Pomerado

Turns any website into an API/MCP in minutes, enabling AI agents to connect to legacy portals and internal tools.

Cover Block

Public sources

Field	Value
Name	Pomerado
Tagline	Turns any website into an API/MCP in minutes, enabling AI agents to connect to legacy portals and internal tools [Pomerado]
Headquarters	San Francisco, United States [Y Combinator]
Founded	2026 [Y Combinator]
Stage	Pre-Seed
Business Model	API / Developer Platform
Industry	Deeptech
Technology	AI / Machine Learning
Geography	North America
Growth Profile	Venture Scale
Founding Team	Co-Founders (2), Akshay Manglik and Alan Ma [Pomerado]
Funding Label	Undisclosed
Investors / Accelerator	Y Combinator [Y Combinator]

Executive Summary

PUBLIC Pomerado builds software that turns websites into APIs and Model Context Protocol servers, a timely pitch as enterprises and developers look for ways to connect AI agents to legacy portals and internal tools that were never designed for programmatic access [Pomerado] [Y Combinator] [pomerado.ai, October 2026]. The company appears to be very early, founded in 2026 and listed in Y Combinator's F26 batch, but the wedge is clear: if Pomerado can make brittle browser automation behave more like dependable infrastructure, it addresses a real constraint in agent deployment today [Y Combinator] [Pomerado].

The founding story, as presented publicly, centers on former McKinsey and Scale AI operators who say they worked on enterprise agent strategy and deployments across finance, healthcare, and software, with Mayo Clinic cited as part of that prior experience rather than as a current customer reference [pomerado.ai/about] [Y Combinator]. Akshay Manglik is identified as co-founder and CEO, with prior roles at Scale AI, PayPal, and Johns Hopkins APL according to Company Launch Tracker and LinkedIn-sourced materials; Alan Ma is identified by the company as co-founder and CTO [Company Launch Tracker, September 2026] [LinkedIn] [Pomerado].

On product, the public claim is straightforward: Pomerado can turn "any website into an API / MCP in minutes," with use cases spanning ERP, EHR, and CRM workflows, customer integrations, enterprise assistants, and consumer web tasks such as Google Flights search and Instacart price comparison [Pomerado] [pomerado.ai, October 2026]. Its differentiation is also framed plainly, though mostly through company-originated assertions: faster, cheaper, and more reliable performance than browser-agent alternatives such as Astra Computer Use, Instinct, and Muse, alongside an open-source core integration harness under an MIT license [Pomerado] [GitHub].

Commercially, the business model reads as a developer platform or API layer, but the public record is still thin on pricing, customer adoption, or retention [Pomerado] [Y Combinator]. Funding is likewise sparse: Y Combinator is the only clearly identified backer in the available sources, no round amount is confirmed, and YC's company page lists the team at two employees, which is consistent with a pre-seed operating posture [Y Combinator].

What matters over the next 12 to 18 months is less the headline promise than proof that the product can hold up in production, especially on reliability, security, and repeatability across messy enterprise systems. The next public milestones worth watching are evidence of sustained developer adoption around the open-source project, named production deployments, and third-party validation of the performance claims that currently rest largely on company or adjacent-source materials [GitHub] [Pomerado].

Company-stated, unverified -- This section relies materially on company website claims and Y Combinator listings, with limited independent corroboration from Company Launch Tracker, LinkedIn, X, and GitHub.

Taxonomy Snapshot

Axis	Value
Stage	Pre-Seed
Business Model	API / Developer Platform
Industry / Vertical	Deeptech
Technology Type	AI / Machine Learning
Geography	North America
Growth Profile	Venture Scale
Founding Team	Co-Founders (2)
Funding	Undisclosed

How the Company Got Here

PUBLIC Pomerado emerged in 2026 with a narrowly defined pitch: take websites that were never built as software interfaces and convert them into APIs or Model Context Protocol servers that AI agents can use directly [pomerado.ai]. The company is based in San Francisco and is listed by Y Combinator as part of its F26 batch, which is the clearest public milestone in its formation to date [Y Combinator].

The public record is still thin, but the chronology is straightforward. Pomerado’s website presents the product as a way to connect agents to legacy portals, consumer sites, and internal tools without a native API, while the company’s October 2026 documentation shows live, task-level examples such as searching Google Flights and comparing prices on Instacart [pomerado.ai, October 2026]. That sequence suggests a company moving quickly from launch positioning to developer-facing documentation, although the available public materials do not establish incorporation details or a disclosed financing amount [pomerado.ai] [Y Combinator].

The founding team is identified publicly as Akshay Manglik, CEO, and Alan Ma, CTO, and the company frames itself as built by former McKinsey and Scale AI operators who worked on enterprise agent deployments before starting Pomerado [pomerado.ai]. Y Combinator’s company page also lists the startup as active and reports a two-person team, consistent with an early-stage buildout rather than a scaled commercial organization [Y Combinator].

Company-stated, unverified -- This section relies primarily on company materials, with partial corroboration from Y Combinator for location, batch, status, and team size.

Product and Technology

MIXED Pomerado is pitching a fairly specific abstraction layer: instead of asking an agent to operate a website through browser actions, it turns that website into an API or MCP endpoint that the agent can call directly [Pomerado] [Y Combinator]. The public product language is consistent across the homepage, YC profile, and docs, with the company describing support for legacy portals, consumer websites, and internal tools, particularly where no usable API exists [Pomerado] [Y Combinator] [pomerado.ai, October 2026]. In plain terms, the claim is that Pomerado sits between an agent and brittle web interfaces, then exposes those workflows as structured tools rather than screen-driven steps [Pomerado] [Y Combinator].

The public demos and documentation stay at the workflow level rather than the systems level, which is normal for a company this early. The docs cite website tasks such as searching Google Flights and comparing prices on Instacart, and the homepage includes setup instructions for connecting the Pomerado MCP server in agent environments such as Claude Code and Codex [pomerado.ai, October 2026] [Pomerado]. Pomerado also says the core integration minting harness is open source and MIT licensed, which matters because it gives technical buyers a way to inspect at least part of the implementation rather than relying only on a hosted black box [GitHub] [GitHub, Unknown].

Performance differentiation is the main public technical claim, but the evidence is still company-led. Pomerado says it is 2x faster, 10 to 1,000 times cheaper, and 30% more accurate than Astra Computer Use, Instinct, and Muse for browser use, while an external X post from Aaron Epstein repeats a similar framing with a 2 to 4x speed range and the same broad cost and reliability narrative [Pomerado]. That is enough to identify the intended wedge, namely replacing browser-agent interaction with API-like calls, but not enough yet to validate benchmark methodology, workload mix, or production reliability across enterprise systems.

Company-stated, unverified -- This section relies materially on company website, docs, and YC profile claims, with partial corroboration from GitHub and an external X post but no independent benchmark validation.

Where the Demand Sits

PUBLIC The market matters now because AI agents are moving from demos into workflows, and the limiting factor is often not model quality but access to systems that were never built with agent interfaces in mind [Y Combinator] [pomerado.ai].

Pomerado is aimed at a real integration bottleneck, but the public record here is thin on market sizing. There is no cited third-party TAM, SAM, or SOM for Pomerado specifically in the available materials, so the cleanest read is through adjacent categories: developer infrastructure for application integration, enterprise automation software, and emerging agent tooling that sits between models and legacy systems [Y Combinator] [pomerado.ai, October 2026]. That framing matters because Pomerado is not selling a general-purpose model, according to its website and documentation, it is selling a way to make websites and portals usable by agents through API or MCP layers when no native API exists [pomerado.ai] [pomerado.ai, October 2026].

The immediate demand driver is straightforward. Enterprises have many systems, including ERP, EHR, CRM, internal tools, and external portals, that are still operationally important but hard to automate cleanly, according to Pomerado's own use-case descriptions [pomerado.ai]. If that pain point persists, tools that reduce browser-based fragility and replace repetitive UI automation with a more structured interface should benefit. That said, the strongest performance claims in the public record, including assertions that Pomerado is faster, cheaper, and more accurate than browser-agent alternatives, come from the company and a third-party social post rather than independent benchmarking, so they should be treated as positioning rather than settled market proof [pomerado.ai].

The closest adjacent markets are browser automation, robotic process automation, enterprise integration platforms, and MCP or agent infrastructure. Pomerado's examples, such as Google Flights search and Instacart price comparison, suggest that the product can also touch consumer web tasks, but the more durable commercial wedge appears to be enterprise systems with poor or missing APIs, where reliability and governance matter more than novelty [pomerado.ai, October 2026] [Y Combinator]. Substitute solutions are easy to identify in principle: direct API integrations where they exist, browser-use agents, and traditional RPA stacks. The public materials do not yet establish which segment is adopting fastest, or whether buyers view Pomerado as a developer tool, an automation layer, or a lower-friction replacement for parts of RPA.

Regulatory and macro forces cut both ways. On the supportive side, the pressure to deploy AI into regulated and operationally sensitive workflows is rising across healthcare, finance, and software, sectors the company references when describing founders' prior experience rather than current customer contracts [pomerado.ai/about] [Y Combinator]. On the constraining side, any product that intermediates access to internal tools, legacy portals, and account-based websites will face buyer scrutiny around security, permissions, auditability, and terms-of-service risk. The homepage's emphasis on sign-in flows and read-only trial tasks suggests the company is aware of those trust barriers, but the public record does not yet provide independent evidence of compliance depth or production-scale controls [pomerado.ai].

Market lens	Public sizing claim
Pomerado-specific TAM/SAM/SOM	No third-party market sizing surfaced in the available sources
Adjacent category	Enterprise and developer-tool demand is implied by use cases across ERP, EHR, CRM, internal tools, and web workflows [pomerado.ai]
Adoption signal	YC lists Pomerado as an active F26 company building website-to-API or MCP infrastructure for agents [Y Combinator]

The absence of hard third-party sizing does not negate the opportunity, but it does narrow what can be said with confidence. At this stage, the market case rests more on the visible integration problem and the rise of agent tooling than on independently quantified category data.

Company-stated, unverified -- This section relies primarily on company and accelerator materials, with no independent third-party market sizing confirmed in the available sources.

Competitive Landscape

MIXED

Pomerado is positioning itself against browser-use agents by arguing that the better abstraction is not an agent that clicks through a site, but an API or MCP layer generated from the site itself [pomerado.ai] [Y Combinator].

Company	Positioning	Stage / Funding	Notable Differentiator	Source
Pomerado	Turns websites into APIs and MCP servers for AI agents, with a focus on legacy portals, consumer sites, and internal tools	YC F26; funding amount undisclosed	Open-source core integration minting harness; claims faster, cheaper, and more accurate browser-use performance than named peers	[Y Combinator] [pomerado.ai] [GitHub]
Astra Computer Use	Browser-use competitor referenced by Pomerado in comparative performance claims	Not established in the provided sources	Included in Pomerado's benchmark set for browser-use alternatives	[pomerado.ai]
Instinct	Browser-use competitor referenced by Pomerado in comparative performance claims	Not established in the provided sources	Included in Pomerado's benchmark set for browser-use alternatives	[pomerado.ai]
Muse	Browser-use competitor referenced by Pomerado in comparative performance claims	Not established in the provided sources	Included in Pomerado's benchmark set for browser-use alternatives	[pomerado.ai]

The market map breaks into three layers. First are direct challengers in browser automation and browser-use infrastructure, where Pomerado itself names Astra Computer Use, Instinct, and Muse as the comparison set for speed, cost, and accuracy claims [pomerado.ai]. Second are incumbent substitutes, meaning the internal engineering teams and traditional API integration work that enterprises still use when a system has no usable API, an integration pattern implied by Pomerado's focus on ERP, EHR, and CRM workflows across legacy portals and internal tools [pomerado.ai] [Y Combinator]. Third are adjacent agent platforms and MCP tooling layers that can orchestrate workflows once access exists, but do not necessarily solve the core problem of extracting reliable actions from websites without native APIs [pomerado.ai, October 2026].

Where Pomerado appears strongest today is not proven distribution, but product framing. The company has chosen a narrow wedge, converting websites into reusable API or MCP surfaces for agents, rather than asking users to tolerate brittle browser sessions for each task [pomerado.ai] [Y Combinator]. That can matter if reliability, latency, and cost become the main buying criteria for enterprise automation. The edge is still perishable. The performance claims are company-stated or repeated by third parties citing the company, and the public evidence does not yet show a large installed base, proprietary dataset moat, or channel control that would make the lead difficult for peers to copy [pomerado.ai].

The main exposure is straightforward. If Astra Computer Use, Instinct, or Muse can match reliability while keeping the developer experience at the browser layer, Pomerado's abstraction advantage narrows because customers may prefer tools that work across more sites without an integration-minting step [pomerado.ai]. There is also a category boundary here. Pomerado is strongest where a website or portal is the bottleneck, but its public materials do not establish an advantage in systems that already have well-maintained APIs, where conventional integration platforms or native connectors may remain simpler and more governable [pomerado.ai] [pomerado.ai, October 2026].

The most plausible 18-month scenario is a split market rather than a winner-take-all outcome. Pomerado is the winner if enterprise buyers decide that agent reliability on legacy and API-poor systems matters more than preserving a browser-centric workflow, and if the open-source core helps it spread through developer adoption inside YC-adjacent and forward-deployed software circles [GitHub] [Y Combinator]. Muse is the loser if the market moves toward API-or-MCP generation as the default architecture for repeatable workflows instead of generalized browser operation, though that remains a conditional view because the public record here does not independently verify customer preference, retention, or benchmark methodology for any of the named peers [pomerado.ai].

Company-stated, unverified -- This section relies materially on company statements and company-cited competitor comparisons, with limited independent corroboration from Y Combinator and GitHub.

Opportunity

PUBLIC

The prize here is not a better browser agent, but a control layer that lets AI systems reach the large share of enterprise and consumer software that still lacks clean APIs, which would make Pomerado relevant anywhere agents need to act rather than just answer [Pomerado] [Y Combinator].

The headline opportunity is to become the default website-to-API and MCP infrastructure for agent builders who need reliable access to legacy portals, internal tools, and consumer sites [Pomerado] [Y Combinator]. That outcome is reachable, not merely aspirational, because the company is attacking a specific bottleneck that current agent workflows visibly struggle with: software surfaces that were built for humans, not programmatic access. Pomerado's public product framing is consistent across its homepage, docs, and YC profile, all of which describe turning websites into APIs or MCP servers in minutes, with example tasks such as Google Flights search and Instacart price comparison [Pomerado, October 2026] [Y Combinator]. If developers increasingly prefer stable tool calls over brittle browser automation, the company could sit in the request path for a wide class of agent actions.

That is a meaningful wedge because the initial buyer does not need to believe in a full agent future to see value. A team that wants to automate one ERP, EHR, or CRM workflow, or connect an internal assistant to one legacy portal, can adopt a point solution first and broaden usage later if reliability holds [Pomerado] [Y Combinator]. The open-source positioning also matters here: Pomerado's GitHub materials describe the core integration minting harness as MIT licensed, which can reduce adoption friction for developers who want to test before committing to a broader platform decision [GitHub] [GitHub].

Scenario	What happens	Catalyst	Why it's plausible
Developer beachhead to enterprise standard	Developers adopt the open-source core for narrow website tasks, then enterprise teams standardize on Pomerado for internal assistants and workflow automation	A visible increase in MCP-based agent tooling and enterprise demand for non-browser integrations	The company already frames itself around MCP and publishes concrete website-task examples, while YC positions it as infrastructure for agents connecting to websites and internal tools [Pomerado, October 2026] [Y Combinator]
Legacy-system integration layer	Pomerado becomes the fastest path to make old portals usable by AI agents in healthcare, finance, and back office operations	One or two strong reference deployments in API-poor environments such as ERP, EHR, or compliance-heavy workflows	Public materials repeatedly target ERP, EHR, and CRM automation, and the founders' prior work is described as enterprise agent deployments across finance, healthcare, and software [Pomerado] [Y Combinator]
Consumer-web action rail for assistants	Personal and enterprise assistants use Pomerado as the default way to complete tasks on travel, shopping, booking, and billing sites	Wider adoption of agents that need dependable website actions rather than chat-only responses	The homepage and docs already position the product around practical website tasks such as flights, hotels, shopping, orders, bills, and bookings, which suggests an action-oriented product surface rather than a narrow internal-only tool [Pomerado] [Pomerado, October 2026]

What compounding could look like is straightforward. Each successful website integration can lower the cost of the next one if the company is in fact building reusable patterns for authentication, page structure, action execution, and exception handling across common web workflows, though that mechanism remains more inferred than publicly demonstrated [Pomerado] [GitHub]. The early signs are modest but relevant: there is a public docs surface, an open-source codebase, and a simple product message that maps to immediate developer use cases, all of which can help a young infrastructure product accumulate usage before it has a traditional enterprise go-to-market motion [Pomerado, October 2026] [GitHub] [Y Combinator]. If that usage base grows, the strategic value rises because the winning layer would not just expose websites to agents, it would shape how developers prefer agents to interact with software.

The size of the win is easier to frame qualitatively than numerically because the available public record does not include revenue, customer count, or a third-party market size study. Still, there is a credible path to a very large outcome if Pomerado becomes core infrastructure for agent connectivity across legacy enterprise software and consumer web actions, since infrastructure layers that become part of developer workflow selection can support platform-scale valuations over time (scenario, not a forecast) [Y Combinator] [Pomerado]. The evidence in hand supports treating that upside as real but early: the company has a clear technical wedge, public product artifacts, and YC backing, but not yet the independent traction markers that would allow a tighter valuation frame.

Company-stated, unverified -- This section relies materially on company and accelerator materials, with limited independent corroboration beyond YC and GitHub.

Sources