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RAG & LLM development — pgvector, citations & enterprise search
Retrieval that respects who can see what: chunking, embeddings, citations, and filters in one query — so support and sales answers trace to real sources.
RAG & LLM development for teams comparing vendors
Searchers often compare RAG versus fine-tuning. Most production knowledge assistants start with RAG plus good chunking and metadata filters. We explain ingestion, re-ranking options, and evaluation so SEO pages match the questions CTOs actually ask. If your content is messy or permissions are complex, we surface that in discovery — it affects cost more than model choice.
- RAG development services
- LLM retrieval augmented generation
- pgvector RAG developers
- enterprise ChatGPT search
- hybrid vector search
- document AI citations
- RAG vs fine tuning
- BalochDev RAG
When RAG is the right LLM pattern
RAG fits when answers must cite internal knowledge that changes often.
Usually works well
- Support deflection with links to policy and ticketing context.
- Sales enablement across brochures, decks, and win/loss notes.
- Internal research assistants for engineers reading long specs.
Proceed carefully
- If there is no authoritative source — models will invent plausible structure.
- If permissions are undefined, delaying RAG is cheaper than leaking data.
What buyers get on this engagement
Permission-aware
We mirror your access model — not a flat corpus if your org is not flat.
Measurable quality
Starter evaluation sets so updates do not silently degrade answers.
Stack fit
Postgres + pgvector, managed vector DBs, or Cloud edge patterns — chosen for your ops.
Cost-aware pipelines
Batch embeddings and caching so monthly bills stay predictable.
Phases from brief to handoff
Like our practice hubs and technology stack pages, we keep scope readable: written milestones, demo checkpoints, and assumed budgets before long commits — so procurement and founders stay aligned.
Source audit
Where content lives, refresh cadence, and legal retention rules.
Ingestion MVP
Pipeline for a representative slice with filters and citations.
Product integration
UI, auth, analytics, and rate limits in your app.
Tune & expand
Re-rankers, synonyms, admin tools, and new sources.
Typical bands before your final quote
| Phase / package | What is included | Typical timeline | Assumed from |
|---|---|---|---|
| RAG discovery | Corpus map, permission model sketch, eval plan | 1 wk | ~$2.5k–$7k |
| MVP RAG assistant | Ingestion, hybrid search API, chat UI or widget, basic eval | 4–8 wks | ~$15k–$48k |
| Enterprise RAG | SSO, multi-tenant filters, SLAs, monitoring, expanded corpora | 8–16+ wks | ~$48k–$120k+ |
Assumed bands are typical before unusual integrations, heavy compliance, or bespoke UI — we confirm fees in writing after a short brief. Most engagements are milestone-invoiced in USD.
Often paired services
What “done” looks like on a RAG program
Buyers should know which artifacts they receive — not just “a chatbot.”
- Ingestion jobs or streaming connectors
- Vector + metadata schema
- Query API with logging
- Admin screen or scripts for reindex
- Evaluation spreadsheet or notebook + pass criteria
- Deployment guide for your infra
What shipping looks like
Questions people ask before signing
For case studies, see the portfolio — and the parent AI & Intelligence hub.