Time to production
Days to a working prototype, weeks to a live system. Every milestone is dated.
PROOF, NOT PROMISES
The work is the argument.
We measure ourselves the way your board does. Time to production, cost removed, revenue moved, risk reduced.
How we measure
What your board measures.
Cost removed
Cost per case, per claim or per contact, measured before and after on your own numbers.
Revenue moved
Conversion, retention and throughput, tracked in the metric your finance function already owns.
Risk reduced
Errors caught, exposure lowered and audit coverage, evidenced in production.
Representative engagements
01
Financial services
- Challenge
- First notice of loss spread across channels and days.
- Deployed
- An agent that reads, classifies and routes every claim, escalating the uncertain ones to a human.
- Outcome
- Touch time cut and the backlog cleared, under a full audit trail.
02
Manufacturing
- Challenge
- Contract intake to purchase order took weeks across departments.
- Deployed
- Clause extraction, supplier scoring against history and a drafted negotiation brief.
- Outcome
- Cycle time compressed, buyers approving, the client team operating it.
03
Telecom
- Challenge
- Routine contacts overwhelming a tier-one queue.
- Deployed
- An agent resolving routine cases end to end against a satisfaction bar.
- Outcome
- Deflection up, operators freed for the complex work.
Representative scenarios. Specific clients and numbers are shared in a working session, under NDA.
The pattern behind the numbers.
Every engagement starts with one workflow, reaches production fast, and hands over cleanly. That is why the results hold after we leave.
Shared under NDA, not on a public page.
Specific clients, systems and numbers belong in a working session, not on a landing page. NDA-first is how we operate. Bring your hardest workflow and we will show you comparable results in context.
Measured the way your board measures.
San Francisco · IstanbulQuestions
Common questions
How do you measure the success of an AI deployment?
The way your board does: time to production, cost removed, revenue moved, risk reduced. Measured before and after, on your own numbers.
How do enterprises move AI pilots into production?
By treating deployment, not the model, as the product: one workflow, real data, hard constraints, evaluations and a rehearsed go-live. That is the pattern behind every engagement we run.
Who can help scale an AI proof of concept to production?
A production AI deployment partner that builds inside your operation. That is what we do: we take stuck pilots, harden them against real constraints and carry them live.
Why are there no client logos on this page?
NDA-first is how we operate. Specific clients, systems and numbers belong in a working session, not on a landing page.
Can we get references?
Yes. In a working session, under NDA, we share comparable engagements and numbers, and direct references where clients agree.
What is a typical time to production?
Days to a working prototype, weeks to a live system. Every milestone is dated, so the timeline is evidence, not a promise.
Do the results last after you leave?
Yes, by design. Every engagement hands over cleanly to your team, with the skills and the operating playbook in-house. That is why the results hold.
What outcomes should we expect in the first quarter?
One workflow in production, measured before and after on cost, revenue or risk, and an in-house team that operates it.
How are you different from the best-known forward deployed engineering firms?
We stayed independent. Most top deployment teams now belong to a model lab or platform. We are model-agnostic and loyal only to your outcome.
What does an engagement cost?
It is scoped in the working session, structured so you pay for production, not slideware. A bounded first workflow keeps the entry commitment small and the payback near.
The independent forward-deployed AI partner