RAG Knowledge System
A RAG knowledge system makes your company's accumulated documents answerable. Someone asks a question in plain language and gets an answer drawn from your SOPs, contracts, manuals, past proposals and project files — with a citation to the document it came from, so the answer can be checked.
Who it's for
Any organisation where knowledge lives in people's heads and scattered folders.
What changes
Institutional memory that survives people leaving.
- Starting at
- ₹75,000
- Timeline
- 2–4 weeks for an audit
- Category
- AI Consulting & Enablement
- Built from
- Vashi, Navi Mumbai
Key takeaways
- Citations are mandatory — an answer with no source is not usable in a business context.
- Permissions must be enforced at retrieval, or the system leaks documents people should not see.
- Document quality decides answer quality; contradictory sources produce contradictory answers.
- The largest gains are in onboarding, support and anywhere expertise sits with one person.
- Builds in 8–16 weeks from ₹3,50,000.
The knowledge that only lives in one person's head
In most established businesses, the answer to a substantial share of internal questions is "ask Prakash". Prakash has been there fourteen years and knows why the process works the way it does.
The documents exist — SOPs, manuals, past project files, contracts — but nobody can find anything in them, so asking Prakash is genuinely faster. Which means Prakash is interrupted forty times a week, and when he retires a great deal leaves with him.
A retrieval system does not replace Prakash. It answers the forty routine questions so that the ones reaching him are the ones that actually need him.
Why retrieval rather than training a model
Clients often assume the approach is to train a model on their documents. Retrieval is almost always the better answer, and the reasons are practical.
| Consideration | Retrieval (RAG) | Fine-tuning |
|---|---|---|
| Adding a new document | Immediate | Requires retraining |
| Citing the source | Built in | Not possible |
| Permissions per user | Enforceable | Very difficult |
| Correcting an error | Fix the document | Retrain |
| Cost to set up | Moderate | High |
| Answers from private data | Yes | Yes |
| Suits changing content | Yes | Poorly |
What goes wrong with the documents themselves
The system is only as coherent as its sources, and most document archives are not coherent.
Three versions of the same SOP with no indication which is current. A policy superseded in 2023 sitting alongside its replacement. Two departments with contradictory procedures for the same task. Scanned documents that are images with no text layer.
Part of the project is a document audit: what is current, what is superseded, what contradicts what. Clients frequently find this the most valuable output, because contradictions that a person navigated by knowing which document to trust become visible when a system tries to answer from all of them.
Where it delivers most
Not every knowledge problem suits this, and the strong cases share a pattern.
- Onboarding. A new joiner asks the system rather than interrupting colleagues, and reaches competence considerably faster.
- Customer support. Agents answer from product documentation without holding the customer while they search.
- Field and service teams. Equipment manuals and procedures answerable on a phone at site.
- Proposal and tender work. Past proposals, pricing and case material findable instead of half-remembered.
- Compliance and process. What the procedure actually says, with the citation, rather than what someone thinks it says.
Making answers trustworthy
The system must say when it does not know. A retrieval system that produces a plausible answer from irrelevant documents is more dangerous than one that returns nothing, because the user has no way to tell.
Every answer carries its sources with a link to the document and the specific section. Where retrieved material is weak or conflicting, the answer says so rather than picking one.
We also log every question asked and answer given. That log is both an audit trail and the most direct measure of whether the system is working — questions it answers badly are visible and fixable, usually by fixing a document.
Where it runs, and what it costs
Eight to sixteen weeks from ₹3,50,000, depending on document volume, how much cleanup is required and how many source systems need connecting.
It deploys on your infrastructure. Company documents do not leave your control, and for clients with particularly sensitive material we can run models on-premise entirely so nothing goes to an external provider.
You receive the system, the ingestion pipeline that keeps it current as documents change, the permission model, the query log, and full source.
FAQ
RAG Knowledge System — your questions
How many documents does it need to be worthwhile?
It is less about count than about how hard things are to find. A few hundred documents across several systems, where people regularly cannot locate what they need, is a strong case. Fifty well-organised documents in one folder is not — good search or a decent wiki solves that. We assess the actual finding problem during scoping rather than counting files, because volume alone is a poor predictor of value here.
Can it read our scanned PDFs?
Yes, with an OCR step during ingestion. Quality depends on the scans — clean text documents extract well, poor photocopies less so, and handwritten annotations largely not at all. Where a critical document is a bad scan, the practical answer is often to obtain a better copy rather than accept degraded retrieval on it. We flag documents that ingested poorly so you can decide which are worth re-scanning.
Does it work with SharePoint, Drive or our file server?
Yes to all three, plus Dropbox, Confluence and most document systems with an API. The connector reads on a schedule and respects the source system's permissions, so a document a user cannot open in SharePoint is not searchable by them here either. Plain network file shares work too, though permissions there are often less granular than clients assume, which is worth reviewing during setup.
What about documents in Hindi or regional languages?
Supported, including mixed-language archives where some documents are in English and some are not. A question asked in English can retrieve from a Hindi document and answer in English, which is useful for organisations where operational documents were written locally and management works in English. Retrieval quality is somewhat lower for languages with less training data, and we test on your actual documents during the pilot.
Will people actually use it?
Only if it is faster than asking a colleague, which sets a high bar. In practice that means it must be where people already are — inside Teams or Slack, or in the tool they work in, rather than a separate site they have to remember. It also means the first month matters enormously; if early answers are poor, people stop trying and do not come back. We run a pilot with one team and fix the document problems before wider release for exactly this reason.
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Next step
Want a RAG Knowledge System for your business?
Tell us what the process looks like today and we'll tell you what it would look like automated — and what it would cost.