Data-team agentic infrastructure
Agentic AI systems for data teams.
DataPlaneLabs builds the agents and infrastructure that put data teams’ work into production — securely, on your own stack.
See what we build →- Control execute · policy · govern
- Context knowledge · lineage · observability
- Corpus data · memory · vault
What we do
A company building agentic systems for data teams.
DataPlaneLabs is a product company. We build AI agents — and the secure runtime they run on — for the people who own pipelines, warehouses, analytics, and governance. Each system is designed to act in production, on your infrastructure, with every action governed and observable.
Every action, one governed path
An agent never touches your systems directly. It routes through a runtime that authenticates, applies policy, and records every tool call.
How our agents run: every action flows through a governed runtime.
What we build
Agentic solutions for the work data teams own.
A growing family of agents and runtime — built for data engineering, analytics, and governance. Each one ships on your stack and acts under your controls.
- The substrate
Governed tool-execution runtime
The substrate every agent runs on — policy, identity, and audit on each tool call.
Agents that can act on real systems without losing control.
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Pipeline & ELT agents
Agents that build, fix, and maintain ingestion and transformation pipelines.
Less manual pipeline toil; faster time to a working dataset.
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Data-quality & observability agents
Agents that watch freshness, schema, and anomalies — and act when something breaks.
Issues caught and triaged before they reach the dashboard.
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Analytics & insight agents
Agents that answer questions over your warehouse and surface what changed and why.
Self-serve answers grounded in your real data, not guesses.
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Self-hosted agent control
Deploy, observe, and govern your agent fleet inside your own perimeter.
Your data, models, and logs stay on your infrastructure.
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The family is expanding — named products are on the way.
Why data teams
Most data work never makes it to a reliable agent.
Demos look promising. Production breaks them. For data teams the gap is rarely the model — it is everything the agent has to touch: warehouses, pipelines, tools, and the controls around them.
- Over 40%
of agentic AI projects are predicted to be canceled by end of 2027 — driven by escalating costs, unclear value, and weak governance. The blocker is the execution layer, not the model.
e.g. an ELT agent that fixes a broken pipeline in a notebook — but can’t be trusted to run it against production.
Source: Gartner, 2025 - Ungoverned
Agents query warehouses, write tables, and call tools with no audit trail and no policy in the way.
e.g. a data-quality agent that can drop or rewrite a table, with nothing recording who asked or why.
- Locked in
Point integrations wire your agents to a vendor. Your data and logs live on their infrastructure, not yours.
e.g. a warehouse-query agent whose prompts, results, and logs all flow through a vendor’s cloud.
How we build
Built for agents that act in production.
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Secure & governed by default
Zero-trust on every tool call. RBAC, least-privilege, and a full audit trail — no agent touches a system without a policy decision.
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Production-ready
Durable runtime with retries, circuit breakers, and recovery. Agents keep running when the world is imperfect.
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On your infrastructure
Self-hosted. Your data, models, and logs stay on your stack. No lock-in, no egress, no surprises.
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Scales & observes
One agent or ten thousand — cost, latency, and policy visible per run. Observability is built in, not bolted on.
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Knowledge-aware
Retrieval grounded in your warehouse, schemas, and docs — agents reason over your data, not generic hallucinations.
Works with your stack
Bring your own everything.
No partnerships to sign, no platform to adopt. Our agents and runtime plug into the tools your data team already runs — at the category level, on your infrastructure.
- LLM providers Hosted or self-hosted models — route to whichever you already use.
- Vector stores Ground retrieval in the embeddings store your team already operates.
- Data warehouses Read and write through your warehouse, under your access controls.
- Your existing tools Orchestration, identity, and observability you already run — wired in, not replaced.
Category-level by design — we connect to what you run, not to a fixed vendor list.
See what an agent could do on your stack
Walk through your data workflows with an engineer who builds these systems — no slides, just architecture.
30-minute call · Your stack · No commitment
How we work
From first call to running in production.
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Assess
Week 1We map your data workflows, identify where agents create real leverage, and define what production-ready looks like for your stack and compliance requirements.
Deliverable A prioritized workflow map and a production-readiness definition for your stack.
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Deploy
Weeks 2–4We build and deploy the agents and runtime on your infrastructure — integrating with your warehouse, tools, identity providers, and data sources.
Deliverable A first agent running in your environment, governed end-to-end on your stack.
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Operate
OngoingWe stay in the loop — ongoing support, policy tuning, and new agents as your needs grow.
Deliverable Tuned policies, observability dashboards, and a roadmap for the next agents.
Typical time to a first production workflow: ~4 weeks. Scope and stack complexity shift the range.
Why it holds
Built on a security-first, self-hosted architecture.
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Security posture
Zero-trust network model, encrypted at rest and in transit, RBAC with least-privilege defaults, full audit log per tool invocation.
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Self-hosted
Runs on your Kubernetes cluster. No vendor cloud, no shared tenancy, no data leaving your perimeter.
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Production-tested
Battle-hardened on real data workloads. Not a research project — operational systems with runbooks and SLOs.
Common questions
The questions data teams ask first.
Does governance add latency?
A thin policy check runs per tool call — an authorization decision, not another model hop. It sits between your agent and the systems it touches, so the cost is a fast local check, not a round-trip to a model.
How does pricing and cost work?
It runs self-hosted on your own compute, so you pay for the infrastructure you already have. No per-seat licensing and no usage tax on every agent run.
Do we have to rebuild what we already have?
No. Adoption is incremental — we wrap your existing agents, pipelines, and tools so they run under governance, rather than asking you to rewrite them up front.
Are we locked in?
No. The runtime is open and runs on your infrastructure. Your data, models, and logs stay inside your perimeter, so you can take your stack with you.
How is it secured?
Zero-trust at the point of tool invocation. Every tool call requires a policy decision with identity and least-privilege scope, and each one is written to a full audit log.
Do you build foundation models?
No. We build the agents that do your data team’s work and the secure runtime they run on. You bring the models — hosted or self-hosted — and we put them to work in production.
By the numbers
Built for production from day one.
- 100%
- on your infrastructure
- Every
- tool call governed & audited
- Zero
- data egress to our systems
- 0
- vendor lock-in
Ready to put your data team’s work into production?
Tell us what you are building. We will show you where an agent fits.