Lato

Lato (LATO Labs) is a San Francisco company building an agent-native research and simulation platform for investors. Its agents source and conduct hundreds of voice interviews with verified domain experts and customers in any language, each returned with a recording, transcript and thematic analysis, then combine those interviews with thousands of documents from public sources and a fund's own proprietary knowledge into a single commercial study. The study becomes a live simulation of the market that analysts can question and run scenarios against to predict where a market is headed. Investors use Lato for commercial due diligence, expert interviews, market sizing, competitive mapping, customer interviews, pricing studies, deal sourcing, inbound screening and portfolio value creation, compressing research that traditionally costs hundreds of thousands of dollars and takes weeks into hours. The product integrates with the tools investment teams already run on, including Outlook, SharePoint, Gmail, Google Drive, Slack, Teams, Excel, Affinity, Attio, Harmonic and Granola. Lato was founded by Tymek Staniszewski (CEO) and Tien Chu (CTO), is backed by Y Combinator (Summer 2026), and builds on Anthropic, OpenAI and ElevenLabs. As of this profile Lato publishes no public developer API, documentation or SDKs; the company surface is captured here for discovery and the repository will be re-enriched when a developer program appears.

Lato is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Agents, Market Research, and Due Diligence.

Lato’s developer surface includes support and 12 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

12.8/100 emerging ▬ flat Agent 12/100 agent aware Front door AI 2/6 trace saas Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

0 APIs 1 MCP Servers

Company Artificial Intelligence Agents Market Research Due Diligence Investment Venture Capital Private Equity Simulation Voice

Kin Score

Kin Score How this is scored →
scored 2026-11-06 · rubric v0.27.0

Composite quality — 12.8/100 · emerging

Contract Quality 0.0 / 21

Developer Ergonomics 0.8 / 17

Access Clarity 3.6 / 17

Operational Transparency 1.2 / 11

Contract Governance 0.0 / 10

Discoverability 5.0 / 9

Regulatory Posture 2.7 / 15

Agent readiness — 12/100 · agent aware

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 12 / 12

Machine-Readable Auth 0 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 5 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

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Agent readiness — checks not met, largest first (exact, from this profile):

Machine-Readable Contract +18 pts — api_specs includes an OpenAPI document
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author
Machine-Readable Auth +10 pts — a served openid-configuration/oauth-authorization-server carrying `issuer` (served), else the securitySchemes class in the provider OpenAPIs — bound (mutualTLS, DPoP, Signature) > negotiable (oauth2 authorizationCode/clientCredentials, openIdConnect) > bearer (http bearer, apiKey)
Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented)
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0

11 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Contract Quality scores 0.0 — up to +25.0 composite points
Developer Ergonomics scores 4.8 — up to +19.0 composite points
Access Clarity scores 21.1 — up to +15.8 composite points

Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from the provider profile at artifacts.apis.io/lato/apis.yml: open an issue on github.com/api-evangelist/lato to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Markets this provider is in provisional

The Kin Score Market layer reads a provider inside its market: its position, and how deep its integrations go. Depth is only comparable within a position. Provisional and unweighted: not part of the Kin Score or agent readiness.

Market	Position	Depth	Registered MCP server	Declares compatibility
Surveys, Market Research and Experience Management	undeclared	-	-	1

Registered MCP server and declares compatibility are report only — not weighted. Undeclared means we found nothing we can read, so there is no number: we never infer integrations.

Standards implemented 2

Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.

Model Context Protocol (MCP)

measured — observed by APIs.io (verified)

OAuth 2.0 Protected Resource Metadata

measured — observed by APIs.io (verified)

MCP Servers 1

Model Context Protocol servers that expose these APIs to AI agents.

Lato MCP Server

Remote MCP server at api.latolabs.io over HTTP.

MCP SERVER

Security Posture 2

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Lato Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Lato Vulnerability Disclosure

Hackerone · security.txt · contact published

SECURITY

Resources

Get Started 1

Portal, sign-up, and the first successful call

Demo

Demo

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

WellKnown

WellKnown

LLMsTxt

LLMsTxt

Access & Security 4

Authentication, authorization, and security posture

SecurityTxt

SecurityTxt

VulnerabilityDisclosure

VulnerabilityDisclosure

Security

Security

DomainSecurity

DomainSecurity

Operate 1

Status, limits, changes, and where to get help

Support

Support

Commercial 2

Pricing, plans, and the legal terms of use

PrivacyPolicy

PrivacyPolicy

TermsOfService

TermsOfService

Company 2

The organization behind the API

Website

Website

LinkedIn

Other 1

Properties that don't map to a standard resource type

CompanyProfile

CompanyProfile

Source (apis.yml)

aid: lato
name: Lato
description: Lato (LATO Labs) is a San Francisco company building an agent-native research and simulation platform for investors.
  Its agents source and conduct hundreds of voice interviews with verified domain experts and customers in any language, each
  returned with a recording, transcript and thematic analysis, then combine those interviews with thousands of documents from
  public sources and a fund's own proprietary knowledge into a single commercial study. The study becomes a live simulation
  of the market that analysts can question and run scenarios against to predict where a market is headed. Investors use Lato
  for commercial due diligence, expert interviews, market sizing, competitive mapping, customer interviews, pricing studies,
  deal sourcing, inbound screening and portfolio value creation, compressing research that traditionally costs hundreds of
  thousands of dollars and takes weeks into hours. The product integrates with the tools investment teams already run on,
  including Outlook, SharePoint, Gmail, Google Drive, Slack, Teams, Excel, Affinity, Attio, Harmonic and Granola. Lato was
  founded by Tymek Staniszewski (CEO) and Tien Chu (CTO), is backed by Y Combinator (Summer 2026), and builds on Anthropic,
  OpenAI and ElevenLabs. As of this profile Lato publishes no public developer API, documentation or SDKs; the company surface
  is captured here for discovery and the repository will be re-enriched when a developer program appears.
deliveryModel:
  model: saas
  open_source: false
  commercial: true
  callable_host: false
  label: Hosted service · you call their endpoint
  confidence: medium
  source:
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source:
  - security
  generated: '2026-11-06'
  method: derived
image: https://latolabs.io/og-image.png
url: https://raw.githubusercontent.com/api-evangelist/lato/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- y-combinator
x-tier: profile
x-tier-reason: researched-no-public-api
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-19'
tags:
- Company
- Artificial Intelligence
- Agents
- Market Research
- Due Diligence
- Investment
- Venture Capital
- Private Equity
- Simulation
- Voice
apis: []
common:
- type: Website
  url: https://www.latolabs.io/
- type: PrivacyPolicy
  url: https://www.latolabs.io/privacy
- type: TermsOfService
  url: https://www.latolabs.io/terms
- type: Support
  url: https://www.latolabs.io/contact
- type: Demo
  url: https://www.latolabs.io/book
- type: LinkedIn
  url: https://linkedin.com/company/latoio
- type: CompanyProfile
  name: Y Combinator company profile
  url: https://www.ycombinator.com/companies/lato
- type: SecurityTxt
  url: well-known/lato-security.txt
- type: WellKnown
  url: well-known/lato-well-known.yml
- type: VulnerabilityDisclosure
  url: security/lato-vulnerability-disclosure.yml
- type: Security
  url: https://www.latolabs.io/security
- type: DomainSecurity
  url: security/lato-domain-security.yml
- type: LLMsTxt
  url: llms/lato-llms.txt
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

Lato API — frequently asked questions

Does Lato have an MCP server?

Yes. APIs.io lists 1 MCP server for Lato: Remote MCP server at api.latolabs.io over HTTP.

Is the Lato API ready for AI agents?

Lato has a Kin Score of 12.8 out of 100 (emerging) and an Agent Readiness score of 12.2 (agent-aware), scored by API Evangelist on 2026-11-06.

Work with this as data

Every provider here is available over the APIs.io API and to AI agents over MCP.

MCP server

One button, every client — Claude, Cursor, VS Code and the rest.

https://apis.io/mcp

Tools for providers

9 MCP tools reach this

find_providersBrowse and filter every provider in the catalog.
get_provider_artifactsEvery artifact this provider publishes, grouped by type.
get_provider_operationsEvery operation across all of their OpenAPIs — one call instead of parsing every spec.
get_provider_toolsEvery MCP tool they ship, with the operation each wraps.
get_provider_evidenceHow each part of their score was established. Free — the basis for a claim should not sit behind it.
get_provider_ratingPRO — composite, band, trend and facet scores.
apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
resolveTurn a domain, URL or GitHub org into the provider it belongs to.
find_cohortsEvery scored population of providers in the catalog.

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Every operation they expose

curl "https://apis.io/api/v1/providers/lato/operations?limit=25"

How their score was established

curl "https://apis.io/api/v1/providers/lato/evidence"

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