Unitforce Technologies
Consulting Pvt Ltd
Generative AI that answers from your facts, cites where they came from, and says nothing when it has no evidence.
GenAI grounded in real business data. We build retrieval-augmented systems over your documents, policies and databases, so models answer from facts, cite their sources, and rank answers you can trust to production.
A general model knows the internet as of its training cut-off. It does not know your pricing policy, last quarter's board pack or the clause in your own contract template — and asked about them, it will produce something plausible. Retrieval-augmented generation fixes that by fetching the relevant passages from your own corpus first and requiring the model to answer from them.
The practical payoff is traceability. Because the answer is assembled from retrieved passages, every claim can carry a citation back to the source document, which is what makes the output usable in regulated and audited settings.
We load and chunk your documents, policies and database records, embed them, and index them for semantic search — commonly on Postgres with pgvector, so the vector store is the database your team already backs up, monitors and secures rather than a new system to operate.
Retrieval quality is where most RAG projects are won or lost, so it gets the attention: chunking strategy, hybrid keyword-plus-vector search, re-ranking, and metadata filters that keep a user's results inside what their role is allowed to see.
Before a RAG system goes anywhere near users, it is scored against a question set drawn from the real corpus with answers your subject experts agree on. That harness stays in place afterwards, so a change to chunking, a model swap or a prompt edit is measured rather than hoped about.
Abstention is treated as a correct answer. A system that says it cannot find the answer is far more valuable to an enterprise than one that fills the gap with something convincing.
RAG retrieves the passages most relevant to a question from your own documents or databases, then has the model answer using only those passages. Because the answer is grounded in retrieved text, it can cite its sources and can decline when nothing relevant is found.
Usually not, and rarely first. Retrieval gets your current data into the answer without retraining, updates the moment a document changes, and shows its sources. Fine-tuning is worth considering for tone and task format, not for facts.
Most often Postgres with pgvector, so retrieval lives in a database your team already runs, backs up and secures. Where a workload genuinely needs a dedicated vector store, we will say so rather than add one by default.
By grounding answers in retrieved passages, requiring citations, and instructing the system to abstain when retrieval returns nothing relevant. An evaluation harness then measures how often it answers correctly, wrongly, or not at all before rollout and after every change.
Agents that do the work, not just describe it — wired into the systems your team already uses.
An assistant inside the tool people already have open, grounded in what your company actually knows.
Whole processes automated end to end, with the exceptions routed to people instead of hidden from them.
Fraud flagged as it happens and credit scored at the point of decision — with reasons an auditor will accept.
The unglamorous half of AI — versioning, serving, monitoring and rollback — owned by us so your team doesn't carry it.