Control & Sovereignty | Pillar 3 of the platform

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Your proof stays
inside your walls.

You’re accountable for the release. So the verdict you sign off on, and the evidence behind it, are computed inside your perimeter by code with no model in the path. Reproducible run to run, defensible to an auditor, and never sitting on a vendor’s servers.

Schedule a Demo See the business case
Self-hosted, air-gapped,
BYO-LLM by design.

One Docker container on your infrastructure. Bring any model: cloud, approved, or fully local via Ollama. No outbound calls, no telemetry, no hosted control plane, and the deterministic core runs with zero AI at all.

Self-hosted AI testing See how it works

What stays provably yours

  • The ship verdict

    READY, CONDITIONAL or NOT-READY, computed by code

  • The evidence

    Traceability and coverage, computed inside your perimeter

  • Reproducible

    Run it twice with the network off, same answer

  • Defensible

    A no-AI report you can hand an auditor

Your proof, your perimeter, no vendor in the path

What stays inside your walls

  • Your data

    No egress, no telemetry, ever

  • Your models

    BYO-LLM, including fully local

  • Your network

    One Docker container, air-gap ready

  • Your budget

    Token cost capped · ~$0 replay

Local-first by design, not a deployment option

No outbound calls.·No telemetry.·No hosted control plane.

Every component runs inside your perimeter. We don’t know what you’re testing, unless you tell us.

The business case

The proof is yours
to keep.

You’re the one who signs the release. So the verdict and the evidence behind it are computed inside your walls, by code, and reproduce exactly. Nothing you stake your name on lives on someone else’s servers.

Your evidence never leaves

Requirements, traceability, and coverage are computed inside your perimeter. The record you sign off on lives in your own git, not a vendor’s cloud, so what you approve is yours to keep.

A verdict that reproduces

Confidence-to-ship is computed by code with no model in the path. Run it twice with the network off and you get the same answer, down to the same hash. No black box to second-guess.

Defensible when someone asks

A reproducible, no-AI report ties every requirement to the runs that proved it. When an auditor asks which rule you proved and where the evidence is, the answer is a report, not a shrug.

I’m the one who signs the release. I can’t stake my name on evidence I can’t reproduce, or that lives on a vendor’s servers I don’t control.

— What product leaders in regulated firms tell us

You feel this when…

  • You sign off on releases in a regulated firm (finance, insurance, healthcare)
  • The evidence auditors want cannot leave your network
  • “Prove it” has to mean reproduce it, not screenshot a green dashboard
  • Approved-model-only and data-residency rules disqualify most AI SaaS
  • The proof has to outlive any one vendor

Your proof. Your perimeter. Computed by code. The agent does the labor; a deterministic check inside your network grades every claim and issues the verdict. Nothing leaves, and nothing ships on a claim.

Inside your walls, on your terms

No egress, no telemetry • BYO-LLM (approved or fully local) • ~$0 deterministic replay • Scale by the container, not the seat

Under the hood

One container. Your network. Your model.

What leaves your network: nothing. The entire pipeline (browser, agent, model) runs where you put the container.

Cloud AI testing SaaS

  • •Sends your code, screens & data to vendor servers
  • •Locked to the vendor’s models & token markup
  • •Disqualified by security in regulated shops

Karate, self-hosted

  • •One Docker container on your infrastructure
  • •BYO-LLM: cloud, approved, or fully local via Ollama
  • •Deterministic core runs with zero AI; air-gappable
# everything runs where you put the container
docker run -p 8080:8080 \
  -e LLM_ENDPOINT=http://ollama.internal:11434 \   # your network
  -e LLM_MODEL=gemma4 \                              # open-weight, local
  karatelabs/karate-agent

# no outbound calls  ·  no telemetry  ·  no keys leave your VPC
Self-hosted & air-gap → Bring your own LLM → Security & compliance →

How we keep it inside your walls

Three guarantees, one perimeter

One self-hosted container

The whole pipeline ships as a single Docker image you run on your infrastructure: CI, cloud, or fully air-gapped bare metal. The unit of scale is a box, not a seat.

Self-hosted AI testing

Bring your own LLM

Point it at your approved cloud model, or self-host Gemma 4 or Qwen 3.6, two benchmark-verified open-weight models that each run on a single 24 GB GPU. DOM-first means small, local models work, so you don’t need a frontier vision model or a vendor’s token markup.

Bring your own LLM

Air-gap & compliance ready

Self-hosted, air-gapped, audit logs, and a reproducible no-AI report: the controls procurement and security ask for. Local-first, with nothing phoning home.

Security & compliance

Use cases

Built for the rooms that can’t use SaaS

Regulated

Finance, insurance, healthcare

Data-residency and audit obligations met by design: the stack runs inside your network with reproducible evidence.

 Approved models

Copilot / Azure shops

Already standardized on an approved model? Point Karate at it: no new vendor in the data path, no second AI bill.

 Air-gapped

On-prem & offline

Run the whole pipeline on bare metal with a local model: zero external calls, even during heavy regression.

Run it inside your perimeter.

Deploy the container in your environment, point it at an approved or open-weight model, and watch the token meter: nothing leaves your network.

Schedule a Demo Self-hosted AI testing →