AI PoC development
AI proof of concept development for a clear next decision
Softblues builds focused AI prototypes to test whether an approach works on your data and tasks. We agree the question, scope and evaluation criteria before building, then document the results, limitations and work needed for the next stage.
Paid, scoped experiment · Evaluation against agreed criteria · Production work scoped separately
- OpenAI Select Partner
- Google Cloud Services partner
- Anthropic Partner Network member
- Member of the Microsoft AI Cloud Partner Program
When is an AI proof of concept useful?
A PoC is useful when a technical uncertainty stands between you and an investment decision. It might test document extraction quality, whether a knowledge source supports useful answers, or whether an agent can complete a bounded workflow.
If the main uncertainty is which business process to change, start with discovery. If an existing platform already performs the task well, a configuration pilot may be more useful than a custom build.
How do we develop and evaluate the prototype?
Four steps, each one producing something the next decision needs. The schedule is agreed after scoping.
Discovery and scoping
Define the task, relevant users, constraints and the decision the PoC should inform. Agree criteria for proceeding, changing approach or stopping.
Data preparation and architecture
Assess representative examples, access and quality. Select a suitable model and approach; custom model training is only considered where needed and explicitly agreed.
Build and iterate
Implement the bounded functionality and a practical way to test it. Keep known limitations visible as the prototype develops.
Validate and report
Evaluate against the agreed cases, including failures and review effort. Record results and propose the next step.
Data access, integrations and evaluation depth affect the work. A prototype timetable is not a production-launch commitment.
What is included in your AI PoC?
Four deliverables, defined before the work starts so you can tell whether you received them.
- An agreed experiment
- A defined question, scope, inputs and evaluation criteria.
- A working prototype
- The functionality needed to test that question, with the interfaces and integrations included in the scope.
- An evaluation record
- Results on representative tasks, observed limitations and the checks still needed.
- A next-stage recommendation
- Proceed, revise or stop, with remaining production requirements made explicit.
The proposal specifies documentation, access to project code and the applicable ownership and third-party licence terms. User testing is included where it is part of the agreed evaluation scope.
PoC, pilot or production system?
Three different questions, three different kinds of evidence. Knowing which one you are commissioning is most of the decision.
| Stage | Main question | What it establishes |
|---|---|---|
| Proof of concept | Can this approach perform the bounded task? | Technical evidence and limitations under the tested conditions |
| Pilot | Can people use it in a controlled real workflow? | Operational fit, adoption, review effort and exceptions |
| Production | Can the agreed service be operated and maintained? | Release controls, support ownership, monitoring and accepted operating requirements |
A successful PoC supports a decision about further work. It does not automatically establish production readiness, adoption across the business or financial return.
What can we test?
Four kinds of uncertainty a bounded experiment can resolve, each evaluated against agreed examples rather than a demonstration.
Which technology should the PoC use?
We choose the model, knowledge retrieval, integration and hosting approach around the experiment and your existing systems. The options include OpenAI, Anthropic, Microsoft and Google Cloud. The aim is to test the decision with enough engineering to make the findings useful.
The AI proof of concept: at a glance
- What it is
- A paid, scoped experiment that tests whether an approach works on your data and tasks, with the question, scope and evaluation criteria agreed before building.
- What you receive
- An agreed experiment, a working prototype, an evaluation record covering results and observed limitations, and a recommendation to proceed, revise or stop.
- Evaluation
- Against the agreed cases, including failures and the review effort the output requires. Results, limitations and remaining checks are recorded.
- Model training
- Custom model training is considered only where it is needed, and it is explicitly agreed rather than assumed.
- Timetable
- Agreed after scoping. Data access, integrations and evaluation depth affect the work, and a prototype timetable is not a production-launch commitment.
- Price
- Quoted against the experiment, data preparation, integrations and evaluation. The price and deliverables are agreed before work starts. GBP.
- What it does not establish
- Production readiness, adoption across the business or financial return. Those are separate decisions with their own scope.
Common questions about a proof of concept
How much does an AI proof of concept cost?
The quote depends on the experiment, data preparation, integrations and evaluation. We agree the price and deliverables before work starts. Our pricing page sets out the cost categories and the next step.
What if the PoC shows the idea will not work?
That is a useful finding when it prevents an unsupported production investment. The report explains what failed, what remains uncertain and whether another approach is worth testing.
Do I need to have my data ready?
You need a route to suitable examples and the right to use them. Discovery assesses readiness. Redacted or synthetic material may be appropriate for early tests; validation must reflect the intended real task and agreed data handling.
What happens after a successful PoC?
We scope the next stage, which may be a controlled pilot or production engineering. Its requirements, costs and acceptance criteria are separate decisions.
Can the PoC code be used in production?
Some components may be reusable. Production use can require further work on access, reliability, testing, deployment and monitoring. The findings identify that gap.
How long does an AI PoC take?
The timetable is agreed for the scope and dependencies. We separate time spent waiting for access or preparing data from the implementation and evaluation work.
What needs to be true before you invest further?
Bring the task, the uncertainty and the decision you need to make. The first call helps us identify whether a PoC or another first step is appropriate.
Timeframe and fee are agreed against the scope. We do not quote a standard price for an experiment we have not defined with you.