The RAG pipeline development company behind answers you can trust

Very likely, because most hallucination in a knowledge assistant is a retrieval problem wearing a generation costume. If the pipeline hands the model the wrong passage, or no passage, the model fills the gap with something plausible and wrong. Fixing the prompt rarely helps; fixing retrieval usually does. We can assess your existing setup, measure whether it's actually fetching the right sources, and rebuild the pipeline underneath the chatbot you already have without replacing the whole application.

We measure it, which is the part most RAG projects skip. We build a ground-truth set of questions with known-correct sources from your own content, then score whether the pipeline retrieves those sources — recall and precision on retrieval — using Ragas and custom metrics. That score runs on every change, so improvements are demonstrated rather than asserted and regressions get caught before users do. Judging a retrieval system by whether the first answer sounded convincing is how wrong-but-fluent answers reach production.

Not when permissions are built into retrieval, which is how we build it. Entitlements are enforced at retrieval time, so the pipeline only ever considers passages the current user is allowed to see — filtering happens before results are assembled, not after. We map the permission model from your sources, such as SharePoint and Confluence, and your identity provider. Treating permissions as an afterthought is the most common way an enterprise knowledge base turns into a data-leak incident, so we design for it from the audit onward.

It depends on scale, infrastructure and operational appetite, and we decide after those are clear rather than by default. Postgres with pgvector is often enough and keeps everything in a database you already run; a dedicated store like Pinecone, Weaviate or Qdrant earns its place at larger scale, higher query volume or when you want managed operations. Because the pipeline sits behind a clean interface, the vector database is a component we can benchmark and swap, not a decision that locks you in.

With a re-indexing strategy built into the pipeline rather than a one-off ingestion at launch. We track additions, edits and deletions in your sources and re-index on a schedule or on change, so the index reflects the current document and retired content stops being retrieved. Freshness is a first-class requirement in how we build, because a retrieval system that silently serves last year's version while sounding authoritative is worse than no system at all.

The main drivers are the number and messiness of your sources, the parsing difficulty of your formats, the accuracy bar you're targeting, and whether permission-aware retrieval is in scope — enforcing entitlements across several systems is real work. A focused pipeline over a clean, single source with a scored evaluation set is usually a few weeks; a broader enterprise build across multiple connectors with permissions and freshness runs longer. Running cost is mostly embedding and inference per query, which we model during design.

More often than the hype suggests. If the answer is a structured lookup — a status, a balance, a record — a database query is faster, cheaper and exact, and dressing it up as retrieval adds nothing. If your content is contradictory or badly out of date, retrieval will confidently cite the wrong version, so the data has to be sorted first. And if there's no correct answer already written down anywhere, retrieval has nothing to ground on. We'll tell you during the audit if one of these applies rather than build you a pipeline that can't succeed.

They stay within your environment and accounts wherever the architecture allows, and they aren't used for anything outside your project. Where a hosted embedding or model provider sits in the design we tell you exactly which one, what leaves your infrastructure, and what their retention terms are before you approve it — and open-weights embedding models deployed inside your own network are an option when nothing may leave at all. The index we build is yours, on infrastructure you control.

Our own in-house senior engineers in Sahibzada Ajit Singh Nagar, Punjab — 45+ of them, with over a decade of combined delivery experience, delivering for clients in 15+ countries. Nothing is subcontracted; you own the code, repository and pipeline from day one. You meet the engineers who'll be on your project before you sign, and a senior engineer replies within 24 hours of your first message.