01
AI features inside your product
Generation, summarisation, classification and assistance built into your existing workflows — where AI saves hours, not where it makes demos.
AI features that survive contact with real users — multi-model routing, per-generation cost tracking and safety moderation, engineered into products people rely on daily.
01
Generation, summarisation, classification and assistance built into your existing workflows — where AI saves hours, not where it makes demos.
02
Tasks routed across models by complexity and cost — quick models for simple work, reasoning engines for the hard parts — behind one interface you own.
03
Requests and outputs checked against restricted-topic registries your team can update without a deploy — essential for education and regulated sectors.
04
Every generation metered in real time and attributed to the session that incurred it, so pricing stays honest and the invoice stays boring.
05
Regenerate, edit and salvage paths built into the product, because models have off days and your users should barely notice.
06
Output quality measured against real examples from your domain — so model swaps and prompt changes are decisions, not gambles.
Useful AI products begin with a defined task, user and acceptable failure mode. We identify where generation, classification, extraction or summarisation can remove effort without removing necessary judgement. The interface then gives users enough context to review, edit or reject an answer rather than presenting uncertain model output as fact.
Existing products can gain focused AI capabilities without becoming an AI product everywhere. A well-contained feature—such as document summarisation, content drafting or support triage—can sit behind the permissions, data and audit controls users already understand. This reduces rollout risk and makes success measurable against the previous workflow.
Different tasks need different models. Routing considers quality, latency, context size, regional availability and cost, while the application keeps a stable interface around providers that may change quickly. Prompts and outputs are validated where possible, and restricted use cases receive moderation, rate limits and human review paths appropriate to their risk.
Data handling is documented before integration. We minimise what leaves the application, separate customer data, avoid storing sensitive prompts unnecessarily and configure provider retention controls where available. For education, employment and other sensitive contexts, automated output remains assistive and visible to an accountable person rather than becoming an unexplained decision-maker.
A convincing demonstration is not enough. We build a representative evaluation set from the work users perform and score candidate changes against it. Quality checks sit alongside latency, refusal behaviour and cost per successful task, making model or prompt updates observable instead of subjective.
Production monitoring records usage, errors, model versions and spend without exposing private content in routine logs. Feedback controls capture where an answer failed and whether a user could recover. That evidence drives iteration and protects the product from silent quality drift when providers release new models.
Yes. We can add a contained service behind your current authentication, permissions and data model, then expose it through the existing interface with usage and cost controls.
The answer depends on task quality, speed, privacy, region and cost. We test representative inputs across suitable providers and keep routing replaceable so the product is not tied unnecessarily to one model.
We narrow the task, ground responses in approved data where appropriate, validate structured output, provide citations or source context, run evaluations and design a clear human correction path.
Yes. Per-request metering, quotas, model routing, caching and administrative reporting show where spend occurs and prevent one expensive workflow from growing unnoticed.