Lara AI
Lara AI is an AI agent for People and HR teams, built by SIGMA PEOPLE S.R.L. in Argentina and sold across Latin America. Lara talks to every employee in the messaging tools they already use — WhatsApp, Slack, Microsoft Teams and Google Chat — and covers five modules: Employee Experience (continuous listening, custom surveys and people analytics), Helpdesk (an AI help desk the company says automates over 80% of employee FAQs and requests), Onboarding (end-to-end automated flows for new hires), Comunicaciones (personalised, scheduled and segmented employee communications) and People Analytics (dashboards and reports built from people data). It integrates with HR platforms including SAP, BambooHR, Oracle, Buk, Humand, Mandu and Rex+, and targets retail, consumer goods, technology, consulting, banking and finance, education and manufacturing. Lara AI publishes a marketing site, a Spanish help centre and a real llms.txt, but no public API program — there is no developer portal, API reference, OpenAPI definition or SDK.
Index entry only — little beyond a description and a link, and nothing machine-readable enough for an agent to act on without a human reading the site first.
API Evangelist profiles Lara AI the way a machine reads it — 1 machine-readable artifact, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Lara AI scores 13.5/100 (minimal), with a separate agent-readiness read of 0/100 (human only). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Lara AI
Each block below is one kind of artifact we hold for Lara AI. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
Security Posture 1
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Resources
Every other property we hold for Lara AI — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 1
Portal, sign-up, and the first successful call
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 1
Pagination, idempotency, versioning, errors, and events
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 1
Authentication, authorization, and security posture
Operate 2
Status, limits, changes, and where to get help
Commercial 1
Pricing, plans, and the legal terms of use
Company 4
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/lara-ai · machine-readable index on apis.io