🏆The 10 companies
We build RAG systems, so Codazz appears on this list and you should weigh that accordingly. Third-party firms are described from their public positioning — we have not invented ratings or client numbers for other people's businesses.
| # | Company | Strongest at | Best fit for |
|---|---|---|---|
| 1 | Codazz | Permission-aware retrieval, agentic multi-hop search, cited answers | Regulated teams who must verify every answer |
| 2 | Mapletechlabs | ML engineering inside product teams | Fintech and healthtech knowledge products |
| 3 | TML (Tech Media Labs) | Enterprise knowledge platforms at scale | Large internal document estates |
| 4 | Townmedialabs | Consumer-facing search and discovery | Marketplaces and content platforms |
| 5 | LeewayHertz | Broad enterprise AI consulting | Organisations wanting one AI vendor |
| 6 | DataForest | Data engineering and pipeline foundations | Teams whose documents are not ingest-ready |
| 7 | SoluLab | Combined data, AI and blockchain engineering | Products with provenance requirements |
| 8 | Markovate | Discovery and AI product strategy | Teams still defining the use case |
| 9 | Azilen Technologies | Embedded long-term product teams | Extended-team engagements |
| 10 | Master of Code | Conversational front ends over knowledge bases | Customer-facing knowledge assistants |
🔍Why most RAG projects underdeliver
The pattern is consistent. A pilot works beautifully on twenty clean documents. It goes live against forty thousand real ones — scanned PDFs, three versions of the same policy, tables that lost their structure during parsing — and answer quality collapses. The team blames the model and tries a bigger one, which does not help, because the model was never the problem.
Chunking that ignores document structure
Splitting every 500 tokens cuts clauses in half and separates table headers from their rows. Contracts, policies and manuals have real structure — chunk along it, not against it.
Pure vector search on exact identifiers
Embedding similarity is poor at part numbers, clause references and error codes — exactly what enterprise users search for. Hybrid keyword-plus-vector retrieval with a reranking pass fixes this, and its absence is the most common single defect.
No retrieval evaluation
If you only measure whether the final answer was good, you cannot tell whether the system failed to find the document or found it and misread it. Those are completely different fixes. Measure retrieval separately.
Permissions bolted on afterwards
Entitlements must filter the retrieval set at query time. An agent instructed not to reveal something is not access control, and a knowledge assistant that surfaces a document to the wrong employee is a security incident.
Nobody owns freshness
A system quietly answering from last year's policy is worse than no system, because people have stopped double-checking it. Incremental re-indexing and stale-content alerting are not optional.
Ask any RAG vendor one question: "How do you measure retrieval quality separately from answer quality?" If they do not separate the two, they cannot diagnose their own failures.
💵What RAG development costs
A focused RAG system over a well-defined corpus typically runs six to ten weeks. Cost is driven far more by the state of your documents than by the AI work — a clean, well-structured document set with consistent metadata is fast, while a decade of scanned PDFs across three systems with no version control is not.
Expect the corpus audit to be the most valuable early deliverable regardless of what happens next. It routinely surfaces duplicate policies, superseded documents still in circulation, and outright contradictions. Several of our clients have said that finding those was worth the engagement on its own, before the retrieval system shipped.