AI reliability engineering for production workflows

Make one consequential AI workflow ready to ship.

For product and engineering teams with an agent, RAG workflow, or AI-assisted feature. In five minutes, find the gaps; in as little as 48 hours, get a release decision your team can explain.

Get the free 5-minute gap profileSee the Reliability Sprint
5 min
Free gap profile
48h
Fastest paid decision
$4.5k
Focused reliability sprint

AI release gate

Customer Support Agent

Conditional release

Failure paths

03identified

Critical risk

Duplicate action after timeout

Missing controls

  • Idempotency boundary
  • Recovery state
  • Human approval boundary
Release decision with recovery ownership required

The cost of almost ready

Your AI is not failing in the demo. It is failing in the gaps around it.

The hard part is not making a model respond. It is knowing what happens when the context is wrong, the tool is slow, the user asks for too much, or nobody owns recovery.

  • Your AI feature works in a demo, but nobody can explain what happens when a tool call times out.
  • Launch is close, yet the evidence is scattered across prompts, logs, tickets, and team memory.
  • A customer could receive a wrong answer or duplicate action, and there is no clear recovery owner.
  • Your team keeps adding features because the real release decision still feels too risky to make.

Choose the smallest useful intervention

Match the work to the decision in front of you.

Each path names the outcome, the evidence you receive, and the handover your team keeps.

01

Review

02

Harden

03

Build

Abstract paper risks being surfaced, ranked, and turned into a clear release path

Selected artefact

Risk brief

01Launch decision

02Risk-ranked report

03Fix-first list

01 · Review

48-hour Launch Readiness Review

Find the gaps before real users do. Best when: You shipped quickly with AI coding tools and need a clear, plain-English answer on whether it is actually ready for real users.

In 48 hours, we exercise one critical journey and give you a plain-English go, conditional-go, or no-go decision with the fix order made clear.

$750 to $1,500 · 48-hour turnaround · One workflow in scope

See scope, outputs, and next step

Selected work

See the work we do.

Real work. Plain language. No inflated numbers.

Case study · 01Conversational AI

Operating system

AI operations

01 · Conversational AI

Omnichannel AI operations platform

One workspace to create, control, and run AI assistants across six messaging channels.

See all proof

Questions, answered

Clarity before commitment.

Know the work, cost, and next step.

Can you work with our team?

Yes. We can lead one part of the work or work beside your team. We agree roles, access, and what “done” means before we start.

Do we need a finished AI product?

No. Bring a real workflow and the data it uses. We can help before launch or make an existing feature safer.

How do you reduce bad answers?

We test common and edge cases, limit what the system can do, add safe fallbacks, and keep a person in control when risk is high.

What happens after the first call?

If it fits, you get a short scope with the work, timing, price, and result. You can say yes or no without a long contract.

Can you work with sensitive data and internal systems?

We scope access around the workflow, use the systems and data you approve, and make ownership and boundaries explicit before work starts.

Do you promise the AI will never fail?

No. The goal is a system that fails visibly, within defined boundaries, with recovery and human ownership where the stakes require it.

What do we receive at the end?

The exact outputs depend on the path: a readiness report, an evaluation and control plan, or a working integrated workflow with notes and handover.

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