AI PoC & MVP

Validate your AI idea fast.Ship what works.

We build AI-powered proof of concepts and MVPs on real data, giving you a clear answer on what works before you commit a full engineering quarter to it.

See Case Studies
Fixed-Price DeliveryFull IP OwnershipGo/No-Go Report Included12-Month Warranty

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PoC delivery target

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Faster validation vs. in-house

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Higher investment confidence

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Bug-free warranty

Engagement Types

PoC or MVP — which do you need?

Both are fixed-price and time-boxed. The difference is the outcome: one validates an idea, the other ships it.

PoC

Proof of Concept

3–5 Weeks

Confirm that your chosen AI approach actually works before committing to a full-scale build.

Best for: Teams sitting on an AI hypothesis that needs to be de-risked before presenting to the board or unlocking engineering budget.

What's included

  • Working demo tested against representative data
  • Architecture & model selection with written rationale
  • Performance benchmarks measured against agreed success criteria
  • Feasibility report containing a clear go/no-go recommendation
  • Full codebase handover or a detailed integration brief

Not included (added in MVP)

  • Production infrastructure
  • User auth & admin
  • Full error handling

MVP

Minimum Viable Product

6–10 Weeks

Deliver a production-grade AI product that real users can access, test, and provide feedback on from day one.

Best for: Organisations that have validated their AI concept and are ready to onboard early users and iterate on real-world feedback.

What's included

  • Production deployment on your cloud environment or ours
  • User authentication, role management, and access controls
  • Live monitoring, structured logging, and full observability
  • CI/CD pipeline with automated test coverage
  • Human-in-the-loop review workflows where the use case requires them
  • Complete technical documentation and handover session

What We Build

Six AI categories we PoC and ship

Each engagement produces a working, benchmarked system — not a slide deck or a proof of architecture.

LLM

Conversational AI & Copilots

Develop chat interfaces, domain-specific copilots, and multi-turn assistants grounded in your own knowledge base through RAG, fine-tuning, and tool integration — stress-tested against real user queries before you commit to scale.

Document AI

Document Intelligence

Pull structured information out of unstructured documents — contracts, invoices, clinical records, and reports — with high extraction precision and a human review layer for anything the model flags as uncertain.

Vision AI

Computer Vision

Prototype image classifiers, object detectors, defect inspection pipelines, and visual search systems — trained on your own dataset and benchmarked for accuracy before you move toward full productionisation.

ML

Predictive Analytics & Forecasting

Build and validate demand forecasts, churn models, risk scores, and anomaly detectors against your historical data — with explainability outputs that give stakeholders confidence in the predictions.

Personalisation

Recommendation Engines

Prototype collaborative filtering, content-based, and hybrid recommendation systems for products, content, or services — evaluated directly on click-through rates and conversion lift against a baseline.

Agents

Autonomous Agent Workflows

Design and validate multi-step agentic pipelines that reason across data, invoke tools, and act across connected systems — proving the core workflow logic in a controlled environment before any production rollout.

Delivery Process

How we run a PoC engagement

Four phases, five weeks, one clear answer — does this AI approach work for your problem?

01

Scoping & Success Criteria

Week 1

We facilitate a structured scoping session to pin down the core hypothesis, agree on what measurable success looks like, map data requirements, and lock in the model and architecture direction.

Problem statement docSuccess metrics definitionData readiness checklistArchitecture decision record
02

Core Build

Weeks 2–3

We build the working AI system against your actual data — wiring up the models, developing the interface or API surface, and configuring the evaluation harness that will track performance throughout.

Working prototypeEvaluation harnessIntegration layerDaily async updates
03

Validation & Iteration

Week 4–5

We run the system against the agreed success criteria, collect stakeholder observations, and iterate quickly on what needs to change. Edge cases and failure modes are catalogued alongside the final performance baseline.

Performance benchmark reportEdge case documentationStakeholder demo sessionIteration log
04

Handover & Roadmap

Week 5–6

We hand over the complete codebase with documentation, deliver a written go/no-go recommendation, and — if the PoC passes — provide a production roadmap so the transition to MVP can begin immediately without rework.

Codebase + READMEGo/No-Go reportProduction roadmapCost & timeline estimate

Industry Applications

What we've prototyped by vertical

Every industry has different data types, compliance requirements, and success metrics. Select yours to see what we've built and can validate.

AI Validation for Financial Products

Validate credit or fraud models in 4 weeks

Financial institutions use PoCs to de-risk AI investments before compliance reviews, infrastructure changes, or market commitments. We build benchmarked demos your risk and tech teams can interrogate.

PoC & MVP ideas we can validate

  • Fraud detection model on transaction history
  • Credit scoring with explainability outputs
  • KYC document extraction and verification PoC
  • Personalised financial product recommendation engine
  • Trade surveillance anomaly detection prototype
  • Automated regulatory report generation MVP
  • Conversational financial advisory copilot

Technology

Model-agnostic, framework-agnostic

We select the right model and framework for your specific problem — not the one that's easiest for us to use.

OpenAI
OpenAI
FastAPI
FastAPI
Next.js
Next.js
Node.js
Node.js
TypeScript
TypeScript
PostgreSQL
PostgreSQL
Supabase
Supabase
Tailwind
Tailwind
OpenAI GPT-4oAnthropic ClaudeGoogle GeminiGroqLangChainLangGraphCrewAIRAG PipelinesPineconeWeaviatePyTorchHugging FaceAWS BedrockAzure OpenAIVercel AI SDK

Fixed timeline

PoCs in 3–5 weeks. MVPs in 6–10. We agree the deadline before we start and hit it.

Full IP ownership

You own all code, models, and data pipelines from day one. No lock-in, no licensing fees.

Clean handover

Every engagement ends with documented code, a README, and a technical handover session.

Production-ready path

PoCs are scoped so the code is reusable — moving to MVP doesn't mean starting over.

FAQ

Frequently asked questions

Questions we get before almost every PoC engagement — answered honestly.

A PoC answers one question: will this AI approach work for our problem? It's a time-boxed build — usually 3–5 weeks — that delivers a functional demo validated against pre-agreed success criteria. It's not production-ready. An MVP is: it handles real users, includes authentication, error handling, and observability, and can be deployed. If the PoC passes, the path to MVP typically takes 6–10 additional weeks.

Not necessarily. Week one begins with a data readiness check. If your data is accessible and in reasonable shape, we move straight into the build. If there are gaps or quality issues, we identify the minimum dataset required and either use representative synthetic data for the PoC phase or guide you through preparing a working subset. Full data pipelines are scoped and built at the MVP stage.

A PoC that returns a 'no' is still a valuable outcome — it protected you from spending significantly more on a build that would have disappointed. We provide a clear, written go/no-go report covering what was tested, why the approach didn't perform as expected, and what alternative directions are worth exploring. Many clients pivot to a stronger solution rather than shelving the idea entirely.

You do. Full IP and codebase ownership transfers to you upon final payment. You receive the complete repository, all documentation, and a live technical handover session. Whether you take it in-house or pass it to another development team, we make sure the transition is clean and well-documented.

All PoC and MVP engagements are fixed-price with a clearly scoped deliverable list agreed before work starts. You have full cost certainty from day one. If requirements evolve during the build, we discuss scope changes openly before proceeding — no hidden charges or end-of-month surprises.

We make technology choices based on what the problem calls for, not preference. For high-reasoning tasks we typically reach for GPT-4o; for long-context document processing, Claude; for multimodal inputs, Gemini; and for data-sensitive or cost-constrained scenarios, open-weight models like LLaMA or Mistral. On the orchestration side we use LangChain, LangGraph, CrewAI, and custom RAG pipelines depending on the architecture.

Not much, but the right moments matter. We need a 1-hour kickoff call, access to your data, and one domain expert reachable for questions over async chat. We schedule a midpoint demo around weeks 2–3 and a final walkthrough at delivery. Between those touchpoints we work autonomously and send you written progress updates daily.

The complete codebase with a README and setup instructions, architecture documentation explaining the decisions made, a performance benchmarking report against the agreed success criteria, a written go/no-go recommendation with the reasoning behind it, and a production roadmap with time and cost estimates if you choose to proceed to MVP or wider deployment.

Ready to validate?

Get a clear answer on your AI idea within 5 weeks

Fixed-price, time-boxed, and closed out with a written go/no-go report. You leave knowing exactly what to build next — or which direction to rule out entirely.

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