Natural Language Interfaces
Plain-language interfaces over the systems your team already runs — so the answer to a complex question is a sentence, not a query.
The problem with most business software isn't that it lacks features. It's that the features are buried under training requirements, filter combinations and export workflows that require a specialist to navigate. A natural language interface puts the data closer to the person who needs it: ask in plain English, get a direct answer.
We've built this in our own products — including Polly, where the interface runs on-premise so patient data stays there too. The design principle is the same regardless of domain: the model answers from your data, not from its training. Access controls, retrieval architecture and confidence signalling are built in from the start, not retrofitted when someone asks a question it shouldn't answer.
- Interface design and development, with the query layer connected to your actual data
- Access control and permissions built in from the start, not retrofitted
- Privacy architecture reviewed and documented, so you know what goes where
- Integration with your existing databases, APIs or document stores
- Evaluation framework to check that the answers are accurate, not just fluent
- Ongoing prompt and retrieval tuning as usage reveals the edge cases
Quick questions
Is this just a chatbot?
A chatbot has a script. A natural language interface has your data. The difference is that a chatbot gives generic answers; this gives specific answers about your systems, your records, your operations. The underlying technology overlaps, but the result is different.
What stops it giving confidently wrong answers?
Architecture, not faith. We build retrieval systems that ground answers in your actual data rather than model memory, add confidence signalling where it's useful, and test against known-good outputs before anything goes to users. There's no guarantee of perfect accuracy, but there are engineering choices that make it reliable enough to use.
Our data is sensitive. Can this be run internally?
Yes, and for sensitive environments that's often the right call. We've built self-hosted deployments specifically for contexts where data can't leave the environment — Polly for healthcare is the clearest example. The architecture is more complex than a hosted solution, but it's achievable.
We've shipped systems still running a decade later.
Custom software, AI agents and automation for real business problems. We design, build and run production systems — not prototypes. We ship working software that scales from a first version to national platforms. All of Build →
Need this sorted?
Tell us what you need. A real person replies — not a bot, not a call centre.