AI PoC Development
Most AI pilots die at the demo and get thrown away. We build the proof of concept on the same foundation the production system needs - evaluation, write-back, guardrails - so the thing that proves your use case is the thing you ship.
Got it — thanks!
A senior engineer will review this and reply within one business day.
What AI PoC Development Covers
Workflow scoping
We pick the workflow with the clearest baseline and the highest cost-of-manual, so the PoC proves something that matters.
Evaluation harness
A scenario suite scored against your current baseline - the PoC passes or fails on a number, not on a good demo day.
Real-data build
Built on a slice of your actual data and wired to the systems it would touch in production, not a sandbox with clean inputs.
Production path
The same architecture the production system would use, so a successful PoC extends into the real product instead of being rebuilt.
What "Done" Means in Production
- An evaluation harness scoring the agent against a real baseline, not a demo script.
- Write-back into at least one real system of record, not a read-only mockup.
- Monitoring and an audit log so failures in production are diagnosable.
- Cost controls and a fallback path wired in before launch.
- A documented rule for where the agent stops and a human takes over.
Built for Specific Buyers, Not Everyone
CTOs proving a use case
Need evidence the AI use case works before committing a production budget.
Founders under board pressure
Have to show a working AI capability, not a slide, and cannot afford a pilot that gets thrown away.
Product leads scoping AI
Know the outcome they want but need a partner to define the actual system and its baseline.
If you want a flashy demo to show investors and never intend to ship it, we are the wrong partner - our PoC is engineered to become the real product, which costs a little more up front and saves a rebuild later.
From Kickoff to Launch
Scoping & baseline
We choose the workflow and define the metric the PoC will be judged against.
→ Scoping doc + agreed baselineReal-data build
A working slice on your actual data, wired to the systems it must touch.
→ Working PoCEvaluation
Scored against the baseline on a scenario suite, with the failure modes documented.
→ Evaluation reportProduction recommendation
A clear go / no-go with the architecture and cost to take it further.
→ Recommendation + architectureTechnologies We Use
Where This Does Not Apply
- You need a throwaway investor demo, not a system - a cheaper agency will build that faster.
- The workflow has no measurable baseline and you are not ready to define one - there is nothing to prove the PoC against yet.
- A deterministic rules engine would solve it outright - we will tell you that on the call rather than sell you an agent you do not need.
Common Questions About AI PoC Development
What is an AI PoC, and how is it different from a demo?
A demo shows that something can work once, usually on hand-picked inputs. A PoC proves whether it works on your real data against a metric you agreed up front, and it is built on the same foundation the production system would use - evaluation, write-back, guardrails - so it becomes the real product instead of being rebuilt. The difference is whether the thing survives contact with your actual workflow.
How long does an AI PoC take?
A focused PoC on a single well-scoped workflow typically runs a few weeks, ending in a working slice and an evaluation against your current baseline. The range depends on data readiness and how many systems it has to touch, which we map during scoping before committing to a timeline.
Why is there no price on this page?
Because the honest price depends on the workflow, your data, and the systems it integrates with - and a number on a page would be a guess. The PoC is deliberately the small, scoped first step so you can prove the use case before committing a larger budget; we scope it on a call, in time and outcome.
What do we get at the end of the PoC?
A working AI system on your real data, an evaluation report against the baseline, the architecture, and a clear recommendation on whether and how to take it to production. If the honest answer is that it should not go to production, you get that too - a PoC that fails cheaply is a successful PoC.
Do we own what you build?
Yes. You own the code, the infrastructure it runs on, and the evaluation artefacts. There is no proprietary framework you cannot hire for later and no lock-in to a platform you do not control.
What makes a workflow a good PoC candidate?
High volume, clear rules, a measurable baseline, and a real cost to the current manual approach. Reconciliation, document review, support triage and after-hours call handling are strong candidates. If a workflow has no measurable baseline, we will help define one first, because you cannot prove a PoC against a metric that does not exist.