RAG Development

LLM answers grounded in your own data.

A language model on its own answers from what it was trained on — which is not your contracts, your policies or your product data. RAG connects the model to your content at question time, so answers come from your sources and can be traced back to them.

Talk to us All AI development

Knowledge assistant 12,400 docs indexed

“What is our refund window for annual plans?”

Retrieving relevant passages…

refund-policy-2026.pdf §4.2 helpdesk-kb / billing terms-annual.md

Annual plans can be refunded in full within 30 days of purchase; after that, refunds are pro-rata to the unused term.

3 sources cited

In one paragraph

AGI Software Solutions builds custom RAG (retrieval-augmented generation) systems that connect large language models to your documents, databases and knowledge bases, so every answer is grounded in your own content and cited back to its source. We treat retrieval quality as a measured engineering target — chunking, indexing, reranking and evals — because that, not the model, is where RAG projects succeed or fail. Focused builds start around $30k; enterprise deployments with permissions and multiple sources run $60k–$300k.

What We Build

Retrieval quality is the whole game.

Every RAG demo works. Production RAG lives or dies on unglamorous details: how documents are chunked, what gets indexed, how results are ranked, and whether anyone measured retrieval before shipping. We build and measure those layers explicitly.

Grounding

Answers cite their sources

Every answer traceable to the passage it came from, so a person can verify in one click instead of taking the model's word.

Freshness

Current without retraining

New document, new answer. The knowledge base updates by indexing, not by a training run.

Permissions

Retrieval respects access

Users only get answers synthesised from documents they could open themselves — enforced in the pipeline, not the prompt.

Measurement

Retrieval quality, measured

Eval sets for retrieval and answer quality, run on every change, because "it seems to find things" is not a metric.

Plain English

What is RAG?

Retrieval-augmented generation is an architecture in which, before the language model answers, the system retrieves the most relevant passages from your own content — documents, tickets, database records — and hands them to the model as context. The model answers from what was retrieved, not from memory, which is why RAG answers can cite sources and stay current without retraining.

RAG is the right tool when the problem is knowledge: your content changes, it matters who is allowed to see what, and wrong answers must be traceable. It complements rather than replaces fine-tuning, which shapes how a model behaves rather than what it knows.

Compared

RAG vs fine-tuning: which do you need?

The most common architecture question we get — and the honest answer is "usually RAG first".

	RAG	Fine-tuning
Solves for	What the model knows	How the model behaves
Content updates	Immediate — reindex the document	Requires retraining
Citations	Built in — answers cite sources	Not possible
Permissions	Enforced at retrieval time	Not possible — knowledge is baked in
Typical cost	$30k – $300k depending on scale	$15k – $100k+ plus data preparation
Choose when	Answers must come from your content	Output format, tone or task needs shaping

How We Build It

Five stages from problem to production.

The same process across every AI Development project, scaled to the size of the problem.

Discovery

We work out what the system actually has to do, what data exists, and what happens today when it goes wrong.

Design

Architecture, model choice, integration points and failure handling, defined before any of it gets built.

Integration

Connecting to the systems that hold your data, with security and permission boundaries handled properly.

Automation

The system takes on real work, in the workflows your team already uses rather than beside them.

Refine

Tuned against real usage and measured with evals, because how people use a system is never quite how it was designed.

Use Cases

Where rag development pays off.

The patterns we see deliver, across startups, SMEs and enterprise teams.

Internal

Company knowledge assistant

Policies, processes and institutional knowledge, answered with citations, for the questions people currently ask each other.

Support

Grounded support answers

Customer-facing answers drawn only from approved content, with the refusal behaviour that keeps support honest.

Legal & finance

Contract and policy search

Clause-level retrieval across thousands of documents in regulated environments where provenance is non-negotiable.

Product

RAG inside your product

Search and Q&A features in your own software, built on your customers' data with per-tenant isolation.

Related Work

Systems we have shipped.

AI DevelopmentAI Chatbots

Conversational AI Studio

Context-aware multi-channel chat that qualifies inbound leads and routes them to the right person.

3.2× qualified leads, 54% faster replies

AI DevelopmentAI Automation

Support Hub Inbox

One inbox across web chat, email, WhatsApp and Facebook, with AI replies trained on past resolutions.

60% faster replies, 99.95% uptime

AI DevelopmentMarketplace

AI Marketplace Platform

Support chatbot, live SEO analyser and a vector recommendation engine, all sharing one retrieval layer.

+10% GMV, 40% fewer support tickets

See all case studies

What Teams Say

Hear from the teams we work with.

“We needed a voice agent that could actually qualify leads, not a chatbot pretending to be one. The team shipped a sub-700ms pipeline in 6 weeks. It now handles 5k calls a day.”

Priya RajHead of Growth, Ninjatech

“What sold us was their willingness to put AI engineers and product designers on the same call. We got working prototypes by week two and a production rollout in three months.”

James ThorntonCTO, Allindex

“AGI designed a CRM system tailored to our client management process. It is intuitive, reliable, and has centralized all our communication and history in one dashboard. This has greatly improved client retention.”

Carlos MendesProduct Manager, Qilinlab

Common Questions

Before you get in touch.

What is RAG in simple terms?

Before answering, the system looks up the most relevant passages from your own documents and gives them to the language model to answer from. The model answers from your content, not its memory — which is why the answers can cite sources and stay current.

RAG or fine-tuning — which do we need?

If the problem is what the model knows (your documents, your data, changing content), RAG. If the problem is how it behaves (format, tone, a specialised task), fine-tuning. Many production systems use RAG first and add fine-tuning only if behaviour still needs shaping.

What does RAG development cost?

A focused pilot over one clean content source: roughly $30k–$60k. Enterprise deployments with multiple sources, permission enforcement and eval infrastructure: $60k–$300k. Data quality is the biggest cost variable — messy sources cost more than any model choice.

Can RAG respect our document permissions?

Yes, and it must. We enforce access control at retrieval time, so the system never synthesises an answer from a document the asking user could not open. This is a hard requirement in most of our deployments, not an add-on.

How do you stop it making things up?

Grounding narrows the space, but the honest answer is: retrieval evals, answer-quality evals, citation checking, and tuning the system to refuse when the sources do not support an answer. Measured, not assumed.

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