Trellis
Trellis (Trellis Research, Inc.) is an AI-powered state and federal trial court research and litigation analytics platform founded by former litigator Nicole Clark. It makes the fragmented U.S. state trial court system — plus federal, appellate, supreme, and bankruptcy courts via PACER — searchable through a single interface, and exposes that data programmatically through the Trellis Trial Court Data API. Law firms, financial institutions, insurers, and researchers use Trellis for case research, judge and ruling analytics, docket monitoring with alerts, document retrieval, business development, lead generation, and due diligence. API access (V2, adding federal + PACER on top of V1 state coverage) is provisioned through a sales-gated free trial.
More than an index entry, but the surface is still mostly links rather than artifacts — the cohort most likely to move a full band from modest, well-targeted work.
API Evangelist profiles Trellis the way a machine reads it — 4 machine-readable artifacts across 1 API, 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 — Trellis scores 26.2/100 (emerging), with a separate agent-readiness read of 15/100 (agent aware). 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 Trellis
Each block below is one kind of artifact we hold for Trellis. 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.
APIs 1
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Trellis Trial Court Data API
Programmatic access to Trellis's state and federal trial court dataset: Smart Search over rulings, dockets and documents; docket/case data (parties, attorneys, documents, events...
Event Specifications 1
Not every API is request/response. AsyncAPI describes the event-driven and streaming side — the webhooks and channels — so the asynchronous half of the interface is documented the same way the synchronous half is.
AsyncAPI definitions for this provider's event-driven and streaming APIs.
Trellis Webhooks
ASYNCAPISecurity Posture 2
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 Trellis — 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
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 2
Pagination, idempotency, versioning, errors, and events
Access & Security 2
Authentication, authorization, and security posture
Operate 2
Status, limits, changes, and where to get help
Company 3
The organization behind the API
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