The data control plane
for AI agents.
Agents ask Datris for data. Datris finds it, acquires it, validates it, lands it in the stores you already run, and returns it with provenance — over MCP, without ever holding your keys.
keyFields on the pipeline.Your agents already do this work. Without a control plane, they do it badly.
The choice isn't Datris vs. an ETL tool. It's whether the agent operates through a governed surface, or improvises against your infrastructure.
- 63 low-level tool integrations to build and maintain per agent
- Credentials scattered across agent configs, env vars, and prompts
- No audit trail — the chat log is the record
- Chat-only state; every session starts from zero
- Regenerating an agent script quietly loses sync bookmarks
- Failure diagnostics: raw stack traces you paste into another chat
- Generated code lives in a database column, no diff, no history
- 63 capabilities behind one MCP surface — agents learn one door
- Vault-brokered credentials — the agent never holds a key
- Every run recorded; every generated script versioned in git
- Durable pipelines and platform-held sync bookmarks
- Regenerate a script freely — the state was never in the code
- AI Error Explanation — root cause in plain English
- Agent-written code runs in an isolated container with no keys inside
One agent-driven loop, every step recorded
Acquire, validate, land, observe, explain, repair. Same loop every run, same audit trail every time — so the agent's job is reasoning about the data, not improvising infrastructure.
Type what you want.
The Assistant builds it.
Datris ships with an Assistant Agent inside the platform UI. Tell it what data you want. It asks a few sharp scoping questions, picks the right source and destination, generates the fetcher, requests credentials securely, runs the job, confirms the rows landed, and lets you query the result — usually in seconds, with no hand-written config.
- Clarifies scope before building — picks the right source, destination, and cadence with you
- Generates and runs the fetcher (tap) for external APIs and files
- Requests credentials through a secure form — never in chat history
- Polls job status and confirms rows actually landed before saying "done"
- Flags things you didn't ask about — upsert vs append, fair-use policies, schema drift
- Answers natural-language questions over the data once it's in
- "I'm looking for corporate earnings data."
- "Ingest these PDFs into a vector store for RAG."
- "Refresh treasury yields from FRED nightly and let me query trends."
Your AI agents are
first-class pipeline operators
Datris ships with a native MCP server. Claude, Cursor, OpenClaw, and any MCP-compatible AI agent can register pipelines, trigger jobs, and query your structured, document, and vector data in real time — all through natural conversation.
- Register pipelines and generate schemas from sample data
- Create, schedule, and run AI-generated taps
- Ingest documents into vector databases (extract → chunk → embed)
- Upload data for processing
- Trigger and monitor pipeline jobs
- Profile data and get AI insights
- Semantic search across vector databases
- Query PostgreSQL and MongoDB directly
- Manage credentials via Vault — without ever holding the key
The plane Datris controls
Every source, every destination, every AI call, every job — one MCP surface, one audit trail, one place credentials live. Datris owns acquisition, validation, storage, and observability so your agents can focus on reasoning, not integration.
Your warehouses, models, and formats — Datris connects them
Datris is neutral by design. Point it at whichever warehouse you bought, whichever vector store your RAG runs on, whichever model your procurement approved. Destinations and providers are settings — never migrations.
Self-host anywhere you want
Datris runs on-prem, in any cloud, or on your laptop — anywhere Docker runs. Open source under AGPL-3.0, built on proven infrastructure. No proprietary services, no vendor lock-in, no surprise bills. There is no managed service; your deployment lives inside your perimeter.
$ git clone https://github.com/datris/datris-platform-oss.git
$ cp .env.example .env
# Add your API key (at least one required for AI features)
$ docker compose up -d
$ curl http://localhost:8080/api/v1/version
Clone. Configure. Launch. Your full pipeline in under a minute.
$ datris ingest sales_q4.csv \
--ai-validate "revenue must be positive, date must be in 2026" \
--ai-transform "standardize region codes to ISO 3166" Plain-English validation and transformation inline on the ingest — no config file needed for one-shot loads.
What we're building next
Nothing here yet — but it's where the control plane goes. If any of these decide whether Datris fits your team, tell us. Priorities move.
Send us a message
Questions, feedback, or just want to chat — we'd love to hear from you.