Agentic AI for small and medium businesses

Practical AI agents. On infrastructure you control.

DataPlaneLabs builds the agents that take support, ops, sales, and back-office work off your plate — and the secure, self-hosted runtime that runs them on your stack. Fast to deploy. Affordable. Governed.

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Explore use cases →
  • Control policy · identity · govern
  • Context channels · tools · observability
  • Corpus your stack · memory · vault

What we do

Agents that do the work. A runtime that keeps them honest.

DataPlaneLabs is a product company. We build AI agents for small and medium businesses — and the self-hosted runtime they run on. Each system is designed to act in production, on infrastructure the business already controls, with every action governed and observable.

Control Policy, identity, govern — every action authorized
Context Channels, tools, observability — grounded and traceable
Corpus Your stack, memory, vault — the durable foundation

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.

Use cases

Agents for the work SMBs actually run.

We deploy practical agents on your stack — the same kinds of support, ops, chat-channel, and internal-tooling agents we already run for customers. Named logos stay off this page on purpose.

  • In production

    Support & chat-channel agents

    Agents that answer customers and staff on the channels you already use — Slack, Telegram, WhatsApp, Zalo, and more — with policy on every action.

    Faster first response, without handing the inbox to a vendor cloud.

  • Ops agents

    Agents that watch queues, tickets, and incident signals — then take the next safe step or escalate with a full audit trail.

    Less swivel-chair work; issues handled before they pile up.

  • Sales follow-up agents

    Agents that chase leads, draft replies, and keep the pipeline moving — under your identity and your rules.

    Follow-up that happens on time, without another SaaS seat.

  • Back-office agents

    Agents that file, route, summarize, and reconcile the repetitive work behind the counter.

    Hours back each week on the tasks nobody hired a person to babysit.

  • Data & internal-tooling agents

    Agents that query your systems, keep internal tools moving, and answer questions over the data you already have.

    Self-serve answers and automations grounded in your real stack.

  • New workflows as the business needs them — no fabricated product names.

Why SMBs

Most agent demos never become a reliable coworker.

Demos look promising. Production breaks them. For a small or mid-size team the gap is rarely the model — it is cost, control, and everything the agent has to touch: inboxes, tools, and the rules 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. a support agent that drafts a perfect reply in a demo — but cannot be trusted to act on a live customer channel.

    Source: Gartner, 2025
  • Ungoverned

    Agents call tools, send messages, and change records with no audit trail and no policy in the way.

    e.g. an ops agent that can refund, reopen, or overwrite a ticket, with nothing recording who asked or why.

  • Locked in

    Point integrations wire your agents to a vendor. Your conversations, data, and logs live on their infrastructure, not yours.

    e.g. a chat agent whose prompts, customer messages, and logs all flow through someone else’s cloud.

How it works

A runtime that sits between the agent and your systems.

  1. Policy

    Allow or deny

    Every tool call hits a policy decision first. Least-privilege rules decide what the agent may send, write, refund, or change — before it touches a live system.

    In practice An authorization check on the path, not a model hop and not a hope.

  2. Identity

    Who is acting

    Agents run as known identities with scoped credentials. Operators and bots get only the access they need, isolated per workspace.

    In practice You always know which agent acted, under whose authority, and with what scope.

  3. Observability

    Every call recorded

    Each invocation is traced — who, what, when, the policy decision, and the outcome. Optional OpenTelemetry export into the tools you already run.

    In practice A reviewable trail for support, ops, and compliance — not a black box.

  4. Self-hosted

    Your perimeter

    The runtime, models you choose, prompts, and logs stay on your infrastructure. GitOps manifests and Helm charts keep the fleet declarative and repeatable.

    In practice No vendor cloud, no shared tenancy, no data leaving unless you send it.

Deployed with GitOps and Helm on infrastructure you control — a VPS, a Kubernetes cluster, or the stack you already operate. We help you go from first call to a first production agent in weeks, not quarters.

What we build

The runtime, the control plane, the charts.

These are the systems we ship and run today. Public repos, no invented product line. Links go to source.

  • GoClaw

    Runtime

    Multi-agent gateway: teams, delegation, and orchestration in a single Go binary. Policy, identity, and OpenTelemetry on the path. Chat channels including Slack, Telegram, WhatsApp, and Zalo.

    github.com/dataplanelabs/goclaw

  • GCPlane

    GitOps

    Declarative control plane for GoClaw. Manage agents, providers, channels, MCP servers, and scheduled jobs as YAML — plan, apply, reconcile.

    github.com/dataplanelabs/gcplane

  • GoClaw Helm charts

    Deploy

    Kubernetes charts for the GoClaw server, UI, optional PostgreSQL, and the GCPlane control plane — the path we use to put a fleet on your cluster.

    github.com/dataplanelabs/goclaw-charts

  • miu-bot

    Framework

    Durable assistant framework with Postgres, Temporal workflows, multi-tenant isolation, and its own Helm chart. Built for long-running chat and ops agents.

    github.com/dataplanelabs/miu-bot

Also in the open: scry (an agent-drivable browser) and gws-cli (read-only Google Workspace skills).

Why this holds

Built so a small team can run agents in production.

  1. 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.

  2. Fast to stand up

    A single-binary runtime, Helm charts, and GitOps manifests — designed to get a first agent running on infrastructure you already have.

  3. On your infrastructure

    Self-hosted. Your data, models, and logs stay on your stack. No lock-in, no egress, no surprises.

  4. Affordable by design

    You pay for the compute you already run. No per-seat tax, no usage meter on every agent turn, no enterprise platform you do not need.

  5. Observable on every run

    Cost, latency, identity, and policy visible per call. Observability is built in — traces and metrics, not a bolt-on dashboard later.

Works with your stack

Bring your own everything.

No partnerships to sign, no platform to adopt. Agents and runtime plug into the tools an SMB already runs — at the category level, on your infrastructure.

  • Chat channels Slack, Telegram, WhatsApp, Zalo, Discord, Feishu — meet customers and staff where they already are.
  • LLM providers Hosted or self-hosted models — route to whichever you already use.
  • Tools & MCP Connect the APIs, browsers, and MCP servers your agents need — granted, not wide open.
  • Your existing stack Identity, observability, and compute 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 a real workflow — support, ops, sales, or back-office — with an engineer who builds these systems. No slides, just architecture.

Book a call

30-minute call · Your stack · No commitment

Why it holds

Built on a security-first, self-hosted architecture.

  • Security posture

    Zero-trust network model, encrypted at rest and in transit, RBAC with least-privilege defaults, full audit log per tool invocation.

  • Self-hosted

    Runs on your Kubernetes cluster or the compute you already operate. No vendor cloud, no shared tenancy, no data leaving your perimeter.

  • In production

    Not a research project. We run support, ops, chat-channel, and internal-tooling agents for customers on this same stack — with runbooks, not slideware.

Common questions

The questions operators 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. We do not publish a price list here. There is 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 tools, channels, and workflows 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 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

Honest claims. Nothing we have not earned.

100%
on your infrastructure
Every
tool call governed & audited
Zero
data egress to our systems
0
vendor lock-in

Ready to put an agent on your stack?

Tell us the workflow. We will show you where an agent fits — and how it stays governed.

Book a call