Ask a generic AI model about your own contracts or policies and it will answer confidently, often wrongly, because it is guessing instead of reading your data. Our AI software development team builds RAG systems that pull the right passage from your own content and ground every answer in it, with a citation you can check and retrieval quality you can measure.
End-to-end systems that connect a language model to your knowledge base and return answers with sources attached.
Measurement that tells you how often the system answers correctly before your users find out, using the AI-powered testing discipline we apply across builds.
Our engineers turn messy, scattered content into a retrieval-ready corpus that stays current, drawing on our AI-powered data engineering practice.
Precision work on the layer that decides whether your system finds the right passage or a plausible wrong one.
The generation layer built to use retrieved context well instead of ignoring it.
Ongoing engineering that keeps quality from decaying as your content and usage grow.
The first two weeks produce a clear-eyed answer on whether your data can support the use case, plus a roadmap with architecture, scope, and cost. You take it to your board or keep it, with no obligation to build with us.
Your scattered content becomes a clean, indexed knowledge base: parsed, chunked, embedded, and synced to the systems it came from. This stage decides most of the final answer quality, so it gets engineering attention.
The grounded pipeline goes up: retrieval, reranking, generation with citations, and guardrails. You test it against real questions from your team while our engineers tune what the tests surface.
Quality gets measured before launch. You see groundedness, relevance, and answer accuracy on your own test set, with thresholds agreed for go-live.
The system ships into your environment with access controls, index freshness monitoring, and query analytics. Your team gets the runbooks and the training to operate it, with our support behind them as long as you want it.
RAG quality lives in the corpus and the retrieval layer. 92% senior-level engineers who treat ingestion and indexing as the core of the build, not a preprocessing chore.
Every build ships with an evaluation harness. You see groundedness and relevance scores on your own test set before launch.
99% of projects arrive on time and on budget, which matters when a RAG rollout has internal stakeholders waiting on it.
ISO 27001:2022 certified, HIPAA-ready, GDPR covered. Access permissions carry through to every retrieved passage.
The Relevant AI Delivery Framework™ cuts delivery time by up to 50% with AI-assisted development and ships up to 20% fewer bugs, without loosening the review gates that keep quality up.
Fine-tuning adjusts how a model writes: its tone, format, and style. It does not give the model dependable access to facts, and it goes stale the moment your content changes, because the knowledge is baked in at training time.
RAG works the other way: it retrieves the current version of your content at the moment a question is asked and grounds the answer in it, with a citation pointing back to the source passage. For knowledge that updates, carries compliance weight, or needs an audit trail, retrieval wins on accuracy, freshness, and defensibility. Fine-tuning earns its place for consistent voice or a fixed output structure, and the two combine well when you need both a house style and reliable facts.
We have delivered both, including standalone generative AI development, and we recommend the approach your problem actually calls for rather than the one that demos best.
Packaged features are tuned for the average case: a tidy document set, general questions, and no compliance exposure. They tend to break exactly where your business lives, in permission-aware retrieval, ranking tuned to your domain language, and evaluation you can actually audit.
A custom system delivers four things a packaged feature cannot:
Three layers work together to keep answers grounded:
No system eliminates the risk entirely, so the build defines the failure path explicitly: on a weak or conflicting retrieval, the assistant states that it does not know and routes the question to a person, rather than producing a confident guess. That behavior is a decision we set with you, calibrated to your risk tolerance, and it is verified through testing.
Yes, and it must, because without it a well-phrased question becomes a data leak. Permission-aware retrieval filters the index against the asking user’s rights before any passage reaches the model, so two employees posing the same question can receive different answers, each drawn only from the content they are cleared to see.
This is not a filter applied at the end; it is built into the retrieval architecture and validated as a security requirement. In regulated settings such as healthcare, where our HIPAA compliance and cybersecurity work applies, access control and audit logging enter the specification at the first stage rather than arising as a question after launch.
Before launch, you get groundedness, retrieval relevance, and answer accuracy measured on a test set built from your own questions, with go-live thresholds agreed up front. After launch, query analytics show where retrieval falls short and how often users escalate to a human, so quality stays visible instead of drifting quietly.
A demo that “feels smart” is not a metric; a groundedness score you can defend in a board review is. We build that measurement into the first stage and hand you the harness, so the proof is yours to rerun whenever the content or the model changes.
You do, entirely: source code, index, evaluation sets, infrastructure configuration, and documentation. Vendor lock-in is a real risk in this space, so the architecture uses replaceable components, and the handover includes training your team to operate and extend the system without us.
You need access to the content the system will answer from and a clear sense of the questions it should handle. You do not need clean, organized, or complete documentation; the feasibility stage inspects what you have, flags the gaps that matter, and folds the cleanup into the plan. A named point of contact on your side who knows the content and the goal keeps the engagement moving.
Our engagement covers design, engineering, evaluation, deployment, documentation, and handover training, all of which you own at the end. Third-party costs sit outside it and stay with you: language model API usage, vector database or cloud hosting, and any commercial data source licenses.
We size those running costs after the discovery call so there are no surprises, and where budget matters we architect toward models and infrastructure that keep the ongoing bill predictable.
A 4.9 Clutch rating from clients who point to transparency and delivery quality in their own words.
A grounded system cites its source and scores its own accuracy. Bring us the use case, and you’ll leave knowing what it takes to build one you can stand behind.
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