We build copilots, search and generative AI features grounded in your documents and data, inside the tools your teams already work in. Each one is evaluated for accuracy and cost before it reaches users.
Generative AI is easy to demo and hard to depend on. A chat window over a few documents can impress in a meeting and still fail the people who need it every day, because the answers are not grounded, the costs are not controlled or the feature lives outside the tools they use. We build for the everyday case.
Every feature starts with the task it supports. We work with the people who will use it to define what a good answer looks like, gather real examples, and decide where the feature should live: inside your CRM, your help desk, your intranet or your own product. Adoption matters more than novelty.
We build retrieval (RAG) over your documents, tickets and knowledge bases, with your access rules respected so people only see what they are allowed to see. Prompt, model and cost engineering keep the feature fast and affordable. Evaluation suites measure accuracy, safety and drift before launch and after it, so you know whether the feature is getting better or worse.
We are model agnostic. We pick commercial or open weight models per task on quality, cost, latency and data residency, and design the system so a model can be replaced without a rebuild. The code, prompts and evaluation sets live in your repositories, and the feature runs in your environment.
Answers from your policies, manuals, contracts and tickets, with sources shown so people can check them.
Drafting, summarising and next step suggestions inside your CRM, help desk or internal apps, where the work already happens.
Semantic and hybrid search across documents and records, so people find the right answer instead of a list of files.
Extraction and classification for invoices, forms, claims and correspondence, with confidence scores and review queues.
Generative features in your web and mobile products, designed with guardrails for tone, accuracy and safety.
Assistants and content workflows that work across the languages your customers and teams use.
We define what good looks like with the people who will use the feature, and collect real examples to test against.
Retrieval over your documents and systems, with access rules respected, so answers reflect your business rather than the model's guesses.
Accuracy, safety and cost are measured against an evaluation suite agreed with you. The feature ships when it meets the bar.
In production we track quality, usage, cost and drift, and keep tuning prompts, retrieval and models as needs change.
Retrieval augmented generation (RAG) means the model looks up relevant content from your own documents and data before it answers. If the answers need to reflect your policies, products or records, you almost certainly need it.
Most business use cases are best served by retrieval, because your content changes and answers need sources. Fine tuning helps with style, format or narrow tasks. We often use both, and decide based on evaluation results rather than preference.
Grounding in your data, showing sources, constraining what the model may answer, and testing against an evaluation suite before launch. In production we monitor accuracy so problems are caught early.
We choose the model per task, cache where it is safe, keep prompts and context lean, and track cost per request from day one, so cost is a design decision rather than a surprise.
No. We deploy into your cloud tenancy or on premise infrastructure with your retention and access rules, and use providers and settings that do not train on your data.
Tell us what you are trying to fix. A senior member of the team replies within one business day.
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