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An AI assistant is planned. Until it is genuinely useful we would rather point you at the page that actually answers your question.
Generative AI produces new content — text, images, code, structured data — rather than classifying or predicting from fixed options. Commercially, most of the value sits in text: drafting, summarising, answering questions, and turning unstructured input into structured records.
The engineering problem is not generating output; a model does that immediately. It is constraining output so it is grounded in your data, consistent in format, and safe to show to someone outside your company.
Language-shaped work at volume is expensive and slow when done entirely by hand. Drafting responses, summarising long documents, and reformatting between systems are tasks where generation genuinely reduces cost per item.
The failure mode is what makes this different from ordinary automation. A rules engine that breaks produces an obvious error. A generative system that fails produces fluent, confident, wrong output that reads exactly like correct output. Without grounding and review, that reaches customers at scale.
Our approach
We ground generation in retrieval wherever facts matter. The model should be summarising and rephrasing content pulled from your systems, not recalling from training data. Combined with visible citations, this turns an unverifiable claim into something a user can check in one click.
We constrain output format at the API level rather than asking politely in a prompt. Structured output and schema validation mean a malformed response is rejected and retried automatically instead of flowing downstream into a system that expected valid JSON.
We define the human checkpoint explicitly for anything consequential. Drafting a customer reply for approval is a very different risk profile from sending it. Where automation is warranted, we set a confidence threshold and route the remainder to a person.
Capabilities
Grounded answers from your documents with citations, so claims can be verified at the source.
Turning unstructured text into validated records, with schema enforcement and a review queue for low confidence.
Drafting at volume with brand and tone constraints, and an approval step before anything is published.
Input filtering, output validation and refusal handling, tested against adversarial cases rather than assumed.
Scored test sets that run in CI so prompt and model changes cannot silently degrade quality.
Model routing, caching and context management so unit economics stay viable at production volume.
Stack
Process
Deciding precisely what gets generated, from what inputs, and what makes an output acceptable.
Real examples with agreed-good outputs, built before implementation so quality has a number.
Retrieval plus a strong model, scored against the golden set to establish where quality starts.
Structured output, validation and guardrails tested against deliberately awkward inputs.
The human checkpoint designed as product, since approval queues are used all day by real staff.
Quality, cost and latency tracked continuously, because provider model updates shift behaviour.
Use cases
Staff or customers asking questions of documentation and policies, with citations back to the source.
Generating replies grounded in account context for a person to review, edit and send.
Extracting fields from contracts, invoices and applications into validated records.
Product descriptions and summaries generated consistently, with approval before publication.
Outcomes
FAQ
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