Platform

AI Context Platform

A managed context layer that assembles, ranks, and serves the right knowledge to your models on every request.

30+

Prebuilt data connectors

<200ms

Median retrieval latency

SOC 2

Aligned access controls

All products

Hybrid semantic + keyword retrieval

Live connectors keep sources fresh

Row-level access policies on context

Overview

The AI Context Platform sits between your data and your models, turning scattered documents, tickets, and databases into retrieval-ready context. Connectors ingest and keep sources fresh, a hybrid retriever ranks what matters for each query, and a policy layer enforces who can see what — so answers stay grounded, current, and auditable.

How it works

Point it at your sources — object storage, wikis, databases, ticketing — and the platform chunks, embeds, and indexes them continuously. At query time a hybrid retriever blends semantic and keyword signals, re-ranks candidates, and returns a compact, cited context window your model can trust.

What's included

Managed ingestion and refresh, a hybrid retrieval API, per-source access policies, and full request tracing — so every answer can be traced back to the exact passages that produced it.

See it in action

Book a walkthrough and we'll show it running against a stack like yours.

All products

AI Model Aggregator

One API in front of every major model provider, with routing, fallbacks, and unified billing — an OpenRouter you can run yourself.

Agent Orchestration Runtime

A runtime for multi-step agents — tool calls, state, retries, and human-in-the-loop — running reliably in production.

Vector Memory Store

A managed vector database tuned for retrieval workloads — fast, filtered, and horizontally scalable.

Prompt Evaluation Suite

Test, score, and regression-check prompts and model changes before they reach production.

Enterprise RAG Gateway

A hardened gateway that adds retrieval, guardrails, and audit logging in front of any model endpoint.