Artificial Intelligence · Build
We build retrieval-augmented systems that answer from your approved content, cite the source of every answer, and respect who is allowed to see what. Permission-aware retrieval is designed in from the start, because filtering access after an answer is generated is unreliable and retrofitting it is worse. We measure retrieval quality separately from answer quality, since most failures are retrieval failures dressed up as model weakness. The result is a system whose answers can be traced, trusted and corrected rather than taken on faith.
In short
Retrieval-augmented generation systems answer questions from your own documents and data, with citations and permission awareness. Most of their accuracy depends on retrieving the right passages, not on the model that writes the answer.
Start a conversationWhat we do
We bring in documents and records, handle their varied formats and structure, and keep the index current as content changes. Stale content is a common and quiet failure, producing confident answers from policies that have since been superseded. A refresh strategy keeps the index honest about what the organisation actually believes today.
We combine keyword and semantic retrieval, tune how documents are chunked, and re-rank results against the questions users actually ask. Chunking and ranking decisions have more effect on answer quality than most teams expect. We tune them against real queries rather than assumptions about how people will search.
We filter retrieval by each user's entitlements so an answer can never surface a document they are not allowed to open. This is enforced at retrieval time, before the model sees anything, rather than patched afterwards. It is the control that lets a RAG system be pointed at confidential content safely.
We show the sources behind each answer and measure retrieval quality as a separate metric from answer quality. Because most failures are retrieval failures, isolating them is where the real improvement comes from. Citations also let users verify an answer themselves rather than trusting it blind.
What you receive
Documented output your teams keep and can act on without us in the room.
When this work helps
How an engagement runs
Understand the workflow, the data available, the constraints, and what a good outcome would look like. We establish the problem precisely enough to judge whether AI is even the right instrument for it.
Review data protection, security, oversight, and feasibility for the specific use cases on the table. We report which cases are worth building, which are better solved another way, and what would have to be true to proceed safely.
Set out the approach, controls, governance, and evaluation measures before build begins. Deciding data boundaries, oversight, and how success will be measured up front keeps them from becoming retrofits.
Support implementation and evaluate performance, error modes, and control effectiveness in use. We measure the system against the criteria agreed at design, and report honestly where it falls short.
Where we work
Fidova advises on AI adoption and AI governance from Delhi, India, and works with organisations internationally, including teams that must satisfy overlapping regimes such as India's data protection law, the EU AI Act, and GDPR.
Engagements run in English, are scheduled around your time zone, and can be delivered remotely end to end, with on-site time planned where a phase genuinely benefits from being in the room.
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Questions
Straight answers to what buyers ask us before an engagement begins.
Enquiries
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Prefer email? contact@fidovaadvisory.com
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