Head to head: Google: Gemini 4 Carbon vs Kimi-K3.1-Pro

Google: Gemini 4 Carbon: C

Kimi-K3.1-Pro: C

Google: Gemini 4 Carbon: 6

Kimi-K3.1-Pro: 6

Google: Gemini 4 Carbon: # Arcwell Launches Relay AI to Speed Up Support Tickets and Flag Churn Arcwell has launched Relay, an artificial intelligence platform designed to help customer service teams close support tickets faster while alerting businesses to customers who may be preparing to cancel. Customer service departments have become a primary proving ground for enterprise AI, as software vendors pitch automation to trim response times and reduce repetitive clerical work. Arcwell’s entry into the market focuses on examining customer support conversations to assist human agents rather than replacing them outright. Priced at $65 per agent each month, Relay analyzes support exchanges to suggest answers to representatives, create conversation summaries, and identify customers who may cancel their accounts. The tool targets a persistent bottleneck in support operations, where staffers routinely stall while digging through internal files for troubleshooting steps or policy details. “Support agents spend too much time searching for information that their companies already have,” Arcwell CEO Priya Shah said. Relay’s commercial debut follows a six-month testing period involving 12 companies. According to Arcwell, support agents using the software during that trial closed tickets 14% faster. While Arcwell pitches the platform as a way to spot customers before they cancel, the company did not release any customer-retention results from the pilot, leaving open the question of how often those early warnings translate into saved accounts. Deploying the system requires granting Arcwell access to a substantial collection of internal records. To generate relevant replies and accurate summaries, Relay needs access to both customer support transcripts and internal help documents, a level of data sharing that routinely draws close scrutiny from corporate security and legal teams evaluating third-party AI tools. To address those privacy concerns, Arcwell says all customer data processed by Relay is encrypted. The company also says client records are not used to train shared AI models, a key safeguard for businesses wary of exposing proprietary materials to outside systems. Businesses adopting Relay can govern how long their information remains in the system, choosing between a 30-day or 90-day retention period.

Kimi-K3.1-Pro: # Arcwell launches Relay, an AI platform for customer support teams Arcwell has introduced Relay, an artificial intelligence platform that examines customer support conversations and suggests answers for agents. The software can also summarize interactions and identify customers who may be considering cancellation, combining ticket assistance with alerts aimed at customer retention. Those features address different stages of support work. Suggested responses and summaries focus on handling and documenting customer conversations, while cancellation warnings extend Relay’s scope beyond the ticket itself. Arcwell tested the platform with 12 companies over six months. Agents using Relay closed tickets 14% faster, according to the company. Arcwell did not release customer-retention results, so the trial figures do not show whether the cancellation warnings helped companies keep customers. Relay costs $65 per agent each month. CEO Priya Shah said the product is aimed at reducing the time agents spend looking for information their companies already possess. “Support agents spend too much time searching for information that their companies already have,” Shah said. Putting that idea into practice requires access to company material. Relay needs support transcripts and internal help documents, connecting the platform to records of customer interactions as well as the documentation agents use to assist them. That makes Arcwell’s data-handling policies central to the product’s pitch. 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, giving them two defined options for how long data is kept. Relay gives Arcwell a product spanning response suggestions, summaries and cancellation alerts, with an early performance claim centered on ticket speed. The six-month test provides a specific figure for that claim, but the lack of retention results leaves a key question unanswered. For prospective customers, the decision will involve weighing Arcwell’s reported efficiency gain against the access Relay requires and the controls available for the data it handles.

Google: Gemini 4 Carbon: # Milwaukee’s Foundry Hall Prepares to Lock Its Doors Since 2018, the hum of early-stage ambition has filled Foundry Hall—whiteboards crowded with revenue models, quiet debates over prototypes, and founders rehearsing pitches in glass-walled rooms. On October 31, the Milwaukee nonprofit startup accelerator will lock its doors for good. Foundry Hall is shutting down after losing its largest corporate sponsor, Blank River Bank, which supplied 38 percent of the organization’s annual budget. When the bank declined to renew its sponsorship, a spokesperson attributed the decision to a shift in community investment priorities rather than any shortcoming in the accelerator’s performance. Whatever the rationale, the withdrawal left an insurmountable gap in Foundry Hall’s ledger. Leadership spent months courting new backers, ultimately determining that while remaining funds could sustain operations through October, there was not enough money to carry the organization through another full program. “We kept waiting for the replacement check that never came,” said executive director Camille Ross. She is one of six employees who will lose their jobs when the shutdown takes effect. The closure has set off an urgent search for real estate. Twelve startups currently rely on Foundry Hall’s workspace and must pack up before the end of the month. To soften the blow, Deputy Mayor Susan Cole indicated that the city is actively talking with local universities and private building owners to secure new space for those companies, hoping to keep them anchored in Milwaukee even as their shared home disappears. For years, Foundry Hall offered fledgling businesses something harder to find than square footage: a sense of permanence for untested ideas. Since opening, the organization has supported 212 startups that now collectively employ 1,140 people. Among alumni like founder Dev Malik, the building’s legacy is measured in turning points rather than headcounts. Early on, the accelerator gave his venture an institutional foothold when little else felt established. “My first investor meeting happened in that building,” Malik said. “Before Foundry Hall, I was pitching from the back table of a coffee shop.” That progression—from borrowed café tables to sustained payrolls—defined the quiet promise of the space. While the companies it helped launch remain part of the city’s fabric, the absence of a central proving ground leaves a quieter, steeper climb for founders just starting out. Inside the building, daily work now unfolds alongside the packing. Tenants still take client calls down the hall from stacked cardboard boxes, and staff members spend their remaining weeks helping resident teams plan their exits. Rather than a sudden collapse, the final stretch resembles a measured dimming—years of shared momentum dismantled, desk by desk, before November arrives.

Kimi-K3.1-Pro: # Foundry Hall to Close After Losing Its Largest Sponsor Foundry Hall, a nonprofit startup accelerator in Milwaukee, will close Oct. 31, ending a program that since 2018 has supported 212 startups. Those companies collectively employ 1,140 people, a record that now stands alongside a more immediate count: six employees will lose their jobs, and the 12 startups currently using its workspace must leave. The decisive loss was Blank River Bank, its largest corporate sponsor. The bank supplied 38% of Foundry Hall’s annual budget and declined to renew its sponsorship. A bank spokesperson said the decision reflected a change in community investment priorities, not the accelerator’s performance. Executive director Camille Ross described the search for other support in stark terms. “We kept waiting for the replacement check that never came.” Foundry Hall has enough money to operate through October, but not enough to run another full program. That distinction shapes the final weeks: the lights can stay on through the end of the month, but the organization cannot responsibly promise founders another cycle. Its closing is therefore both gradual and abrupt, with a date on the calendar and little time for the companies still working inside. For founder Dev Malik, the building’s value was not abstract. “My first investor meeting happened in that building. Before Foundry Hall, I was pitching from the back table of a coffee shop.” The change he describes is difficult to capture in a budget line: a place to meet, to be taken seriously and to encounter other people attempting similarly uncertain work. The accelerator’s totals—212 startups and 1,140 jobs—suggest how often that opportunity was used, though they do not make its replacement any simpler. Deputy mayor Susan Cole said the city is speaking with building owners and local universities about space for the current companies. Those conversations could help the 12 startups find somewhere to go, but they do not restore the sponsor funding or preserve the six staff positions. For those companies, the immediate task is narrower than saving an institution. It is finding desks, meeting rooms and a workable address before October ends. For employees, it is finishing the work while preparing to leave it. Foundry Hall’s legacy will continue in businesses that have already outgrown their first pitches and in employees who may never see the building. But its last month will be measured in smaller acts: clearing space, making introductions and helping twelve companies pack up without mistaking the end of a program for the end of their work.

Google: Gemini 4 Carbon: # Startup Spotlight: LedgerLark Brings Guardrailed AI to Post-Trade Operations In financial services, minor clerical mistakes have a way of compounding into expensive operational stalls. For Mina Batra, the moment that brought this problem into focus came during her seven years working in operations at a regional brokerage. She watched a six-person team spend three full days tracking down and resolving a single trade mismatch—an error ultimately traced back to a copied account number. That episode prompted Batra to start exploring whether software could shoulder more of the investigative burden in the back office. The result is LedgerLark, a startup building AI agents for post-trade operations at small investment firms. Batra teamed up with co-founder Owen Carr, who previously built reconciliation systems at a large bank, to adapt institutional-grade workflows for smaller firms that lack large internal engineering teams. Post-trade operations occupy a quiet but critical corner of finance. Once a trade is executed, operations staff must verify that every detail aligns across multiple parties before settlement is finalized. LedgerLark’s agents read and compare trade confirmations, custodian records, and internal ledgers to spot discrepancies—such as mismatched identifiers, unexpected fee calculations, or conflicting settlement dates. Rather than simply flagging a break in a queue, the software investigates the underlying documents and proposes a specific correction for a human operator to review. Crucially, the founders have placed clear boundaries around what the software is permitted to do. LedgerLark’s agents do not execute trades or move money, and every proposed adjustment requires explicit human approval before a ledger is updated. In a heavily regulated environment where automated missteps carry real financial and compliance costs, treating AI as a preparatory analyst rather than an autonomous decision-maker is a deliberate design choice. That measured scope has helped LedgerLark secure its initial commercial foothold. Five firms are currently paying customers, with annual contracts ranging from $36,000 to $110,000. While the sample size is still small, the company says one customer reduced its unresolved daily exceptions from 70 to 18 after deploying the software. To expand its engineering team and support additional data integrations, LedgerLark recently raised a $3.5 million seed round led by Motive Row. Turning five early contracts into steady, repeatable growth will test the young company on several fronts. LedgerLark enters a category already served by established reconciliation vendors whose software is deeply embedded in daily financial workflows. It also faces competition from internal bank software—whether built by larger institutions or bundled to smaller managers through banking and custodial relationships—as well as the persistent industry habit of managing edge cases in spreadsheets. Convincing operations leads to adopt a new layer of tooling requires proving clear time savings without disrupting existing reporting pipelines. Go-to-market timing presents another practical constraint. Financial institutions, even smaller ones, routinely require lengthy security assessments, data-handling audits, and procurement reviews before granting a third-party vendor access to sensitive trade records. A product that generates enthusiasm in an initial demo can still spend months navigating compliance checklists, stretching out sales cycles and testing a seed-stage startup’s patience and runway. Finally, there is an ongoing product challenge. Financial documents and custodian feeds vary widely in format and quality, and back-office teams have little tolerance for software that generates noisy false positives or misdiagnoses complex breaks. LedgerLark’s trajectory over the next year will likely depend on how reliably its agents handle messy, real-world exceptions across a wider variety of firms—and whether keeping humans firmly in the approval seat strikes the right balance between operational speed and institutional trust.

Kimi-K3.1-Pro: # Startup Spotlight: LedgerLark’s cautious case for AI in post-trade operations *The startup wants software agents to handle the investigative work around trade mismatches, while leaving decisions—and money movement—with people.* LedgerLark is building AI agents for post-trade operations at small investment firms, a part of the business where an error can be both mundane and time-consuming to untangle. Its approach is narrower than autonomous finance: examine records, identify discrepancies and suggest a fix. A person still decides what happens next. ## An operational problem, not a trading thesis Co-founder Mina Batra spent seven years in operations at a regional brokerage. She began exploring LedgerLark after watching a six-person team spend three days resolving a trade mismatch traced to a copied account number. The episode suggested that the scarce resource was not simply another system of record, but the time required to reconcile conflicting records and reconstruct what had happened. Her co-founder, Owen Carr, previously built reconciliation systems at a large bank. That experience is relevant because LedgerLark is entering a field with established workflows and software, rather than inventing reconciliation itself. The founders’ insight is that an agent may be useful between the alert and the correction: gathering evidence, comparing records and preparing a recommendation for review. ## What the agents do LedgerLark’s agents compare confirmations, custodian records and internal ledgers, then propose corrections for human approval. The product does not execute trades or move money. Those limits keep the product focused on investigation and make its output easier to understand as a recommendation, though they do not remove the need to test accuracy, permissions and audit trails. The design also sets a practical test for the company. A useful agent must do more than produce a plausible explanation; it needs to surface the right evidence, avoid confident but incorrect corrections and fit the controls already used by an operations team. For customers, the value would be less about novelty than whether staff can clear routine exceptions faster without creating new review work. ## Early customers, limited evidence Five firms are paying customers, with annual contracts ranging from $36,000 to $110,000. That is evidence that some firms will pay for the approach, but it is too early to treat the contract range as proof of repeatable demand. The spread also leaves open how pricing varies with firm size, transaction volume or deployment complexity. LedgerLark says one customer reduced its unresolved daily exceptions from 70 to 18. If sustained, that would be a substantial operational improvement. It remains a company-reported result from a single customer, however, and does not establish how typical the outcome is, how long it took or what else changed in the customer’s process. Those are sensible questions for prospective buyers to ask, rather than reasons to dismiss the result. The company has raised a $3.5 million seed round led by Motive Row. The funding provides capital for further development, but customer retention and independently verifiable results will say more about its progress than the round itself. ## A crowded route to adoption Competition comes from established reconciliation vendors and internal bank software. Some prospects may prefer to extend tools they already own, especially if those tools are embedded in reporting and compliance processes. LedgerLark will need to show that its agents add measurable value without becoming another difficult system to maintain. Financial institutions may also require lengthy security and procurement reviews, which can slow sales even when an operations team is interested. For a startup selling to small firms, long implementation cycles could be as important as model performance. LedgerLark’s early traction makes it worth examining, not because its approach is assured, but because its scope is concrete. The next useful evidence would be results across several customers: exception backlogs, correction accuracy, review time and the effort required to keep the system working. Those measures would clarify whether human-supervised agents can become dependable operational software.

Google: Gemini 4 Carbon: # HelioDock Launches Remote Robot Software Alongside $24 Million Series B *The autonomous warehouse robotics maker will make its Night Shift fleet-monitoring platform generally available on September 15 following a 14-month private beta across nine logistics operators.* HelioDock, a maker of autonomous warehouse robots, launched a remote fleet-monitoring software platform called Night Shift on Thursday alongside $24 million in Series B funding. Led by North Coast Ventures, the financing round brings HelioDock’s total capital raised to $39 million as the robotics manufacturer expands into recurring monthly software subscriptions for warehouse operators. Night Shift allows operators to monitor fleets of autonomous warehouse robots from afar and step in via remote control when machines encounter problems on the floor. By pairing its warehouse robots with paid fleet-management software, HelioDock aims to cut down on operational interruptions caused by stalled equipment, entering a software category where competing fleet-management products are available from Relay Robotics and YardOS. HelioDock pitched the platform around reducing the hands

Kimi-K3.1-Pro: # HelioDock launches Night Shift for remote warehouse robot oversight *After a 14-month beta spanning nine logistics companies and 310 robots, HelioDock is pairing Night Shift’s commercial launch with $24 million in Series B funding.* HelioDock on Thursday launched Night Shift, software that lets operators remotely monitor fleets of autonomous warehouse robots, and announced $24 million in Series B funding as it moves the product toward general availability on September 15. The launch gives HelioDock a per-robot subscription product in a market that already includes fleet-management software from Relay Robotics and YardOS. The company is citing fewer unplanned stoppages among beta customers as evidence of Night Shift’s value, but that result has not been independently verified. One beta customer also said the system’s usefulness depended heavily on the quality of a facility’s connectivity. Night Shift will cost $90 per robot per month. For an operator running 100 robots, that amounts to $9,000 a month at the listed rate. The pricing model ties the software bill directly to fleet size, allowing operators to calculate the subscription cost as deployments expand or contract. HelioDock CEO Mara Velez framed the product around the gap between an automated facility and the people responsible for keeping it running. “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. The remote-monitoring pitch follows a private beta that ran for 14 months and involved nine logistics companies and 310 robots. HelioDock said those beta customers experienced 17% fewer unplanned robot stoppages. The company supplied that figure, and it has not been independently verified. The beta establishes the scale and duration of HelioDock’s testing, but the stoppage claim does not provide independent confirmation of how Night Shift will perform across other facilities and fleets. 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. His account identifies connectivity as a practical condition on the software’s usefulness, rather than treating remote access as uniform across every site. That limitation also shapes what happens when a connection is lost. The robots return to their locally configured behavior, and remote control becomes unavailable. The fallback provides a defined response to an outage, but it does not preserve remote control. Night Shift’s remote capabilities therefore sit alongside, rather than replace, the behavior configured in the robots themselves. Velez’s middle-of-the-night example and Pike’s outage experience address different parts of the same operational problem. The first concerns avoiding a trip to a warehouse when a machine needs attention; the second concerns the conditions under which remote tools remain useful. HelioDock’s claimed reduction in stoppages addresses the frequency of disruptions, while the customer account describes circumstances that can restrict the response. Night Shift will enter a field where Relay Robotics and YardOS already offer competing fleet-management products. HelioDock can point to more than a year of private testing and participation by multiple logistics companies, but the available evidence does not establish how Night Shift compares with either rival. Its claimed 17% reduction in unplanned stoppages is a company-reported beta result, not an independent benchmark. North Coast Ventures led the $24 million Series B round. Announced alongside Night Shift, the financing supports HelioDock as it expands beyond robot manufacturing into subscription fleet-monitoring software. The round brings the company’s total funding to $39 million.

Google: Gemini 4 Carbon: # Software Glitch Triggers $2.8 Million in Duplicate Payouts at Expense Firm KiteBridge Ninety-one corporate customers unexpectedly funded $2.8 million in duplicate employee reimbursements this week after a software update at expense-management provider KiteBridge went awry, unleashing thousands of erroneous payouts in just over an hour on Tuesday morning. Before engineers halted the system, the software generated 13,400 duplicate reimbursements. Affected employers absorbed the immediate financial hit because corporate accounts funded the extra payments. KiteBridge confirmed, however, that no employees had money withdrawn from their accounts as a result of the error. By Wednesday evening, $2.3 million of the overpayments had been recovered, while the rest of the misdirected money remained pending. The malfunction undercuts a prominent reliability feature KiteBridge had touted earlier this year. Because expense-management platforms automate payouts from company coffers to workers submitting business costs, preventing double payments is a foundational requirement. According to an April changelog for version 4.0 of its software, KiteBridge had introduced “exactly-once payment protection”—a safeguard designed to ensure an approved reimbursement is paid a single time, even if network errors interrupt processing. That safeguard failed to hold on Tuesday morning after KiteBridge deployed version 4.2 of the platform at 8:45 a.m. Following the rollout, the system encountered a timeout from a payment processor. In digital banking, a timeout occurs when an external payment network takes too long to respond, leaving the sending software uncertain about whether a transfer succeeded or stalled. When payment systems work as intended, they respond to timeouts cautiously, verifying the status of an ambiguous transaction before attempting to resend money. During Tuesday’s disruption, however, KiteBridge’s fallback system mishandled those automatic retries. Rather than recognizing the retries as follow-up attempts on existing claims, the backup software treated each retry as an entirely new reimbursement request, repeatedly dispatching fresh payouts for the same expenses. Signs of the breakdown emerged less than half an hour after the update went live, with the first customer complaint arriving at 9:12 a.m. As duplicate transactions accumulated across client accounts, KiteBridge moved to cut off the automated flow, suspending all automated payments at 10:03 a.m.—roughly 50 minutes after the first warning sign and just over an hour after version 4.2 was deployed. Although engineers stopped the runaway payouts mid-morning, KiteBridge’s public communications lagged behind the reality of the incident for much of the day. The company’s public incident page initially described the problem simply as “delayed reimbursements,” the opposite of the overpayments hitting dozens of corporate clients. That initial notice left affected businesses without an accurate public explanation throughout the afternoon; KiteBridge did not update the incident page to disclose the duplicate payments until six hours after automated processing had been suspended. In the aftermath of the rollout failure, KiteBridge CEO Lena Ortiz apologized for the disruption and pledged a full accounting of how the safeguard failed. Ortiz said the company will publish a detailed technical report examining the breakdown as work continues to recover the remaining pending funds.

Kimi-K3.1-Pro: # KiteBridge Failure Generates $2.8 Million in Duplicate Reimbursements KiteBridge’s expense-management software generated 13,400 duplicate reimbursements totaling $2.8 million Tuesday, affecting 91 corporate customers and prompting the company to suspend automated payments. By Wednesday evening, $2.3 million had been recovered, leaving roughly $500,000 still pending. Employers funded the duplicate payments; no employees had money withdrawn from their own accounts. The incident began after KiteBridge deployed version 4.2 at 8:45 a.m. Tuesday. The first customer complaint arrived at 9:12 a.m., and the company halted automated payments at 10:03 a.m. Those timestamps place the failure shortly after the rollout, but KiteBridge has not said whether changes in version 4.2 themselves caused the problem. The immediate trigger was a payment-processor timeout. A payment processor is the outside service that helps execute a transaction; a timeout occurs when that service does not respond within the time the software allows. Because a timeout can leave the status of a transaction uncertain, payment systems may retry rather than immediately assume that nothing happened. KiteBridge’s fallback system mishandled that uncertainty. Instead of recognizing the retries as repeated attempts to complete existing reimbursements, it treated them as new reimbursement requests. The software consequently issued thousands of duplicates before automated payments were suspended. The incident contrasts with a safeguard KiteBridge described earlier this year. An April changelog said version 4.0 had introduced “exactly-once payment protection.” In payment software, “exactly once” generally refers to controls designed to ensure that the same transaction is processed only one time, even if a request is sent again or a system must recover from an error. Tuesday’s outcome showed that the protection did not prevent duplicates in this failure, but the company has not yet provided a technical explanation for why it broke down. KiteBridge’s public communications also changed as the incident developed. Its incident page initially characterized the problem as delayed reimbursements. The page did not disclose the duplicate payments until six hours after automated payments had been suspended. Recovery efforts continued into Wednesday. Of the $2.8 million sent in duplicate reimbursements, $2.3 million had been recovered by Wednesday evening, while the remainder was still pending. The distinction over who funded the payments is significant for affected workers: although the reimbursements flowed through an employee-expense system, employers supplied the money, and employees did not have funds pulled from their accounts. CEO Lena Ortiz apologized for the incident and said KiteBridge would publish a technical report. The report is expected to provide further detail on a failure that turned routine payment retries into millions of dollars in duplicate transactions and left corporate customers waiting for the remaining funds to be recovered.

Google: Gemini 4 Carbon: # 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.

Kimi-K3.1-Pro: # 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.

Google: Gemini 4 Carbon: # Cloudnote Exposes Customer Prompt Snippets in Brief Security Lapse *An unauthenticated debugging page held excerpts from 1,842 sessions on Monday, though full documents, passwords and payment data remained separate.* Cloudnote discovered that a debugging page containing excerpts of customer prompts was accessible without authentication for four hours on Monday, briefly exposing user inputs on a platform with 3.2 million registered users. Company logs show the page included snippets from 1,842 user sessions, with each excerpt containing up to 200 characters. Full documents, passwords and payment information were stored elsewhere and were not present on the page, confining the exposure to partial text from a tiny fraction of Cloudnote’s overall user base. Still, even limited prompt leaks test customer trust, as users routinely type proprietary workplace details, private notes or sensitive questions into prompt fields expecting strict confidentiality. While 200 characters amounts to roughly two short sentences, a brief fragment can be enough to reveal sensitive context without exposing an underlying file. Cloudnote removed the page 19 minutes after a researcher notified the company of the exposure. For now, Cloudnote says it has found no evidence that anyone besides that researcher accessed the page. Because the company has not yet completed its investigation, however, that finding remains preliminary, leaving open whether any unauthorized third parties viewed the excerpts while the page stood unprotected.

Kimi-K3.1-Pro: # Cloudnote Debugging Page Exposed Customer Prompt Excerpts for Four Hours *Logs show excerpts from 1,842 user sessions. Cloudnote says it found no evidence of access beyond the researcher who reported the problem, but its investigation is incomplete.* Cloudnote discovered that a debugging page containing customer prompt excerpts was accessible without authentication for four hours on Monday. Logs show excerpts from 1,842 user sessions; each could include up to 200 characters from a prompt. The exposure made the text available to anyone who reached the page. The session count does not establish how many of Cloudnote’s 3.2 million registered users were affected, and the excerpts’ contents remain unclear. The incident matters because troubleshooting tools can create privacy risks when access controls fail, even if they expose only small fragments of customer text. Cloudnote removed the page 19 minutes after a researcher notified it. The company says it has found no evidence that anyone besides the researcher accessed the page. That is not proof that no one else did so; the investigation is unfinished, and whether others viewed or copied excerpts remains unknown. Cloudnote said full documents, passwords and payment information were stored elsewhere and were not present on the page. Those facts narrow the data involved, but key questions remain about what the excerpts contained, how many people they represent and why the page lacked authentication.

Google: Gemini 4 Carbon: # Top-Tier AI Customer Support Doesn’t Require Top-Tier Spending Companies shopping for artificial intelligence to handle customer support often assume they must pay flagship prices for flagship quality. A new benchmark of five language models challenges that assumption, revealing that the smartest buy isn’t the most expensive one. In a head-to-head evaluation of customer-service drafting, a mid-priced contender named Model Vale matched the overall quality of the priciest option, Model North, while operating 18% faster and costing less than half as much. For support teams looking to scale, the clearest takeaway is that top-shelf performance is available at a steep discount—provided buyers understand where all current models still stumble. ### How the Models Were Tested To move beyond synthetic trivia and test real-world readiness, researchers gathered 200 anonymized, English-language support tickets from a single software company. Each of the five participating models was tasked with generating a single proposed reply for every ticket. Rather than relying on automated grading—where one AI judges another—the benchmark used human domain experts. Three experienced customer-support managers evaluated the responses blindly, with no knowledge of which model wrote which reply. They graded every answer on a combined rubric of factual correctness, practical usefulness, and appropriate tone, resulting in an average score on a 100-point scale. ### A Statistical Tie at the Top On paper, Model North posted the highest number at 84.2, edging out Model Vale’s 83.8. In practice, researchers found that this 0.4-point gap was not statistically significant. To the veteran managers grading the queue, the two models were effectively indistinguishable in overall quality. Their operational profiles, however, were starkly different. North carries an estimated cost of $1.90 per 1,000 tickets. Vale delivers equivalent performance for $0.74 per 1,000 tickets—a 61% cost reduction—while generating answers 18% faster. In live support environments where latency frustrates customers and volume drives up overhead, Vale’s combination of speed and efficiency makes it the standout default choice. Still, digging beneath the headline averages reveals a compelling case for specialized routing rather than a winner-take-all deployment. While the two leaders tied overall, they excelled on different types of problems. Vale led the pack on technical troubleshooting, making it well-suited for Tier-1 product queues. North, meanwhile, performed best on billing disputes, where nuanced phrasing and policy precision may justify paying a premium on a smaller, sensitive subset of tickets. ### What You Lose in the Budget Tier Below the two leaders, the benchmark illustrates a clear trade-off between cost savings and answer quality. Stepping down to the middle of the pack drops costs into fractions of a dollar per thousand tickets, but human graders noticed the decline. Model Ember landed in a respectable middle ground, scoring 79.1 at an estimated $0.31 per 1,000 tickets. Further down the price curve, quality eroded more noticeably: Model Kite scored 76.4 at $0.22 per 1,000 tickets, while ultra-cheap Model Harbor trailed the field with a 71.0 average at just $0.08. While these budget models might suffice for internal tagging or rough first drafts, the gap between a 71 and an 84 represents a tangible burden on human agents who must rewrite flawed or off-tone replies before hitting send. ### Shared Blind Spots and Real-World Limits Even the highest-scoring models shared a glaring weakness: visual context. Whenever a customer’s ticket depended on an image attachment—such as a screenshot of an error message or a broken user interface—all five models performed poorly. Support teams relying on any of these systems will still need clear triage rules to route image-heavy tickets straight to human staff. Buyers should also weigh the boundaries of the study before overhauling their workflows. Because the dataset came exclusively from one software company’s English-language queue, the results may not translate cleanly to retail, healthcare, or multilingual support desks. More importantly, the benchmark only measured a model’s ability to draft a single standalone text response. Researchers did not test multi-turn conversations, tool use, or "agentic" actions—such as looking up an account status in a database, issuing a refund, or resetting a password. Writing a polite, accurate paragraph is only half the job of modern customer service. Even with those caveats, the benchmark offers a practical roadmap. Support leaders don’t need to default to the most expensive model on the market to get human-approved answers—and by pairing a fast, high-value model like Vale with smart routing for billing issues and screenshots, they can capture peak quality without overpaying.

Kimi-K3.1-Pro: # The Top Score Wasn’t the Strongest Buying Signal The clearest finding from this customer-support benchmark is not that Model North earned the highest average score. It is that **Model Vale delivered statistically indistinguishable quality while responding faster and costing substantially less**. North averaged 84.2, compared with 83.8 for Vale. That 0.4-point gap was not statistically significant, so the results do not establish that North produces better answers. Vale, meanwhile, answered 18% faster. Its estimated cost was $0.74 per 1,000 tickets, versus $1.90 for North—a reduction of roughly 61%. For teams whose workloads resemble the test, that makes Vale the strongest default candidate. It does not make Vale universally superior, and it does not justify treating North’s nominally higher score as a meaningful victory. ## What the benchmark measured Researchers evaluated five language models on 200 anonymized customer-support tickets. Each model produced one proposed answer per ticket, and three experienced support managers graded the answers blindly. The scores combined factual correctness, usefulness and tone—an appropriate attempt to measure answer quality rather than simply whether a response matched a predetermined script. The design also defines the scope of the conclusions. This was a test of drafted responses, not of complete ticket resolution. The composite averages do not show whether points were lost through a few severe factual errors or through more numerous problems with usefulness and tone. Nor does one answer per ticket demonstrate how consistent a model would be across repeated runs. The costs are estimates, not a complete assessment of operating expense. Integration, human review and other production requirements could change the economics. ## Category strengths complicate a simple ranking North performed best on billing disputes, while Vale performed best on technical troubleshooting. Those results matter because an overall average can obscure meaningful differences across a support queue. They also argue against selecting a model solely from its headline score. A billing-heavy operation may reasonably investigate North further, while a technical-support team has an additional reason to start with Vale. A mixed or routed deployment could be worth exploring, but the benchmark does not establish whether the added complexity would pay off. Category sample sizes and the significance of those category-level differences are not reported. Below the leaders, the results show a broader price-quality trade-off. Ember averaged 79.1 at an estimated $0.31 per 1,000 tickets, while Harbor reached 71.0 at $0.08; Kite fell between them. These options could merit consideration where lower cost outweighs the measured quality difference, but the averages alone do not define an acceptable deployment threshold. The smaller score gaps should not automatically be treated as decisive either. ## Every model shared an important weakness The most consequential operational warning is that **all five models performed poorly when a ticket depended on an image attachment**. Support requests involving screenshots, visible error states or other image-based evidence therefore remain a problem for the entire field tested. That shared weakness may matter more than a fractional difference at the top. A model with a strong overall average could still be unsuitable if image-dependent tickets are common or carry high risk. The benchmark does not explain why the models struggled, but it clearly shows that buyers should test this workflow separately rather than assuming strong text-ticket performance will carry over. ## A useful shortlist, not a universal verdict The evaluation used English-language tickets from one software company. Its findings may not transfer to other products, customer expectations, writing styles or support policies. Researchers also did not test agentic actions, tool use or multilingual support. Consequently, the results say little about which model could autonomously resolve tickets, work with account systems or serve a global customer base. The practical next step is a pilot using an organization’s own ticket mix, including billing, troubleshooting and image-dependent cases. That pilot should measure correctness and consequential failures alongside speed and estimated cost. For this benchmark, however, the decision is clearer than the raw ranking suggests: North’s top score is not a demonstrated quality advantage. **Vale’s comparable measured performance, faster answers and much lower estimated cost make it the most compelling starting point—provided its category fit and the shared image limitation are addressed.**

Google: Gemini 4 Carbon: # Raincheck Raises $8 Million for Municipal Flood-Risk Software *The Austin-based company helps local governments decide where to dispatch maintenance crews before storms hit.* Raincheck, an Austin-based maker of flood-risk software for local governments, announced today that it has raised an $8 million seed round. The company [maps drainage systems and combines those maps with rainfall forecasts](https://example.com/raincheck-methodology), enabling cities to decide where to send maintenance crews before a storm. Its forecasts serve as recommendations, and emergency managers retain control over deployments and public warnings. Describing the challenge facing local officials, Raincheck 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.” Basin Ventures led the seed round, with participation from existing investor Long Mile, bringing Raincheck’s total funding to $10.5 million. Raincheck says the new funding will help it hire eight engineers and expand into Florida. The company currently works with seven cities in Texas and Louisiana, though it declined to identify them because some contracts have not been publicly announced. Other flood-planning vendors in the market include HydroMap as well as municipal engineering contractors.

Kimi-K3.1-Pro: # Raincheck Raises $8 Million to Help Cities Manage Flood Risk *The Austin company’s seed round brings its total funding to $10.5 million as it plans to hire engineers and expand into Florida.* Raincheck, an Austin-based company that makes flood-risk software for local governments, announced an $8 million seed round today. Basin Ventures led the round, with participation from existing investor Long Mile. Raincheck has raised $10.5 million in total. The company maps drainage systems and combines those maps with rainfall forecasts. Cities use its software to decide where to send maintenance crews before a storm. Raincheck provides additional detail about its approach in its [flood-risk methodology](https://example.com/raincheck-methodology). 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. ## 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.

Google: Gemini 4 Carbon: # QueueZero Unveils AI Meeting Assistant in Unscripted Launch Demo Enterprise software maker QueueZero launched an artificial intelligence meeting assistant called Proxy on Wednesday, introducing a workplace tool built to attend meetings, produce structured notes, and assign follow-up work directly to software agents and team members. While a growing wave of corporate software records audio and summarizes discussions, QueueZero is positioning Proxy to manage the execution phase that follows a business conversation. During Wednesday’s launch demonstration, however, the new platform underscored the practical challenge automated systems still face when interpreting unscripted human speech during live meetings. As QueueZero Chief Executive Evan Wu presented the software during the event, a customer in the audience said, “Someone should tell Evan to stop talking and send us the pricing sheet.” Proxy, which was actively monitoring the session, interpreted the audience remark as a direct instruction. Moments later, it created two tasks assigned to Wu: “Stop talking” and “Send pricing sheet.” Wu laughed, completed the second task, and deleted the first. While pricing distribution proved ready for automation, executive brevity remained subject to manual override. The exchange highlighted how the platform separates internal organization from outward-facing actions. According to QueueZero, internal tasks can be created automatically, allowing teams to capture action items and route follow-up work to software agents without manual data entry. By contrast, the company says Proxy requires confirmation before an external message is sent, a safeguard meant to prevent stray remarks from triggering unauthorized communications outside an organization. For businesses that prefer stricter oversight over internal workflows, QueueZero said system administrators can disable automatic task creation altogether. Doing so allows organizations to use Proxy to attend scheduled sessions and compile meeting notes without permitting the software to populate internal task lists on its own. The demonstration illustrates a broader operational challenge across the enterprise technology sector as vendors move beyond passive transcription toward agentic tools capable of taking action. As AI assistants gain the authority to initiate workflows across corporate systems, distinguishing between genuine instructions and passing conversational remarks has become a central question of IT governance. Proxy enters public beta next week and will cost $30 per user per month. With the release, QueueZero steps into a crowded enterprise productivity market where both specialized AI developers and major cloud providers are competing to turn routine meeting discussions into automated follow-up work.

Kimi-K3.1-Pro: # QueueZero’s Proxy Turns a Launch-Demo Aside Into Tasks for Its CEO **The AI meeting assistant can take notes and delegate follow-up work to software agents. Its launch demonstration also showed why task-creation controls matter.** QueueZero on Wednesday launched Proxy, an AI meeting assistant designed to attend meetings, produce notes and assign follow-up work to software agents. During the launch demonstration, however, the product offered an unscripted example of how easily an audience comment can become an item on a chief executive’s to-do list. A customer in the audience said, “Someone should tell Evan to stop talking and send us the pricing sheet.” Proxy treated the remark as an instruction rather than an aside. 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. For once, a live demo produced action items before the closing slide. The incident was brief, but it highlighted a central design question for meeting software that can do more than transcribe: which spoken remarks should result in action, and who should approve that action? A system listening for commitments, requests and decisions must also contend with jokes, audience commentary and statements that were never intended as commands. QueueZero said Proxy requires confirmation before sending an external message. Internal tasks, by contrast, can be created automatically. That means a user does not necessarily have to approve each task before it appears in a workflow, even though Proxy cannot simply send an outside communication on its own. Administrators can disable automatic task creation, giving organizations a way to limit the feature while retaining Proxy’s meeting-attendance and note-taking capabilities. The controls are likely to be important for companies testing how much autonomy to give AI assistants in everyday work. Automatic task creation can reduce manual follow-up, but it can also turn ambiguous language into assignments that someone must review, reassign or remove. Proxy is priced at $30 per user per month and is scheduled to enter public beta next week. QueueZero is positioning it as an assistant that connects meeting discussion to execution: instead of ending with a summary alone, a meeting can generate notes and route follow-up work to software agents. The demonstration did not show Proxy sending a message without permission, and the tasks it created remained internal. It did, however, provide a concrete preview of the review work that can accompany automation. As QueueZero opens the beta, prospective customers will be able to assess not only the quality of Proxy’s notes, but also how reliably it distinguishes actionable requests from everything else said within earshot.