AI Strategy & Generative AI Development
AI Proof-of-Concept Development
A short, tightly scoped build that tests one AI idea on your real data, so you learn whether it works and what it would cost before committing to full development.
Overview
Why it matters
for your business.
Before funding a large AI project, you need evidence. Will the model read your invoices correctly? Can it answer policy questions without inventing things? We build a focused proof of concept around a single use case, using a sample of your real data, with success criteria agreed in writing before any code is written.
A good PoC is honest. We measure results against those agreed criteria, document where the approach struggled and estimate what production would require in effort, infrastructure and running cost. Engagements typically run a few weeks. If the idea does not hold up, you have learned that cheaply; if it does, the findings feed straight into a production plan.
What's included
- Written success criteria and test plan
- Working prototype on a sample of your data
- Evaluation report with accuracy and failure analysis
- Production architecture and running-cost estimate
- Demo session for stakeholders and decision makers
- Go, pivot or stop recommendation
Outcomes
What changes
for your business.
Ideal for
- Teams needing proof before board approval
- Startups validating an AI product idea
- Enterprises comparing AI approaches or vendors
Evidence-based decisions
Leadership decides on investment using measured results from your own data rather than vendor demos.
Contained spend
A small, time-boxed budget tests the riskiest assumptions before larger commitments are made.
Faster path to production
Lessons, code and architecture from a successful PoC shorten the full build that follows.
How we deliver
A clear, accountable
process.
Every engagement follows the same disciplined approach, adapted to the size and urgency of your project.
- 01
Identify
We find the use cases and processes where AI or automation clearly pays off, and estimate the impact.
- 02
Prototype
A proof of concept on your own data shows what works before you commit to a full build.
- 03
Deploy
We integrate with your systems, add guardrails and human review, and train your team.
- 04
Monitor & improve
Accuracy, cost and usage are tracked, and the solution is tuned as your data and needs change.
Ways to work with us
- Fixed-price projectsDefined scope, milestones and deliverables.
- Implementation & integrationOne-time technology deployment and integration.
- Time & materialFlexible delivery based on actual effort and resources.
- Monthly managed servicesRecurring application, IT, marketing or infrastructure support.
FAQ
Common
questions.
Something else on your mind? Ask us directly.
Is the proof-of-concept code reusable for the production system?
Partly. Prompts, data pipelines, evaluation sets and core logic usually carry forward. The prototype interface and shortcuts taken for speed are normally rebuilt with proper security, error handling and scalability. We state clearly in the report which parts are production-ready and which are not.
How much data do we need to share for a PoC?
Usually a representative sample is enough, for example a few hundred documents or conversations covering common and tricky cases. We can work on anonymised or masked data, sign an NDA, and process everything within your environment if that is a requirement.
Let's talk about AI Proof-of-Concept Development.
Tell us what you need and get a clear plan and proposal, free and with no obligation.