the remark as a directive. The assistant generated two internal tasks and assigned both to Wu: "Stop talking" and "Send pricing sheet." Wu, who founded QueueZero in 2021 and has positioned the company around workflow automation, addressed the moment on stage. He laughed, then completed the second task by emailing attendees the pricing document. The first task, he said, he had deleted. The episode underscored a question that has accompanied the rapid spread of generative AI tools into workplaces: how should an autonomous assistant decide which spoken remarks constitute actual instructions? QueueZero's answer, set out in materials distributed Wednesday, is layered. External communications require explicit human confirmation before Proxy will send them. The assistant may draft an email, for example, but will not transmit it without a user clicking approve. Tasks created inside a customer's own systems, however, can be generated automatically. Administrators may also disable automatic task creation company-wide if they prefer a more conservative posture. QueueZero described Proxy as a step toward what the company calls "ambient operations," in which software agents monitor meetings, support tickets and chat channels and act on routine items without prompting. Critics of the category have warned that such systems can quietly introduce errors into records, surface remarks to unintended audiences or escalate throwaway comments into formal work items. QueueZero said Proxy had been trained to distinguish between offhand remarks and directed instructions, but acknowledged the technology is not infallible. The company said it was reviewing the launch demonstration transcript and would consider additional guardrails before the public beta opens next week.

Meta: Muse Spark Open: # The Last Cycle at Foundry Hall Foundry Hall, the nonprofit startup accelerator that has anchored a converted warehouse on Milwaukee's near west side since 2018, will close on Oct. 31. The decision, after seven years in operation, will leave six employees without jobs and force twelve startups currently using its workspace to leave by the end of next month. Since opening, Foundry Hall has supported 212 startups, which together now employ 1,140 people across the region. The closure follows the loss of its largest corporate sponsor. Blank River Bank, which supplied 38% of the accelerator's annual budget, declined to renew its sponsorship this year. The bank has characterized the move as a change in its community investment priorities, unrelated to Foundry Hall's performance. Executive Director Camille Ross said staff spent months trying to fill the gap. "We kept waiting for the replacement check that never came," she said. Without that funding, the math stopped working, Ross explained. Foundry Hall has enough reserves to operate through October, but not enough to run another full program cycle, and its board voted this week to wind down operations rather than attempt a scaled-back version. Ross said the staff were told last Friday. Until then, she said, many had been volunteering extra hours to keep mentoring sessions and office hours running while leadership pursued new sponsors. At City Hall, officials are looking for places to land the displaced companies. Deputy Mayor Susan Cole said the city has begun conversations with building owners and local universities about available space, though no specific sites have been promised. Alumni have offered to host office hours in borrowed rooms this fall. The news has been felt most acutely by the founders who came through its doors. Dev Malik, whose company was among Foundry Hall's early cohorts, remembered pitching from the back of a coffee shop before he had a place to bring investors. His first real investor meeting, he said, happened in that building. That sentiment — of Foundry Hall as a first address — has echoed in messages since the announcement. For Milwaukee, which has leaned on the accelerator to turn early ideas into employers, the closure leaves both a literal vacancy and a less visible one: the informal network of mentors, alumni, and late-night work sessions that grew up around it. When the doors close on Oct. 31, Ross said, the goal is to leave records, contacts and introductions in good order for whoever tries next.

MiniMax: MiniMax M3: # The Last Light at Foundry Hall On October 31, Foundry Hall will close its doors after serving Milwaukee since 2018. The nonprofit startup accelerator opened that year, offering shared space, advice, and a measure of permanence to founders who might otherwise pitch from coffee shops or improvised offices. Its reach has been substantial: 212 startups have passed through its programs, and those companies collectively employ 1,140 people. The closing will not arrive all at once. It has been settling in for months. “We kept waiting for the replacement check that never came,” said Camille Ross, Foundry Hall’s executive director. Its largest corporate sponsor, Blank River Bank, 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 through another full program. Blank River Bank said the decision reflects a change in its community investment priorities and is unrelated to Foundry Hall’s performance. The statement may ease concern about the accelerator’s work, but it does not soften the consequences of the lost support. A sponsor can reject one institution without diagnosing why an ecosystem is fragile. Here, one renewal became a boundary: the organization could finish October, but not another full cycle. For Dev Malik, the loss is personal. “My first investor meeting happened in that building,” he said. Before Foundry Hall, he was pitching from the back table of a coffee shop. That progression—from a borrowed corner to an investor meeting—captures what the accelerator offered: not only a desk, but access, credibility, and a place to begin. Malik is one of many founders whose companies moved through its programs. Milwaukee’s response remains unsettled. Deputy mayor Susan Cole said the city is speaking with building owners and local universities about space for the current companies. Such arrangements might preserve a desk or a daily routine. They cannot, by themselves, replace the programs, staff, and continuity that are disappearing with Foundry Hall. In its final weeks, Foundry Hall’s rooms will hold the history of the work: pitches prepared before meetings, early products revised at shared tables, companies finding room beside one another. The details are modest because the work was. People came with ideas and left with companies. The scale of that work will be easy to underestimate as the twelve startups now using the workspace prepare to leave. On October 31, six employees will lose their jobs, and the organization will close its doors. The waiting is over. What remains is the harder task of carrying the work elsewhere without pretending it can happen in the same way.

Meta: Muse Spark Open: # Employers Left Covering $2.8 Million in Duplicate Payments After KiteBridge Update Fails **Employers footing the bill for thousands of unintended payouts are still waiting for full recovery after an update to popular expense-management software turned routine retries into repeat payments.** KiteBridge, maker of expense-management software used by corporate clients to reimburse employee expenses, said a bug in its latest update caused its system to generate 13,400 duplicate reimbursements on Tuesday, sending $2.8 million in extra payments to employees. While the company has since recovered most of the funds, the incident has drawn scrutiny to both its technical safeguards and its initial public response. The problems began Tuesday morning shortly after the company deployed version 4.2 at 8:45 a.m. According to KiteBridge, the first customer complaint came in at 9:12 a.m., less than half an hour later. By 10:03 a.m., the company had suspended all automated payments — but not before dozens of corporate customers were affected. In total, 91 corporate customers saw duplicate transactions processed before the shutoff. No employees had money withdrawn from their accounts; because reimbursements flow from employer to employee, the duplicate costs were borne by the employers themselves. In a post-incident summary, KiteBridge traced the failure to an interaction between its payment processor and its own fallback logic. When the payment processor timed out, it failed to confirm whether original reimbursement requests had succeeded. KiteBridge's fallback system, designed to keep payments moving during such outages, then retried the requests — but mistakenly treated each retry as an entirely new reimbursement rather than a repeat attempt of the same one. For a general reader, the distinction matters. Expense platforms like KiteBridge typically rely on safeguards to ensure that even if a payment needs to be retried, it is only executed once. Without that check — often called idempotency in software design — a single $200 expense claim can become two or more identical payouts if the system loses track of what it has already sent. That safeguard was supposed to be in place. KiteBridge's changelog for version 4.0, released in April, said the update had introduced "exactly-once payment protection," a feature intended to prevent precisely this kind of duplication. The company's initial communication told a different story. Its public incident page first described the issue as delayed reimbursements, not extra ones. It was not until six hours after automated payments had been suspended that the page was updated to disclose that duplicate payments had been sent. Recovery efforts have since made progress. By Wednesday evening, KiteBridge said $2.3 million of the $2.8 million had been recovered, with the remainder still pending as banks process reversals. CEO Lena Ortiz has apologized to affected customers and said the company will publish a full technical report on the cause and its planned fixes. Until then, questions remain about how a system advertised as protected against duplicate payments could generate thousands of them in under two hours — and why customers learning about missing or extra money were first told their payments were simply late.

MiniMax: MiniMax M3: # Software Update Triggers $2.8 Million in Duplicate Reimbursements at KiteBridge A routine software update from expense-management company KiteBridge instead triggered more than 13,000 duplicate payments totaling $2.8 million this week, disrupting payroll operations at 91 corporate customers and exposing a gap between the safeguards the company advertises and the way its systems behave under failure. KiteBridge builds software that helps employers approve and pay out reimbursements for travel, meals, and other business expenses. On Tuesday morning, the company pushed version 4.2 of its platform at 8:45 a.m. Less than thirty minutes later, at 9:12 a.m., the first customer complaint arrived. Within the next hour, engineers identified the cause: a payment-processor timeout had triggered KiteBridge's fallback system, which mistakenly treated each retry attempt as a brand-new reimbursement request. The result was 13,400 duplicate payments issued in just over an hour. By 10:03 a.m., roughly seventy-five minutes after the first complaint, KiteBridge suspended its automated payment pipeline. The scope of the damage, however, was not yet public. The duplication was especially troubling because it appeared to contradict safeguards the company had previously promoted. An April changelog announced that version 4.0 had introduced "exactly-once payment protection," a feature the company described as ensuring that retries would not produce duplicate disbursements. Tuesday's incident suggests that protection covered the primary payment path but did not extend to the fallback pathway used when the primary processor fails to respond in time. In one small consolation for individual workers, no money was withdrawn from employees' personal accounts. The duplicate reimbursements were funded instead by employers, who pre-fund the pools that KiteBridge draws from to pay out approved expenses. Still, companies were left to reconcile phantom payouts and, in many cases, chase reversals for funds that had already been disbursed into employee bank accounts. By Wednesday evening, KiteBridge said it had recovered $2.3 million of the $2.8 million total. The remaining $500,000 was still pending as the company worked through bank reversals and customer-by-customer reconciliations. The company's public communications drew additional criticism. Its incident page initially described the issue as delayed reimbursements and did not disclose the duplicate payments until roughly six hours after automated payments had been suspended. For several hours, customers checking the status page saw a description that understated both the scale and the nature of the failure. CEO Lena Ortiz apologized and pledged to publish a full technical report on what went wrong, though she gave no timeline for its release and did not say when automated payments would resume. For corporate finance teams, the episode underscores a recurring risk: a vendor's "exactly-once" guarantee is only as strong as the path taken when the normal path fails. KiteBridge's primary system appeared to honor that promise on Tuesday; its fallback did not. Until the company releases its technical report and demonstrates that the gap has been closed, customers are likely to read each new release note with fresh scrutiny.

Meta: Muse Spark Open: # Raincheck Raises $8 Million to Bring Flood-Risk Software to More Cities *The Austin startup maps drainage systems to help local governments prioritize repairs before storms, with plans to hire and expand into Florida* 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 municipal 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. The latest raise 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. More information about [how Raincheck’s forecasting model works](https://example.com/raincheck-methodology) is available on its website. Its forecasts are recommendations. Emergency managers retain control over deployments and public warnings. The company competes with 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.

MiniMax: MiniMax M3: # Raincheck Raises $8 Million to Improve Flood Preparedness *An Austin-based flood-risk software company helps cities prioritize drainage maintenance before storms and plans to expand into Florida.* Raincheck, an Austin-based company that makes flood-risk software for local governments, announced today that it has raised $8 million in seed funding. The company maps drainage systems and combines those maps with rainfall forecasts. Cities use the software to determine where to send maintenance crews before a storm. The funding 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: > “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. According to [Raincheck’s flood-risk methodology](https://example.com/raincheck-methodology), its forecasts are recommendations rather than required actions. Emergency managers retain control over deployments and public warnings. ## The competition 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.

Meta: Muse Spark Open: # Startup Spotlight: LedgerLark Small investment firms face the same post-trade complexity as large institutions, but without the same operations staff. LedgerLark is building AI agents to handle that cleanup work. The company is early, with a handful of paying customers and a narrow initial use case, and its progress will depend on whether small firms trust software to touch their books. ### An Insight From Operations LedgerLark was founded by Mina Batra and Owen Carr. Batra previously spent seven years in operations at a regional brokerage. Carr built reconciliation systems at a large bank. According to the company, Batra began exploring the idea after watching a six-person team spend three days resolving a trade mismatch caused by a single copied account number. The incident pointed to a broader pattern she had seen: small firms handling a high volume of manual checks across confirmations, custodian records, and internal ledgers, often in spreadsheets and email. The founders’ insight is that this work is constrained not by financial expertise but by time and tooling. Rather than replacing operations staff, they propose agents that do the first pass of comparison and research, leaving judgment and approval to humans. It is a pragmatic framing, and one that reflects Batra and Carr’s backgrounds on opposite sides of the market — a smaller brokerage and a large bank. ### What the Product Does LedgerLark builds AI agents for post-trade operations at small investment firms. The product is focused on reconciliation and exception management. In practice, the agents compare confirmations from counterparties, records from custodians, and a firm’s internal ledgers. When discrepancies appear — a mismatched quantity, a missing settlement instruction, an account number that does not align — the system proposes a correction for a human to review and approve. The company emphasizes that the product does not execute trades or move money. That scope matters. By staying out of execution and payments, LedgerLark avoids the highest-risk actions in the workflow. The approach is assistive by design: surface the mismatch, gather context, and suggest a fix, rather than closing the books autonomously. For potential buyers, the key question will be how the agents explain their proposals. In reconciliation, auditability is as important as accuracy. A suggestion that saves time but is hard to trace could add risk rather than remove it. ### Early Traction LedgerLark has five paying customers, all described as small investment firms. Annual contracts range from $36,000 to $110,000, suggesting deployment varies by size of the firm or number of workflows covered. On outcomes, the company says one customer reduced its unresolved daily exceptions from 70 to 18 after adopting the product. That figure has not been independently verified and reflects a single deployment. It also leaves open what kind of exceptions remain and how much human effort is still required to clear the rest. The company has raised a $3.5 million seed round led by Motive Row. For a startup selling into financial services, that amount provides runway to build integrations and complete security reviews, but not much room for a long enterprise sales cycle. The narrow focus may help here. Serving small firms often means simpler buying committees than at large banks, though diligence requirements can still be demanding. ### Competition and Open Questions LedgerLark is entering an established space. Competitors include long-standing reconciliation vendors and internal software built and maintained by banks. Many small firms also rely on manual processes and outsourced administrators, which are not traditional software competitors but are the status quo LedgerLark must displace. The company will need to show that its agents are more adaptable than rules-based tools without being opaque or brittle. AI agents in this context raise familiar concerns: handling messy data, avoiding false positives, and performing consistently as document formats change. Constructively, the human-in-the-loop design gives the company a way to address this — accuracy can improve with feedback on approvals — but it also means efficiency gains depend on continued oversight. Another uncertainty is distribution. Financial institutions may require lengthy security and procurement reviews, even at the smaller end of the market. That can stretch sales cycles, delay implementation, and make it harder to predict revenue. The startup will also have to integrate with custodians, order management systems, and other record-keepers that were not built to a common standard. If LedgerLark can navigate those constraints, the appeal is clear: fewer lingering breaks, less overtime around settlement, and more time for operations teams to focus on exceptions that truly need judgment. Whether five early contracts can become a repeatable go-to-market motion is the question to watch.

MiniMax: MiniMax M3: # LedgerLark Tackles the Messy Afterlife of a Trade When a trade goes wrong, the fix rarely happens on a trading floor. It happens days later, in back offices, when someone notices that the numbers don't line up. LedgerLark, an early-stage startup, is betting that small investment firms will pay AI agents to do more of that reconciliation work — and to do it before humans ever spot the problem. ## The founders' insight The company was started by Mina Batra and Owen Carr, two operators who know post-trade workflows from the inside. Batra spent seven years in operations at a regional brokerage, where, she says, she watched a six-person team spend three days untangling a single trade mismatch caused by a copied account number. Carr spent years building reconciliation systems at a large bank. Their shared conclusion: post-trade operations are full of small, expensive errors that software should be catching earlier. ## What the product does LedgerLark's agents compare trade confirmations, custodian records and a firm's internal ledgers, then propose corrections for a human to approve. The company is quick to emphasize what the system does not do: it doesn't execute trades, and it doesn't move money. That positioning matters in an industry where compliance teams tend to bristle at anything that looks like autonomous action in the trading chain. The target customer is a smaller investment firm — the kind that doesn't have engineering staff to build reconciliation tooling in-house but does have enough volume to feel the pain of mismatched records. Five firms are currently paying, according to the company, on annual contracts ranging from $36,000 to $110,000. ## Early traction, with caveats The most eye-catching number from LedgerLark so far is a customer-reported outcome: unresolved daily exceptions falling from 70 to 18. It's the kind of metric that grabs attention, but it is worth noting that it comes from a single customer, is self-reported, and has not been independently audited. Useful as a directional signal; less useful as proof of impact. The company has raised a $3.5 million seed round led by Motive Row, with participation from undisclosed angels. That is enough to build, but not enough to absorb the long sales cycles typical of financial services. Security reviews, vendor due-diligence questionnaires and procurement approvals at regulated institutions can stretch for months. For a five-customer startup, every stalled contract is a larger share of the runway than it would be for an established vendor. ## The competitive picture LedgerLark is not the first to pitch automation in this corner of finance. Established reconciliation vendors already serve parts of the market, and large banks run internal tooling that, while rarely sold externally, sets a baseline for what an in-house solution can do. What LedgerLark is offering, in effect, is a different shape of the same product: lighter-weight, agent-based, and aimed at firms that wouldn't buy from incumbents and wouldn't build from scratch. Whether that is durable differentiation, or a feature the incumbents can ship in a release cycle, is one of the open questions facing the company. ## What to watch A few things will determine whether LedgerLark graduates from early traction to a real business: whether its customer cohort expands beyond a handful of firms, whether the exception-reduction story holds up across more than one deployment, and how the company navigates the slow, paperwork-heavy sales process that defines its market. The founders' relevant experience gives them a credible starting point. The harder work — turning a useful tool into a category-defining one — is just beginning.

Meta: Muse Spark Open: # Cloudnote Left Debugging Page Exposing Prompt Excerpts Accessible for Hours *Company says unauthenticated page showed snippets from more than 1,800 sessions Monday; investigation into whether data was accessed is ongoing* Cloudnote exposed excerpts of customer prompts on an unauthenticated debugging page for four hours on Monday before taking it offline after being alerted by an outside researcher, the company said. Logs reviewed by the company show the page contained snippets from 1,842 user sessions during that window, each displaying up to 200 characters from a prompt. Cloudnote said it removed access to the page 19 minutes after the researcher's notification. That figure represents a small fraction of its 3.2 million registered users. Even truncated prompts can contain sensitive personal, business or creative material that users entrust to the notetaking service, raising privacy concerns. Full documents, passwords and payment information were stored elsewhere and were not present on the debugging page, the company said. What remains unclear is whether anyone other than the researcher found or used the exposed page while it was live. Cloudnote said it has so far found no evidence of access by other parties, but cautioned that its investigation is still underway and it has not yet reached a final determination. The company said it will notify affected users if that assessment changes.

MiniMax: MiniMax M3: # Cloudnote Says Debugging Page Briefly Exposed Excerpts From Nearly 1,900 User Sessions *The note-taking platform removed the page within 19 minutes of being alerted, but the full scope of the four-hour window remains under review.* Cloudnote, a note-taking service with 3.2 million registered users, disclosed Tuesday that an internal debugging page containing short excerpts of customer prompts was accessible without authentication for roughly four hours on Monday. The company said it removed the page within 19 minutes of being notified by an independent security researcher. The exposed page contained truncated excerpts from 1,842 user sessions, each limited to approximately 200 characters of input, according to Cloudnote. The excerpts were drawn from prompts users had submitted to the service. Cloudnote emphasized that full documents, account passwords and payment information are stored in separate systems and were not present on the page. The company said its logs show no evidence that anyone other than the reporting researcher accessed the page during the four-hour window. That assertion has not been independently verified, and Cloudnote acknowledged its investigation remains in early stages. The incident matters because prompts often carry sensitive context — meeting notes, draft correspondence, research queries or proprietary material — even within a 200-character limit. Cloudnote said it has begun notifying affected users and will share additional details as its review continues.

Meta: Muse Spark Open: C

MiniMax: MiniMax M3: C

Meta: Muse Spark Open: 6

MiniMax: MiniMax M3: 5

Meta: Muse Spark Open: # The Best Support Bot Wasn't the Most Expensive One The most important result from a new customer-support benchmark isn't who came in first. It's who tied for first — for less than half the price. In a blind test of five language models on real support tickets, the most expensive model, North, earned the highest average quality score. The runner-up, Vale, scored essentially the same — a difference so small it could have been noise — while costing 61% less to run and answering 18% faster. For companies weighing whether to put AI in front of customers, that tie reframes the buying question from "what's best?" to "what's best for the money?" It also punctures a common assumption in AI procurement: that quality tracks neatly with price. It doesn't, at least not here. ### How the test worked The setup was intentionally simple and human-centered. Researchers took 200 anonymized, real customer-support tickets from a software company and had each of five models generate a proposed answer for each one. Model names were hidden from the judges. Three experienced support managers graded every answer blindly. Their scores combined three things that actually matter on the floor: factual correctness (was it right?), usefulness (did it solve the problem?), and tone (would you send this to a customer?). There was no chatbot browsing the web, no agent clicking around to issue a refund or reset a password, no handoff to tools. Just a ticket in, a draft answer out — the core task many teams automate first. Costs were estimated per 1,000 tickets, giving a direct sense of operating expense at scale. This kind of evaluation can't tell you whether a model can *act* as a support agent. It can tell you whether it *writes* like a good one. For many help desks, that's the right place to start. ### A tie at the top changes the math North led the pack with an 84.2 average score. Vale was right behind at 83.8. The researchers found that gap was not statistically significant — in plain terms, you can't confidently say North is actually better. On a different sample of 200 tickets, Vale could easily come out ahead. What *was* clearly different was the bill. North came in at an estimated $1.90 per 1,000 tickets. Vale cost $0.74 for the same volume. Add in Vale's speed advantage — nearly a fifth faster on average — and the trade-off tilts further. Faster answers mean shorter queues and fewer customers timing out in chat. Dig a little deeper and each leader had a specialty. North was strongest on billing disputes, the kind of tickets where policy nuance and precision matter. Vale shone on technical troubleshooting, where step-by-step guidance has to be both accurate and easy to follow. Neither advantage was large enough to make the other model unusable in those categories, but for teams with a lopsided ticket queue — say, a SaaS company drowning in integration questions — that hint could help break a tie. Below that top tier, the story becomes one of diminishing returns. Cheaper, lighter models scored lower, but the price drops were steep. Ember, Kite, and Harbor all cost a fraction of the leaders, with scores in the 70s. For internal triage, ticket summarization, or drafting replies that a human always reviews, those trade-offs might be perfectly acceptable. For autonomous customer-facing replies, the benchmark suggests you'd feel the quality hit. The point isn't that "cheap is good enough." It's that "most expensive" is not the same as "most effective." Once you pass a certain capability threshold — which both North and Vale cleared here — other factors like latency, cost, and reliability begin to dominate. There's another subtle finding worth attention: the grading was done by humans, not by another AI. Automated judges are cheaper and increasingly common, but they tend to favor fluency over factuality and can be gamed by confident-sounding errors. Having three managers score for correctness, usefulness, and tone keeps the results grounded in what a support leader would actually approve. ### Where every model stumbled If Vale's value is the headline, the shared failure is the footnote every buyer should read. All five models performed poorly when a ticket depended on an image attachment — a screenshot of an error message, a photo of a broken invoice, a screen recording still. That's not an edge case. In real support operations, customers constantly send visual proof. The result is a reminder that text-only benchmarks flatter text-only models. A high score on written tickets doesn't mean the same model will cope when a customer writes "see attached" and the real clue is in pixels, not prose. The benchmark's other limits matter for the same reason. All tickets were in English, from a single software company. Researchers didn't test agentic actions, tool use, or multilingual support. In other words, this is a strong test of draft-quality writing for one kind of English-language help desk — not a full simulation of a global, omnichannel support stack. That doesn't invalidate the findings. It scopes them. If your tickets look like these tickets — English, text-heavy, from B2B software — the results are directly relevant. If you live in screenshots, non-English markets, or complex workflows where the model must look up an account and *do* something, you need more evidence before you buy. ### What this means if you're choosing For most teams, the practical takeaway is to stop shopping on leaderboard position alone. A 0.4-point difference on a 100-point scale, flagged as non-significant by the researchers themselves, should not drive a purchasing decision. Cost per thousand, response time, and fit to your ticket mix should. Start with the job to be done. If you want human-in-the-loop drafts, pilot both top performers on your own historical tickets and measure edit rate and handle time — not just a quality score. If you want full automation, test how models behave on your hardest categories, especially anything visual. And budget for the gap the benchmark revealed: none of these models, at any price, handled image-dependent tickets well. That suggests you'll still need either a vision-capable system or a human in the loop where screenshots are involved. Good support is not just about knowing the answer. It's about delivering it quickly, affordably, and in a tone customers trust. On this test, Vale did all three as well as the pricey favorite. That's the clearest signal in the data — and the one most likely to save you money without costing you quality.

MiniMax: MiniMax M3: # Vale Offers the Strongest Quality–Cost Balance in Support AI The clearest finding from this benchmark is not that one model produced the best support answers, but that **Model Vale nearly matched the highest-scoring model while answering faster and costing substantially less**. That makes Vale the strongest overall candidate in this test—not because it definitively outperformed North, but because it offered a more attractive balance of quality, speed, and operating cost. Model North achieved the highest average score, 84.2, while Vale scored 83.8. The 0.4-point difference was not statistically significant, so the results do not support claiming that North is more accurate or useful. More importantly, Vale cost an estimated $0.74 per 1,000 tickets, compared with North’s $1.90, and answered 18% faster. For a support operation handling a meaningful volume of tickets, those advantages could matter without requiring a meaningful sacrifice in answer quality. ## How the benchmark worked Researchers gave five language models the same set of 200 anonymized customer-support tickets. Each model generated one proposed answer per ticket. Three experienced support managers then graded the responses blindly, meaning they did not know which model had produced each answer. The scores combined three dimensions central to customer support: factual correctness, usefulness, and tone. This is more informative than evaluating only whether an answer contained the right facts. A response can be accurate yet confusing, incomplete, or inappropriate in tone. Blinding also reduces the risk that graders’ expectations about a particular model might influence their judgments. The aggregate results show a clear cost-quality gradient. North and Vale occupy the high-quality end, while the less expensive models trail them: Ember scored 79.1 at an estimated $0.31 per 1,000 tickets, Kite scored 76.4 at $0.22, and Harbor scored 71.0 at $0.08. These figures should not be interpreted as proof that the cheapest option is best—or that every point of score has identical business value. However, they show that buyers can make substantial cost reductions by moving below the two leading models. ## Different strengths may matter by ticket type The overall averages also hide meaningful specialization. North performed best on billing disputes, while Vale performed best on technical troubleshooting. That suggests a possible routing strategy: organizations could send billing questions to North and technical issues to Vale. Alternatively, they could use Vale as a general-purpose model and escalate billing cases when the extra quality is likely to justify the additional cost. That approach should be tested rather than assumed. The benchmark does not report category-specific sample sizes, score gaps, or whether differences within each category were statistically significant. A model’s small lead in one category may reflect noise rather than a dependable advantage. ## Image-dependent tickets remain a shared weakness Perhaps the most important operational finding is that **all five models performed poorly when a ticket depended on an image attachment**. This was not an isolated weakness limited to the cheapest or lowest-scoring model. It exposes a broad limitation in handling screenshots, diagnostic photos, proof-of-purchase images, and other visual evidence. A deployment should therefore include a clear escalation path for image-dependent cases. Automatically sending such tickets to an AI system could frustrate customers whose issue is not resolved correctly. Depending on the severity of those cases, human review may be safer than relying on the model’s proposed answer. ## What the test does not establish The findings apply to English-language tickets from one software company. They may not generalize to other industries, languages, customer expectations, or support workflows. The benchmark also did not test agentic actions, tool use, or multilingual support. It therefore says little about whether a model can look up an order, update an account, search a knowledge base, or collaborate across languages. Other practical questions remain unanswered, including integration costs, response-time distributions, infrastructure requirements, privacy controls, and the amount of human review each answer needs. The per-1,000-ticket figures are estimates, and the benchmark does not explain whether they include prompting, retries, or production serving costs. Finally, a nonsignificant difference between North and Vale is not proof that the models are exactly equivalent. It means this test did not provide enough evidence to establish a reliable quality gap. Confirming parity would require a larger evaluation designed specifically to detect small differences. ## Bottom line Vale emerges as the best-supported default from this benchmark: its quality was close to North’s, its estimated cost was much lower, and it responded faster. North remains a reasonable choice for billing disputes, while the cheaper models may fit organizations willing to accept lower scores. Across every option, image-dependent tickets require special care. The broader lesson is that model selection should begin with workload needs and total operating value—not the highest average score alone.

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.

MiniMax: MiniMax M3: # 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.

Meta: Muse Spark Open: # Arcwell Launches Relay to Cut Time Spent Hunting for Answers Arcwell has launched Relay, an artificial intelligence platform that scans customer support conversations and suggests answers to agents drawn from a company's own help documents and past transcripts. The move is Arcwell's bid to reduce one of support's persistent bottlenecks: agents toggling between chats and knowledge bases to find information their employer already holds. "Support agents spend too much time searching for information that their companies already have," Chief Executive Priya Shah said. Relay centers on three functions. It proposes draft responses during live conversations, flags customers who show signs they may cancel, and generates summaries after tickets are resolved. Instead of automating replies outright, Relay is built to keep agents in control, surfacing suggested language they can send, edit, or ignore as the conversation unfolds. Together, Arcwell is betting the features will translate into quicker responses and less churn. The focus on speed reflects a familiar pressure in support operations, where shaving seconds off each ticket can add up across thousands of daily interactions. The company tested Relay with 12 companies over six months. Agents using the tool closed tickets 14% faster, according to Arcwell, which did not release customer-retention results from the trial. Relay will cost $65 per agent each month. Adoption will require trade-offs on data access. To make suggestions, Relay needs permission to read support transcripts and internal help documentation. Arcwell is presenting its data controls as a counterweight to that access. The company says customer data is encrypted and is not used to train shared models, and that companies can choose whether that data is retained for 30 or 90 days. Those limits are meant to reassure buyers weighing efficiency against privacy and compliance risks. For now Relay is positioned strictly as an assistant rather than an autonomous agent. Whether those assurances are enough to drive wide adoption will depend on how much time it actually saves once deployed beyond the initial trial group.

MiniMax: MiniMax M3: # Arcwell Unveils Relay for AI-Assisted Customer Support Arcwell has announced Relay, an artificial-intelligence platform that analyzes customer-support conversations, suggests answers to agents, identifies customers who may cancel and creates summaries. The launch extends AI beyond simple response recommendations as customer-service companies continue to explore ways to improve the work of support agents. Relay was tested by 12 companies over six months, according to Arcwell. Agents using the platform closed tickets 14% faster, the company said. That is the only quantified result disclosed in the announcement, and it measures ticket-closing speed rather than customer retention. Arcwell did not release retention results, leaving prospective buyers without data on whether faster resolutions help customers remain with their providers. The platform costs $65 per agent each month. Relay requires access to support transcripts and internal help documents so it can review past interactions and find relevant company information. In addition to suggesting responses, it can identify customers who may cancel and generate summaries of support conversations. Together, those functions bring information retrieval, response recommendations and routine conversation analysis into a single platform. “Support agents spend too much time searching for information that their companies already have,” Arcwell CEO Priya Shah said. Her statement captures the central problem Relay is designed to address: support workers may have access to company knowledge but need faster ways to find and apply it. The six-month trial provides Arcwell with evidence of a productivity gain, although the company has not disclosed whether the tool improved retention. Relay’s dependence on company records also puts data governance at the center of the product. Arcwell said customer data is encrypted and is not used to train shared models. Companies can choose a 30-day or 90-day retention period, giving them two options for how long Relay keeps the information. For Arcwell, the launch represents a new chapter in its effort to help companies improve customer service using artificial intelligence. Relay enters the market with a defined monthly price and a reported improvement in ticket-closing speed, but key questions about its effect on customer retention remain unanswered.