AI proof of concept company in London

An AI proof of concept should answer one question on your real data, then stop. Go live, iterate, or kill it. As an AI proof of concept company, we write the PoC so the code can become the product if the answer is yes. If you already know it works, skip this page and go to a build.

AgentWise started as an AI PoC. We will also tell you to stop when the proof of concept says don't build. That is the point of paying for one.

Book 45-min discovery call →see our ai work →References available

Built to decide go or no-go
Production architecture from day one
Fixed scope, fixed price, fixed timeline
Honest go / no-go with evidence

4.9/5 on Google

See our reviews on Trustpilot

5.0/5 on Clutch · 10 reviews

Shipping AI POCs and production builds for scaleups and enterprises across the UK, Europe and the US since 2013.

London team. The PoC code is meant to survive, not go in the bin.

/ Deliverables

What the POC Actually Answers

The One Question Worth Answering

Most failed POCs answer the wrong question. 'Can the model do X?' is rarely the right one. The right ones tend to be 'will this work on our messy real data?', 'can we afford to run it at our volume?', 'will it pass Legal and Security?', 'does it actually move the metric our board cares about?'. We define the question with you in the scoping call, in writing, before the POC starts.

If we can't write the question down in one sentence, the POC isn't ready to start.

Question definition

Scoped hypothesis

Board-ready output

Written upfront

Production-Minded Architecture From Day One

Demo PoCs look great and go nowhere. We build the POC on production architecture: real auth, real data layer, real evaluation harness, real cost telemetry, real fallback paths. Different scope from a full production build, but the same architectural shape. If the answer is yes, the code carries forward into the build. No 'phase two starts from scratch' tax.

The gap between a demo agent and a production agent is architecture, not intelligence.

Production architecture

Carries to build

No rewrite

Real auth and data

Data Readiness Validation

Most POCs fail on data, not models. We test on your real data, not a curated demo set: messy formats, stale records, missing fields, edge cases, the same customer represented four ways across four systems. We surface what's clean, what's recoverable, and what needs to be fixed before any production build can succeed.

We have told clients to fix the data warehouse before funding the model. That's the audit doing its job.

Real-data testing

Quality profiling

Edge-case surfacing

Honest data report

Integration Test on Your Real Stack

PoCs that skip integration are demos. We test the AI against the systems it will actually have to talk to in production: your CRM, ERP, helpdesk, billing, custom APIs, IAM, rate limits, audit logging. Surface integration friction in week three, not month three of a build.

Most enterprise AI POCs don't fail at the model. They fail at the integration boundary. We start there.

Real systems

Auth and rate limits

Integration friction

IAM propagation

Cost Model at Production Volume

A POC that works at 100 calls a day can be uneconomical at 100,000. We project the run-rate at your real volume: inference cost, model routing savings, prompt caching, fallback overhead, retry amplification. The number lands in the report before any production budget is committed.

A £50,000 pilot can become a £200K-a-year run-rate by year three if the cost model is wrong. We model that up front.

Run-rate at scale

Inference economics

Model routing

Total cost of ownership

Architecture Recommendation and Trade-offs

The POC report includes the architecture we'd build if you greenlit production: model choice with rationale, retrieval pattern, integration approach, fallback design, monitoring stack, deployment topology. Trade-offs are written down: cheaper vs faster vs more accurate, on-prem vs hosted, self-hosted vs API.

You can hand the document to your internal team or another vendor and they can execute it. The report is written to be useful, not just persuasive.

Architecture proposal

Trade-offs documented

Executable plan

No vendor lock-in

Go / No-Go Decision With Evidence

The POC ends with a clear recommendation: build, iterate, or walk away, backed by the metrics from your real data and your real stack. Not a vibes summary. Not a polite hedge. A defensible call your board can act on.

We've recommended 'don't build' more than once. The POC pays for itself either way: it either becomes the production system or saves you the cost of building the wrong thing.

Go / no-go

Evidence-based

Board-ready

Honest disqualification

Code, IP and Path to Production

You own everything we deliver in the POC. Source code, prompts, evaluation datasets, infrastructure-as-code, monitoring scaffolding, the technical report and the architecture proposal. If the answer is yes, the POC code carries directly into production with the same team. If the answer is no, you keep the report and the data we surfaced. No rented layer, no vendor lock-in.

Full IP transfer

POC to production path

Same team carries on

No lock-in

When a PoC is worth doing, and when to skip it

A hackathon demo is not a PoC

If success criteria are "it kinda works on stage", you will learn nothing. We define what must be true on real data before anyone talks about a full build.

Use private data early, carefully

Synthetic demos hide the hard problems. We work with representative data under sensible controls so the PoC fails or passes for the right reasons.

Exit criteria up front

Go / no-go, cost of the next phase, what we will not build yet. The point is a decision, not a souvenir chatbot.

Hire a designer instead if…

For an investor demo, hire a designer. For a production decision, hire people who will also tell you to stop.

Related AI cluster services

AI readiness audit→AI consultancy→AI development services→AI agent development→RAG development→AI integration services→

How the PoC runs

Scoping Call: Define the One Question (free, 45-min)

Before any contract is signed we sit down with you and write the POC question in one sentence: 'Can [specific AI capability] reach [specific accuracy or business metric] on [specific dataset] inside [specific cost envelope]?'. We surface the assumptions, the data the POC needs, the systems it has to integrate with and the threshold for go/no-go.

If the question can't be written in one sentence, we won't take the engagement.

POC Build (Weeks 1-4)

Build runs as a small senior team led by Michal Vavra. We stand up the POC on production architecture against your real data: data ingestion, model and prompt or pipeline, evaluation harness, integration scaffolding, cost telemetry, monitoring stub. We run the system on your real corpus or traffic and measure against the metric defined in scoping.

Code is yours from day one. Same engineers will carry it into production if you greenlight.

Production Hardening Test (Weeks 4-6)

We run the system against the failure modes that usually kill production AI: edge cases in real data, integration friction with your stack, cost at projected volume, latency budget, security and compliance review. Failures here are the point. They're cheaper to find now than at month four of a build.

Output: an evaluation report sliced by data type and condition, an integration friction list, a projected run-rate at production volume, and a list of the specific risks that would need to be designed against in a production build.

Decision and Path to Production

We deliver the POC with a clear go / no-go recommendation backed by evidence, the architecture we'd build for production, a fixed-scope quote for the build, and the projected monthly run-rate. If the answer is yes, the same team takes the POC code into the production engagement. If the answer is no, you keep the report and walk away with a clear understanding of why and what would have to change.

Most clients move into a production build inside a fortnight. Some take the report and execute internally. Either is fine.

What an AI proof of concept costs

POC cost depends on data complexity, integration surface, regulatory exposure and the depth of validation required. Scoping call is free. Fixed-price POCs typically run £8,000-£30,000 and complete in four to six weeks. The POC code carries directly into the production build, so you don't pay for the same architecture twice. Production builds typically start at £25,000 and scale with scope.

Scoping Call (free, 45-min)

We define the POC question in one sentence, surface assumptions and confirm fit. No contract.

Light POC (from £8,000)

Single feature, single data source, real-data validation, evaluation report. 2-4 weeks.

Standard POC (typically £15,000-£30,000)

Real data plus real integration, cost model at production volume, hardening test. 4-6 weeks.

Path to Production Build

POC code and architecture carry forward. Production builds typically £25,000+, fixed-scope after the POC.

Book 45-min scoping call →Request POC pricing detail

Frequently Asked Questions

Direct answers to the questions CTOs and Heads of Product ask us on every POC scoping call.

How much does an AI proof of concept cost?

Light POCs (single feature, single data source) start from £8,000 and run two to four weeks. Standard POCs with real-data testing, integration on your stack and a cost model at production volume typically land £15,000-£30,000 over four to six weeks. Fixed scope, fixed price, fixed timeline. Scoping call is free, no contract until we've written the POC question down together.

How long does a POC take?

Most engagements run four to six weeks from kickoff to a delivered report and working system. Light POCs can land in two to three weeks where the data layer is already clean and the integration surface is small. The hardening pass at the end (weeks four to six) is where we surface the failure modes that usually kill production AI. We don't recommend skipping it.

What's the difference between a POC and an MVP?

POC answers a feasibility question: will this AI approach work on our real data, in our real stack, at our cost? MVP delivers a working system to real users at small scale to validate willingness to pay or operational fit. POC is for the build/no-build decision. MVP is for the pilot/scale decision. They're stages on the same path. We do both, and we'll tell you in scoping which one your situation actually needs.

What if the POC shows AI won't work for our use case?

That's a successful POC. The point is to find out in weeks, not months. We've delivered POCs where the recommendation was 'don't build the AI feature, fix the upstream data layer first' or 'a rules engine would be cheaper and more reliable here'. You walk away with a defensible technical report, the architecture we'd have built, and clear evidence for the call. The POC paid for itself by avoiding a much larger mistake.

Do we own the POC code? Can it go to production?

Yes to both. Standard contract is full IP ownership for the client: source code, prompts, evaluation datasets, infrastructure-as-code, monitoring scaffolding, the technical report and the architecture proposal. The POC is built on production architecture (different scope, same shape) so the code carries forward into the production build. Same team, no rewrite, no 'phase two starts from scratch' tax.

How do you handle data privacy during the POC?

We sign an NDA before any data leaves your environment. Where data residency or compliance requires it, we run the POC inside your VPC or on-prem rather than on our cloud. We support Azure OpenAI with zero retention, private deployments on AWS and GCP, and self-hosted open-source models. UK GDPR, DPA 2018 and sector frameworks (FCA SYSC, NHS DSPT, PCI DSS) are covered as standard.

When should we skip the POC and go straight to building?

Skip the POC if you've already validated the use case on real data, the integration surface is well understood and the cost model is clear. In that case the POC adds time without adding evidence. We'll say so in the scoping call and point you at our AI Development Services instead. We'd rather not run a POC than run one that doesn't earn its place.

Related AI work

If you already know it works, skip the PoC and go to AI development services. If you do not know whether the data is usable, do a readiness audit first.

AI development company→AI readiness audit→AI consultancy→