RAG development for LLMs that know your business

Retrieval-augmented generation — done as a product, not a demo. We design chunking, hybrid search, reranking, evals, and guardrails so answers come from your data with sources you can trust.

USA Canada Europe SaaS Support Internal knowledge

Get a Free Quote View case studies

In short

RAG (retrieval-augmented generation) development combines a large language model with search over your own documents — using embeddings, vector databases, hybrid retrieval, and evaluation — so the model answers grounded in your content instead of guessing from the public internet.

What we deliver

01

Document RAG

PDFs, Notion, Confluence, Drive, SharePoint, and SQL sources.

02

Hybrid retrieval

Vector + keyword (+ reranker) — not cosine-only search.

03

Evals & quality

Golden sets, regression tests, and human review loops.

04

Citations & UX

Source-backed answers in chat, search, or in-product copilots.

05

Multi-tenant RAG

Per-tenant indexes or strict filters — no cross-contamination.

06

Private / VPC options

Data residency and private networking when compliance requires it.

Honest fit check

A plain answer up front — we would rather not sell you something you do not need.

Yes if

  • Answers must come from your documents — with citations
  • You care about accuracy evals on a real golden set
  • You need this inside a real product or support workflow

Not a fit if

  • You only need creative writing with no grounding
  • You want a generic ChatGPT skin with no quality process
  • You will not review answers or maintain source documents

Why most RAG demos fail in production

Bad chunking, embedding-only retrieval, missing citations, and no evals. We treat retrieval quality as a product requirement: measure it, regress it, and keep humans in the loop where risk is high.

We also ship the surrounding product — auth, admin for sources, feedback buttons, and logging — so ops teams can improve answers over time.

RAG vs fine-tuning vs long context

Your need Recommended
Knowledge that changes often RAG
Consistent tone / format Fine-tuning
One huge document per query Long-context LLM
Private data that must stay controlled RAG (+ private deploy)
Support / KB bots overall RAG (often + light fine-tune)

How we work

  1. Discovery Sources, users, risk, success metrics
  2. Ingest design Chunking, metadata, access control
  3. Retrieval Hybrid search, rerank, citations
  4. Evals Golden set, regressions, guardrails
  5. Ship Product UX, monitoring, handoff

Pricing & timeline

Typical range

Custom quote after scoping

Timeline

5 – 16 weeks

Team shape

1 AI lead · 1–2 engineers · client domain expert

Private deployments and multi-tenant isolation are scoped separately from simpler single-tenant SaaS RAG bots.

Get a written quote

Why teams choose DebuggedSoftware

01

Senior engineers stay on the work

The people you meet in discovery stay involved through architecture, delivery, and launch — not a junior bench swap after the sale.

02

Written decisions, weekly demos

Scope, tradeoffs, and progress live in writing. You see working software in milestones inside our client portal with live chat.

03

You own the output

Repos, infra, analytics, and documentation live in your accounts from day one. No agency lock-in at the end of the engagement.

04

Built for USA, Canada & Europe

English-first delivery, overlap hours where needed, NDA-friendly collaboration, and remote teams used to North American and European product standards.

Related software development services

Teams that work with us on RAG Development also compare these pages when planning roadmap, budget, and long-term maintenance:

Frequently asked questions

RAG or fine-tuning?
RAG for knowledge that changes. Fine-tuning for style, format, or tight latency. Many products use both.
Why is our current RAG bad?
Usually chunking, retrieval design, missing rerankers, or no evals. Fixable with a structured audit and golden set.
Can we keep data private?
Yes — VPC, private models, and access-controlled indexes when your compliance requirements demand it.
Which models do you support?
OpenAI, Anthropic, Google, and open models when appropriate. We choose based on quality, cost, and data-handling requirements — not a single-vendor pitch.
Do you only build chatbots?
No. RAG also powers search, copilots inside admin tools, and document Q&A embedded in SaaS products.

Related case studies

Selected work that used similar stacks and delivery patterns.

Curated Website Discovery App

Curated collection of websites to explore when bored — categories, random discovery, and editorial-style browsing without accounts.

Contractor Lead-to-Quote Platform

All-in-one ops platform for contractors: capture website leads, send quotes, manage projects and invoices, and convert more prospects into jobs.