AI and data · RAG development
RAG development services that make an AI assistant answer from your data, accurately
Retrieval-augmented generation lets a language model answer questions using your documents, tickets and databases instead of guessing. We build it with the evaluation, privacy and cost controls that turn a demo into a product.
Short answer
RAG (retrieval-augmented generation) fetches the most relevant pieces of your own content and gives them to a language model so its answers are grounded in your data. It is the standard way to build support assistants, internal knowledge search and document Q&A. A production-grade RAG feature from Innovation Insight costs $12k to $60k over four to twelve weeks, includes an evaluation set and guardrails, and can run entirely inside your cloud account.
What retrieval-augmented generation is, for buyers
A large language model on its own knows what it was trained on and nothing about your company. Ask it about your refund policy and it will invent one. RAG solves this by adding a retrieval step: when a user asks a question, the system first searches your content for the most relevant passages, then hands those passages to the model with the question, and the model writes an answer that cites them. The model does the language; your data supplies the facts. It is cheaper and faster than fine-tuning, updates the moment your documents change, and lets you show sources so people can trust the answer.
Where RAG earns its keep
- Customer support assistants that answer from your help centre, product docs and past tickets, and escalate when unsure.
- Internal knowledge search across wikis, contracts, policies and Slack history, with permissions respected.
- Document Q&A for legal, compliance, procurement and engineering teams working with long PDFs.
- Sales enablement that drafts answers to security questionnaires and RFPs from approved content.
- Product features that let your users ask questions about their own data inside your application.
The architecture we build
| Stage | What happens | Typical choices | What can go wrong without care |
|---|---|---|---|
| Ingestion | Documents, tickets and database rows are collected and normalised | Connectors to Google Drive, Confluence, Zendesk, S3, Postgres | Stale content, duplicates, PDFs read as noise |
| Chunking | Content is split into passages small enough to retrieve and large enough to make sense | Semantic or heading-aware splitting, 300 to 800 tokens with overlap | Answers that cut off mid-thought |
| Embeddings | Each chunk is turned into a vector that captures its meaning | OpenAI, Cohere, Voyage or open models such as bge | Cost surprises on large corpora, model drift |
| Vector store | Vectors and metadata are indexed for fast similarity search | pgvector, Pinecone, Weaviate, OpenSearch | No permission filtering, no metadata for freshness |
| Retrieval | The best chunks for a query are found, often hybrid keyword plus vector, then reranked | Hybrid search, reranker models, query rewriting | Right answer exists but is never retrieved |
| Generation | The model writes an answer from the retrieved context with citations | GPT, Claude, Gemini or a hosted open model in your VPC | Hallucination when context is thin, prompt injection |
| Evaluation | Answers are scored for correctness, groundedness and safety on a fixed test set | Golden question sets, LLM-as-judge with human review | Silent regressions after a prompt or model change |
The stage most teams skip is evaluation. Without a test set of real questions and accepted answers you cannot tell whether a change made the assistant better or worse. We build the evaluation set in the first two weeks and run it in CI, so every prompt tweak, model swap or new data source is measured before users see it.
Privacy and where your data lives
RAG systems touch your most sensitive content, so we design the data path before the model. Options range from fully managed APIs with zero-retention agreements, to embeddings and vector stores inside your own cloud account, to open models running in your VPC so nothing leaves your network. Permission-aware retrieval ensures a user only gets answers from documents they are allowed to see, which is the single most common gap in internal assistants we are asked to fix.
What it costs
| Tier | What it includes | Range | Timeline |
|---|---|---|---|
| Assistant over one source | Single connector, hybrid retrieval, citations, evaluation set of 50 questions, basic guardrails | $12k to $20k | 4 to 6 weeks |
| Multi-source knowledge assistant | Several connectors, permission filtering, reranking, admin dashboard, evaluation of 150 or more questions | $20k to $40k | 6 to 10 weeks |
| Product-embedded RAG | Multi-tenant, per-customer data isolation, usage metering, monitoring, cost controls, on-call runbooks | $35k to $60k | 8 to 12 weeks |
Running costs are separate and usually modest: model and embedding fees scale with usage, and a retainer covers monitoring and tuning. We publish an estimate of monthly model spend with every proposal, and build token budgets and caching in so a busy month does not become a surprise invoice.
What we deliver
- Ingestion pipelines with scheduling, change detection and deduplication.
- A retrieval layer with hybrid search, reranking and permission filters.
- Prompt and model configuration under version control, with a fallback model.
- An evaluation harness and golden set, run in CI with a dashboard of groundedness and accuracy over time.
- Guardrails for prompt injection, off-topic requests and sensitive data, with escalation paths.
- Observability: traces for every answer showing which chunks were used and what it cost.
- Documentation and a hand-over session so your team can extend the system.
How we work on RAG projects
- Discovery (1 to 2 weeks): collect 50 to 150 real questions, audit the content sources, agree privacy constraints and success metrics.
- Retrieval first (sprint 1): ingest, chunk, embed and measure retrieval quality against the golden set.
- Generation and guardrails (sprint 2): prompts, citations, refusal behaviour, injection defences.
- Product integration (sprints 3 to 4): UI or API, permissions, monitoring, cost controls, load testing.
- Launch and tuning: weekly evaluation runs, content gap reports, model updates as they arrive.
Innovation Insight builds RAG and other LLM features for SaaS and enterprise clients from Lahore and Sialkot, with engineers who have shipped retrieval systems over contracts, support archives and product documentation. See the AI agent development page for systems that also take actions, and the LLM integration page for the model layer on its own.
Signs your RAG assistant is not production-ready
- It answers confidently when the relevant document was never retrieved.
- Two users with different permissions get the same answers from the same restricted document.
- Nobody can say whether last week's prompt change made answers better or worse.
- Costs per conversation are unknown, or spiked after a busy day.
- Answers cite nothing, so users cannot check them and stop trusting them.
Each of those is a design gap rather than a model limitation, and each is covered by the retrieval, permission, evaluation and observability work described above. Most of our RAG engagements that start as rescues begin with exactly this list.
Keeping the assistant good after launch
Content changes, models change and users ask new questions. A healthy RAG system has a weekly evaluation run against the golden set, a content gap report that shows the questions retrieval could not answer, and a process for adding approved answers to the corpus. When a new model version arrives we run the same evaluation before switching, so the upgrade is a measured decision rather than a hope. This is the work a maintenance retainer covers, and it is usually a few hours a week rather than a full-time role.
| Ongoing cost | Typical monthly range | Driver |
|---|---|---|
| Model and embedding fees | $100 to $1,500 | Conversations per month and context size |
| Vector store and hosting | $50 to $600 | Corpus size and traffic |
| Monitoring and tuning retainer | $1,500 to $4,000 | Evaluation cadence, content updates, model upgrades |
What to bring to the first call
Bring 20 to 30 real questions your users or staff actually ask, a sample of the documents that should answer them, and a note on where that data is allowed to live. With those three inputs we can tell you within a week whether RAG will work for your case, which model and hosting option fit your privacy constraints, and what the first release should cost. If you already have a prototype that disappoints, bring that too: most of the fixes are in retrieval and evaluation rather than in the model, and they are cheaper than starting again.
Next step
Tell us what you're building and get a written estimate.
A senior engineer replies within one business day. NDA on request.
Products we've shipped, and what happened next.
Case studies written from the technical documentation of each project: the stack, the scale and the outcome.
Questions we get asked a lot.
Is RAG better than fine-tuning?
For knowledge that changes, yes. Fine-tuning bakes information into the model and goes stale; RAG reads your current content at query time and can cite it. Fine-tuning is useful for style and format, and the two can be combined.
Which vector database do you recommend?
For most teams pgvector inside the Postgres you already run, because it keeps data and permissions in one place. Dedicated stores such as Pinecone or Weaviate make sense at tens of millions of chunks or when you need managed scaling.
Can it run without sending data to OpenAI or Anthropic?
Yes. Open models such as Llama or Mistral can run in your VPC for both embeddings and generation, at some cost in answer quality and operations effort. We help you choose based on the sensitivity of the data.
How do you stop it making things up?
Grounded prompting with citations, refusal when retrieval confidence is low, and an evaluation set that measures groundedness on every change. No system is perfect, so we also design the UI to show sources.
How long until we have something usable?
A working assistant over one source typically demos in three weeks and is production-ready in four to six.
What does it cost to run each month?
Model and embedding fees for a support assistant handling a few thousand conversations a month are usually in the low hundreds of dollars. Monitoring and tuning are covered by a retainer from $1,500 a month.