Retrieval that finds the right answer, not just similar text.
Production RAG and knowledge systems: hybrid search, GraphRAG, document intelligence and evaluation, so your AI answers are grounded and citeable.
What is retrieval and knowledge engineering (RAG)?
Retrieval-augmented generation (RAG) grounds an AI system's answers in your own documents and data instead of the model's memory. Eazyware engineers production RAG with layout-aware document parsing, hybrid keyword-plus-vector search, re-ranking, GraphRAG for relationship-heavy domains, permission-aware retrieval and evaluation suites, so answers are accurate, citeable and measurable.
| Service line | AI-Powered Product Engineering |
|---|---|
| Engagement | Scoped build with milestones |
| Duration | Quoted after scoping; typically 8–16 weeks |
| Starting price | $14,000 |
| Typical range | $14,000 – $49,000 |
| Deliverables | 5 listed below |
| Delivered from | Bengaluru, India (IST, UK and US East hours) |
| Code ownership | Client owns code, infrastructure, prompts and documentation |
What problem does it solve?
Basic RAG demos well and fails in production: wrong chunks, stale indexes, no citations, no way to measure accuracy. The gap between 70% and 95% relevance is engineering.
How do we approach it?
Retrieval quality is engineered, not assumed. We audit the corpus first: formats, structure, ownership, how often it changes, who may see what. Ingestion uses layout-aware parsing so tables, headings and footnotes survive; chunking is chosen per document type rather than a single window size; metadata is enriched so filters work; and search is hybrid, keyword plus vector with a re-ranker, because vectors alone miss exact terms. Permissions are enforced at query time. A golden set of real questions with verified answers drives tuning, and the same set runs on every change so relevance never quietly regresses.
What do clients use it for?
- Internal knowledge assistants over wikis, tickets and drives
- Contract and policy search with citations
- Customer-facing documentation search
- GraphRAG over relationship-heavy data such as suppliers or cases
Is it the right fit?
Good fit when
- Organisations with large, changing document sets
- Products where grounded, citeable answers are required
- Teams whose first RAG attempt underperforms
Probably not when
- Small static FAQ sets
- Use-cases where a database query is the right tool
What do we build?
- Ingestion pipelines for PDFs, contracts, tickets, wikis, databases, Drive, Slack and email
- Document intelligence: layout-aware parsing, tables, OCR
- Chunking strategies, metadata enrichment, hybrid search and re-ranking
- GraphRAG and knowledge graphs for relationship-heavy domains
- Permission-aware retrieval
- Retrieval evals: recall, precision, groundedness
- Internal knowledge assistants and semantic search products
What you get
- Knowledge pipeline
- Retrieval API
- Evaluation suite
- Admin console for sources
- Assistant UI (optional)
How does the engagement work?
- 01
Corpus audit
- 02
Ingestion and parsing
- 03
Retrieval design
- 04
Eval harness
- 05
Tune
- 06
Deploy and schedule refresh
What does good look like?
Answers that are grounded and citeable, where a user can click through to the passage. Recall and precision numbers on the golden set rather than anecdotes. An index that refreshes on a schedule and on events so it is never stale. Zero permission violations, verified by test. And a system that scales from a pilot corpus to millions of documents without changing its architecture.
How does it compare?
| Eazyware | Typical agency | In-house hire | |
|---|---|---|---|
| Time to first result | Sprint Zero in 10 days, then a fixed-scope build | 6–12 weeks of discovery before a proposal | 3–6 months to hire, then ramp |
| Pricing model | Fixed scope, milestone billing, INR or USD | Time and materials, open-ended | Salaries, tooling, management overhead |
| AI depth | Multi-model, evals, cost routing, observability as standard | Often a single vendor API and a prompt | Depends entirely on who you can hire |
| Ownership | Client owns code, infra, prompts and docs | Sometimes retained or licensed back | Owned, but concentrated in one or two people |
| After launch | Care Plans with SLA and AI add-on | Change requests at hourly rates | Ongoing headcount whether or not there is work |
Which pitfalls do we design around?
Basic RAG fails on parsing, on one-size chunking, on vector-only search, on stale indexes, on ignoring permissions, and on having no way to measure relevance. Every one of those is a specific engineering decision we make differently, and the golden set is what keeps us honest.
What do we measure?
Every engagement is instrumented. These are the numbers you see in the dashboard and the monthly report, not claims on a website.
- Retrieval recall and precision on a golden set
- Groundedness of answers
- Index freshness
- Permission violations (target: zero)
Which technologies do we use?
- pgvector / Pinecone / Qdrant / Weaviate
- Unstructured / Docling
- LlamaIndex / LangChain
- Cross-encoder re-rankers
- Node.js / Python
Who does the work?
An AI engineer specialising in retrieval, a data engineer for ingestion and refresh pipelines, and an architect for the permission model.
What do you need to bring?
Access to the document sources (drives, wikis, ticket systems, databases), the permission model for who may see what, a set of real questions with verified answers to seed the golden set, and a view on how often each source changes.
Frequently asked questions
Vector DB or Postgres?
pgvector handles most workloads. We move to a dedicated store when scale demands.
How do you handle permissions?
Document-level ACLs enforced at retrieval time.
Refresh?
Scheduled and event-driven re-indexing.
Where does this fit?
Retrieval & Knowledge Engineering is part of our AI-Powered Product Engineering line. Not sure yet? Start with Sprint Zero, a ten-day discovery whose fee is credited to this build. See all pricing or talk to an engineer.