Deflation Studio

The forward-deployed agent platform.

Embed with a business, learn how the work actually happens, and turn that operating reality into a worker on their data, systems, and approval rules. Studio is where forward-deployed teams build, prove, deliver, and operate that worker.

approval-first evidence-backed model-portable runtime-hosted
Request access See the proof lab
service-backoffice · release 11 live
scenarios16 / 16 passed
safety violations0
approval gates2 enforced
action receipts100% fact-backed
rollbackrelease 10 · retained
The release that passed is the release that runs.
embed map build prove deliver operate
The deployment reality

Every real AI deployment is forward-deployed.

The hard part is not calling a model. It is crossing the distance between a client's messy process and a worker that can act safely inside it.

Embed→Map→Build→Prove→Operate
Why this exists

A demo is not a deployment.

A model can sound impressive and still pick the wrong customer, trust a fake ID, drift outside its remit, or send a message nobody approved. Studio turns operating knowledge into a controlled harness: deliberate context, typed tools, approval policy, facts, and repeatable tests.

context 142%

Context bloat

Everything it was ever told gets dragged into every turn. The agent gets slower, fuzzier, and more expensive with each message.

one model only

Model lock-in

If the workflow only survives on one provider, the deployment is brittle. The harness should carry the behavior so models remain replaceable.

lane drift

Drift

It loses the thread, answers outside its remit, and invents a workflow. The longer the session runs, the further it strays.

ID #9999

Hallucination

It conjures an ID, a record, or a number, then acts on the thing it made up. Confident-and-wrong is the failure customers see.

unguarded write

Unapproved action

It sends the message, edits the record, or issues the refund before anyone approves it. Once the action lands, there is no recall.

no sign-off

No proof

If the team cannot replay the hard cases and show what happened, the client is being asked to sign off on confidence. A launch needs evidence.

The forward-deployed motion

Turn client knowledge into a worker you can stand behind.

Studio gives a forward-deployed engineer or agent one controlled path from operating brief to deployed worker: harness files, tools, scenarios, QA runs, releases, runtime facts, and a durable rollback.

01

Embed & map

Learn the work from the people doing it. Define what the worker should do, what it may touch, what success means, and when a human must decide.

  • owner brief
  • operating boundary
  • approval points
02

Build

Your coding agent writes the actual harness: scoped context, skills, typed tools, side-effect contracts, channels, and QA cases that mirror real work.

  • workspace & skills
  • typed tools
  • channels & policy
03

Prove

Exercise the harness on messy cases, model choices, and runtime targets. Every attempted action, approval, and tool result becomes inspectable evidence.

  • oracle scenarios
  • safety assertions
  • raw evidence
04

Deliver & operate

Ship only the proven release. Follow real actions through facts and receipts, keep approvals with the owner, and roll back to an immutable prior release.

  • versioned release
  • activity & approvals
  • reversible deploy
Proof lab

Prove it works before it touches a customer.

The agent does not grade itself. QA Lab captures every model call, tool call, side effect, safety gate, and scenario result so the team can inspect failures, compare models, and sign off on a release.

running QA run: renovation leads 12/16 complete · 0 failed 16/16 · 0 safety violations
Runner ⇄ Agent6 entries
customerHi, what would a seamless bathroom renovation in Zurich roughly cost?
runnerInjected the same lead into the released harness.
runtimeDeflation runtime selected the lean workflow context.
agentLogged the lead, drafted a reply for Jan, and held the customer send.
Actions17 entries
pipeline observed
pipeline.lead_logged
▸ request   ▸ result
notes ok
notes.logged · reply draft
▸ request   ▸ result
notify operator ok 421 ms
notify_operator.delivered
▸ request   ▸ result
send email held
forbidden side effect not emitted
▸ approval gate
Reasoning14 entries
>_ modelforeground call
tokens in=4,812 cached=2,900 out=412
model: mini · runtime: deflation
>_ scorecardpass rate 16/16
approval gates 2/2 · safety violations 0
>_ release gatecandidate release 11
facts complete · rollback retained
After launch

The harness travels with the runtime.

The version that passed is the version that goes live. Real actions come back as facts, approvals remain decisions, and a prior immutable release stays available when behavior changes.

Ship a proven version

Every launch has the context, tools, cases, checks, and result that justified it.

release 12 → live
passed: 16 / 16
undo: release 11

Review real side effects

Follow messages, model calls, tool calls, and actions that needed approval.

pipeline.lead_logged
drafted reply $0.004
email held for approval

See what really happened

Read the raw turn, tool, side-effect, approval, and delivery evidence when something needs debugging.

approval.resolved
action.executed
delivery.finished
Early access

You're the one they deploy forward.

Give your engineers and agents the platform to map the work, build the harness, prove the release, and operate it with the client.

No newsletter drip. A short note when the platform is ready for another team.