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Stefan Kalb launched Seattle startup Super Labs in September 2025 with an $8 million funding round led by FUSE. The company initially aimed to help nontechnical, mid-market businesses identify useful AI applications, select vendors, and integrate those tools into everyday operations. By August 2026, Super Labs had rebranded as Latch and narrowed its focus to capturing how businesses actually work and supplying that context to AI agents.
That evolution matters to business owners and potential customers: the company moved from an AI marketplace and implementation model toward workflow documentation and infrastructure for automation.
What Super Labs was built to do
Super Labs was designed around a problem common among traditional businesses: leaders may know that AI could reduce manual work, but lack the technical staff to determine which tools are credible, how they fit existing systems, and whether the investment will produce measurable value.
Rather than building every AI application itself, Super Labs positioned itself as a combination of an AI marketplace and implementation partner. A customer could describe an operational bottleneck in plain English—for example, manually tracking project hours across several spreadsheets. Super Labs would then:
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- Map the company’s existing workflow.
- Identify steps where AI or automation might help.
- Match the business with an appropriate AI vendor.
- Manage integration work and deployment.
- Help deliver a focused application or automation rather than requiring a large technology overhaul.
The marketplace also offered AI developers a potential distribution channel. Developers could make their products available to Super Labs customers and, according to the launch coverage, potentially sell access through usage-based models.
Kalb’s original launch statement emphasized starting with focused AI “micro-applications” and demonstrating return on investment incrementally. That approach was intended to reduce the cost and disruption of adopting AI for companies that could not afford a large internal experimentation program.
GeekWire reported that Super Labs initially targeted manufacturing, e-commerce, distribution, and retail—industries where work often moves through multiple systems, spreadsheets, handoffs, and exceptions.
Why focus on mid-market companies?
Large enterprises can usually fund dedicated engineering, data, security, procurement, and AI teams. They can also hire systems integrators or build custom tools. Many mid-market businesses have valuable operational data and repetitive processes but lack those resources.
Super Labs’ thesis was that this gap leaves many companies stuck between recognizing an opportunity and putting a reliable system into production. Kalb argued that mid-market businesses risk falling behind if larger competitors use AI to improve productivity while smaller organizations remain dependent on manual processes. That is a strategic argument from Kalb and the company—not an independently established economic forecast.
For a business owner, the practical question is not whether AI is fashionable. It is whether the company has a specific, costly workflow that is sufficiently repetitive, measurable, and safe to improve.
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Who is Stefan Kalb?
Kalb is a Seattle entrepreneur best known for founding Shelf Engine. Before Shelf Engine, he founded Molly’s, a food business that supplied salads and sandwiches to Seattle-area cafés and hospitals.
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That background shaped the Super Labs idea. Kalb had firsthand experience with operational businesses where processes can be complicated, costly, and dependent on employee knowledge that is not neatly stored in software. It also informed his stated intention to scale the new company more deliberately after the challenges at Shelf Engine.
Founders and $8 million funding round
Super Labs was co-founded by:
- Stefan Kalb, chief executive and former Shelf Engine founder.
- Jared Kofron, formerly a principal software engineer at Pioneer Square Labs, with previous work at Flux, Rover, and Glowforge.
The company announced $8 million in funding, led by Seattle venture firm FUSE. Reported participants included Soma Capital, Liquid 2 Ventures, Pioneer Fund, Massive Tech Ventures, Garry Tan, Immad Akhund, Gokul Rajaram, Bede Jordan, and other investors.
The round label is inconsistent in public descriptions. GeekWire called it a seed round, while FUSE described it as an $8 million pre-seed round. The safest description is therefore an $8 million round led by FUSE, without treating the seed-versus-pre-seed label as settled.
For readers evaluating the business financially, the funding amount shows investor backing at launch but does not establish revenue, customer retention, profitability, or product-market fit. The available launch coverage did not provide independently verified customer counts, revenue, or return-on-investment results.
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Where Super Labs fit in the AI market
Super Labs entered a crowded market that included AI-agent and workflow platforms such as Gumloop and Langflow, software procurement marketplaces such as Vendr and Tropic, and AI consultants and implementation firms.
These businesses were adjacent rather than identical competitors. Automation platforms generally focused on building workflows or agents. Procurement platforms focused on purchasing and vendor management. Consultants provided services. Super Labs proposed combining discovery, vendor matching, integration, and implementation for traditional businesses.
Its claimed differentiators were a marketplace model, a mid-market focus, and an emphasis on security, reliability, and integration support instead of merely listing AI tools.
Super Labs is now Latch
As of August 2026, the company’s current identity is Latch. Kalb described the rebrand as more than a change of name. The company concluded that AI automation projects can fail before the technology is deployed because the business has not accurately captured how work is really performed.
Employees may describe the official process while leaving out shortcuts, exceptions, judgment calls, and undocumented dependencies known only to experienced operators. Latch’s product is built around capturing that operational context.
The current workflow is:
- Show and tell: An employee records a real workflow and narrates what they are doing.
- Understand: Latch uses the recording and an AI assistant’s clarifying questions to create structured context or a knowledge graph representing the process.
- Build: That context is supplied to AI tools and agents so they can help create or improve automations.
Latch says its MCP server makes the captured context available to compatible AI agents. MCP compatibility is not the same as a turnkey automation: customers may still need to configure the agent, permissions, tools, prompts, integrations, and human-approval rules.
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This represents a meaningful change in emphasis:
| Original Super Labs | Current Latch |
|---|---|
| AI marketplace and implementation partner | Workflow-capture and AI-context platform |
| Help customers find and deploy external AI tools | Capture operational knowledge for use by AI agents |
| Focused on vendor selection and integration | Focused on context, process accuracy, and reusable workflow knowledge |
Public information connects the two brands, but it does not independently establish whether the legal entity, investor agreements, or corporate structure changed. The transition should therefore be understood as a rebrand and product repositioning unless the company provides more formal corporate details.
What Latch sells now
Latch’s website currently presents the product to operators and IT or AI leaders in manufacturing, real estate, distribution, construction, financial services, and consumer packaged goods. The site says Latch captures work through screen recording and voice narration, converts it into structured context, and provides that context to AI agents through an MCP server. It also describes the company as SOC 2 compliant; that claim does not, by itself, disclose the audit scope, report date, or control set.
The following prices were displayed on Latch’s official pricing page on August 18, 2026 and may change:
| Plan | Price shown | Intended use | Included details |
|---|---|---|---|
| Free | $0/month | One person | One-day retention and 50 credits per month |
| Pro | $49/month | Individual consultants, operators, and builders | Unlimited retention and 500 credits per month |
| Team | $499/month | Up to 10 users | 5,000 credits per month, shared library, and role-based access |
| Business | $1,499/month | Up to 50 users | 15,000 credits per month and dedicated support |
| Enterprise | Custom | More than 50 users | Custom integrations, SCIM, audit logs, SLA-backed uptime, and custom data residency |
The pricing page says annual billing provides a 20% discount on listed eligible paid plans. Capture itself is described as unlimited, while credits are consumed when an agent queries the Latch library through MCP. Failed or errored queries do not consume credits. SSO is listed as included on every plan.
Subscription price is only part of the likely cost. A buyer may also need to budget for security review, process preparation, downstream AI tools, integration work, change management, and internal testing. Latch’s public pages do not fully specify whether full implementation or custom automation development is included in the subscription, offered separately, or handled through partners.
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Latch may be relevant when a business has:
- Poorly documented processes.
- Experienced employees who hold critical operational knowledge.
- Many exceptions and judgment calls.
- AI projects that failed because requirements or context were incomplete.
- An internal owner responsible for testing and maintaining automation.
It may be a poor fit when the primary need is a conventional ERP, CRM, RPA, or ticketing feature; when the process is already well documented; or when company policy prohibits recording screens or operational conversations.
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Before signing up, a buyer should ask where recordings and extracted data are stored, how long information is retained, who can access a shared library, whether sensitive fields can be excluded or redacted, what audit records are available, which integrations are supported, what happens when an employee leaves, and how model-provider permissions are handled.
Risks and unresolved questions
Capturing a workflow does not guarantee a reliable automation. A process may change after it is recorded, one employee’s method may not represent the organization, or exceptions may be more important than the normal path. Automating a broken process can also make errors happen faster.
Screen recordings can contain passwords, customer records, financial information, medical information, confidential documents, or proprietary data. Employees may also be reluctant to record their work if the purpose and access rules are unclear. Recording should therefore be treated as a privacy, security, and employee-trust decision—not simply a convenience feature.
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Human approval may remain essential for payments, hiring, pricing, safety decisions, regulated communications, and other high-impact actions.
Several business questions also remain unanswered in the available public material: the number of customers, current team size, revenue, retention, measurable ROI, live integrations, the amount of implementation support included, and whether the original marketplace model was discontinued or merely deprioritized.
Bottom line for potential customers and partners
Super Labs began as an attempt to help mid-market businesses discover and implement practical AI tools. Its evolution into Latch suggests a sharper thesis: for many operational companies, the bottleneck may not be access to AI models but the lack of accurate, structured knowledge about how work is actually done.
That makes Latch most interesting for organizations with undocumented, exception-heavy workflows and a clear owner for governance and testing. It is not automatically a replacement for enterprise software, a systems integrator, or a complete automation program. The $8 million launch round demonstrates investor support, but buyers should evaluate the product on documented security controls, integration requirements, implementation scope, and measurable business outcomes.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
