Can you add AI to our existing software without rebuilding it?+
Usually, yes. Most AI integrations connect to existing systems through their APIs, databases or extension points, so the software your team relies on stays in place. We add AI where it helps, such as search over internal documents, drafting inside your CRM or extracting data from forms, and leave the rest of the system untouched.
Which AI models do you work with?+
We choose models per use case rather than forcing one provider. Our builds typically use enterprise model APIs such as OpenAI, alongside PostgreSQL vector search for retrieval and Google Cloud Vision for document OCR. If your organisation already standardises on a cloud or model provider, we build within it.
Do we need to train our own model?+
Rarely. For most business use cases, a pre-trained foundation model combined with retrieval-augmented generation (RAG) over your own documents and data gives grounded answers without the cost of training a model. We recommend the simplest approach that meets your accuracy target, and test it against real examples before committing to a full build.
How do you stop the AI exposing data a user should not see?+
By applying the user's permissions when data is retrieved, before anything reaches the model. Documents and records are indexed with their access rules, and the retrieval layer only returns what the signed-in user is allowed to see. Requests are logged for audit. For regulated or sensitive data, our secure AI development service adds private model deployment and prompt-injection defences.
What does an AI integration cost?+
We do not publish an AI price list, because cost depends on the number of use cases, the state of your data, the systems involved, your security requirements and ongoing model usage fees. We scope the work in a discovery phase and quote against that scope. A prototype on your real data is often the first step, so you can see results before committing to a full build.
How do you test and monitor AI quality?+
Before release, we build test sets from real examples and measure accuracy, response time and cost against them. Model outputs are validated against typed schemas before other systems use them. After release, we monitor answer quality, latency and usage costs, and re-run evaluations when your data or model versions change.
Where is our data processed and stored?+
Your data stays in databases and cloud accounts you control, with UK or EU hosting regions available. Calls to third-party model providers are made under their enterprise terms, and we document which data is sent to which service. For sensitive or regulated data, we can design the integration around private model deployment.
Who owns the prompts, code and integration?+
You do. You own the full IP in the source code, prompts, data pipelines and documentation we build, kept in repositories and accounts you control. Third-party foundation models remain under their providers' licences, and we document which components are yours and which are licensed.
