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TCB Infotech | Expert Odoo & ERPNext Implementation Partner

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RAG Applications

Answers Grounded in Your Own Documents, Not a Guess

A general AI tool answers from whatever it learned during training, which has nothing to do with your policies, your specifications or your past contracts. A RAG application searches your own documents first, writes an answer from what it actually found, and shows the exact passage it used, so an employee can check it in seconds instead of trusting it blindly.

50+ ERP and software projects. Zero failed projects since 2021.

Ask the Policy Manual
Illustrative
What is our warranty period for the industrial pump range?
📄
Warranty Policy v4, section 3.2
Updated 2 months ago
📄
Product Spec Sheet, Pump Range
Updated 5 months ago
Answer, from your documents
The industrial pump range carries a 24 month warranty from the invoice date, extended to 36 months if the annual service contract is active.
Figures are illustrative
Sourced
Every answer shows its passage
Your
Documents, not general training
4-6 wk
To a first working application
0
Invented answers when unsure
Why It Matters

A confident wrong answer is worse than no answer

General AI models are fluent by design. That fluency is a liability when the question is about your specific policy, not general knowledge.

Grounded in Your Documents

The answer comes from what you actually wrote

Before writing an answer, the application searches your document store for the passages that actually address the question. The answer is built from those passages, not from a general impression of similar topics. If nothing relevant is found, it says so.

  • Retrieval from your own document store first.
  • Answers built only from what was retrieved.
  • A clear statement when the documents do not cover it.
Searching through company documents for an answer
Checkable by Design

You do not have to trust it, you can check it

Every answer is delivered with the exact source passage attached, so an employee can open the document and confirm it in seconds. That habit of checking is encouraged, not discouraged, because the source is always one click away.

  • Source document and passage shown with every answer.
  • One click from the answer to the original text.
  • Confidence in the tool built through easy verification.
An employee verifying an answer against a source document
In Practice

What a retrieval-grounded answer looks like

The question, the sources it pulled, and an answer built strictly from what those sources said.

Ask the Contract Library
Illustrative
Can this customer's contract be terminated with 30 days notice?
📄
Master Services Agreement, clause 14
Signed 14 months ago
📄
Amendment 2, clause 14.1
Signed 6 months ago
Answer, from your documents
No. The original agreement allowed 30 days notice, but Amendment 2 changed this to a 90 day notice period. The 90 day term currently applies.
Figures are illustrative
How It Works

From a document store to a grounded answer

1
Index the documents
We connect to your document store and build a searchable index of the content that matters.
2
Retrieve on question
A question triggers a search for the passages most relevant to what was asked.
3
Generate from evidence
An answer is written using only the retrieved passages, not general knowledge.
4
Attach the sources
The passages used are shown alongside the answer, ready to check.
5
Keep the index current
Documents are re-indexed on a schedule, so updates are reflected automatically.
What It Handles

The documents most companies want an answer from

We start with the document set your team asks about most, then extend from there.

📄

Policy and SOP manuals

HR policies, quality procedures and operating manuals, answered directly instead of searched by hand.

📋

Product specifications

Technical specs and datasheets, answered for sales and support without waiting on engineering.

⚖

Contracts and agreements

Clause-level questions on customer and supplier contracts, with the exact clause shown.

📧

Past correspondence

Emails and support tickets searched for how a similar case was handled before.

📁

Internal knowledge base

Scattered wiki pages and how-to documents made searchable from one place.

🔍

Regulatory and compliance text

Standards and regulations relevant to your industry, with citations to the exact clause.

What Changes

What a grounded question-answering tool changes

Faster
Answers to policy questions
Checkable
Source shown every time
Fewer
Interruptions to the expert
Consistent
Answers across the team
Current
As documents are updated
Honest
When the documents do not say
Why Us

Why teams choose TCB Infotech for RAG applications

1
Grounding is not optional
We design for the application to say it does not know rather than produce a fluent guess.
2
Sources always attached
Every answer carries the passage it was built from, so trust is earned through checking, not asked for.
3
We work with real documents
Messy PDFs, scanned pages and inconsistent formatting are the normal case, and we plan for it.
4
Kept current automatically
The index refreshes on a schedule, so an updated policy does not sit stale for months.
5
Model-agnostic build
We choose the approach that fits your data and hosting rules, with no lock-in to one vendor.
6
Proven delivery
50+ projects across industries, with zero failed projects since 2021.
FAQ

RAG application questions

What is a RAG application?
RAG stands for retrieval-augmented generation. It is an application that, before answering a question, searches your own documents for the relevant passages, then writes an answer based only on what it found. Every answer comes with the source passages attached, so it can be verified.
How is this different from a general AI chatbot?
A general chatbot answers from whatever it learned during training, which may have nothing to do with your business and cannot be traced to a source. A RAG application answers from your specific documents, and shows exactly which passage it used, so the answer is grounded and checkable.
What happens if the answer is not in our documents?
It is built to say so rather than invent an answer. If the retrieval step finds nothing relevant, the application reports that clearly instead of guessing, which is the central design goal of a properly built RAG system.
What kinds of documents can it use?
Policy manuals, product specifications, contracts, SOPs, past correspondence and any other text-based records you hold, in the formats you already use such as PDF, Word or your document management system.
How do you keep the answers accurate as documents change?
The document index is refreshed on a schedule matched to how often your source documents change, so an updated policy or specification is reflected without anyone manually rebuilding the system.
Is our document content kept private?
Yes. Retrieval runs against your own document store under your access rules, and hosting is chosen to fit your data policy. We build model-agnostic and do not tie your documents to one vendor's platform.
In Short

The right answer is already written down somewhere

Most companies do not lack the answer, they lack a fast way to find it inside hundreds of pages of policy, spec and contract text that nobody has time to search by hand.

A RAG application does the searching, writes the answer from what it found, and shows its source. It does not replace the document, it makes the document usable at the speed a question actually arrives.

Put Your Documents to Work

Book a free consultation. Tell us which documents your team searches most, and we will show you what a grounded question-answering tool would look like.

Book a Free Consultation →

No commitment. A practical look at what your documents could answer.