Best RAG development companies in 2026
Any firm can demo retrieval on clean text. The project is decided by what happens to your scanned contracts and your tables that break across pages.
The short answer
Choose a RAG development company on two things: how it handles your messiest documents, and how it proves answer quality. Parsing and evaluation decide these projects long before the model does.
Hashlogics builds retrieval systems over insurance documents, clinical trial criteria and ESG research, so we are one of the firms in this category.
Send any shortlist twenty of your worst documents. The responses will separate the field faster than any proposal.
Firms that clear these signals today
A working shortlist, not a ranked contest. Test each one against the signals below before you call.
- 01
Hashlogics
Retrieval over regulated, messy documents
We build retrieval for insurance audits, clinical trial matching and ESG research, where documents arrive scanned, inconsistent and change format without warning. We are listing ourselves here so you can hold us to the same test as everyone else on this page.
Best for
- Regulated document sets where wrong answers carry real cost
Not for
- Teams wanting the largest possible delivery bench
- Focus
- Parsing, permissions, evaluation
- 02
Thoughtworks
Global software consultancy with a data and AI engineering practice
This long-established consultancy publishes widely on data engineering and applied AI. It staffs enterprise RAG work as part of a broader software delivery practice.
Best for
- Large enterprises wanting one vendor across the wider platform, not just retrieval
Not for
- Small corpora or a single narrow retrieval build
- Focus
- Enterprise data and AI engineering
- 03
EPAM Systems
Large-scale engineering firm with dedicated generative AI delivery teams
This publicly traded engineering firm operates at large scale. Its named practice areas cover generative AI and data platforms for enterprise clients in regulated industries.
Best for
- Enterprises needing a large delivery team across multiple workstreams
Not for
- Buyers wanting a small, senior-only team on one system
- Focus
- Enterprise-scale AI and data delivery
- 04
Slalom
US consulting firm with a named generative AI practice
This consulting firm built its name on cloud and data modernization work. Its generative AI practice includes retrieval-grounded assistants for enterprise clients.
Best for
- Enterprises already running major cloud programs who want retrieval added to that footprint
Not for
- Buyers without an existing cloud or data modernization relationship
- Focus
- Cloud, data and generative AI consulting
- 05
Persistent Systems
Enterprise software firm with a data and AI engineering line of business
This publicly listed software firm runs a dedicated data and AI engineering line of business. It delivers platform-level work for healthcare, financial services and technology clients.
Best for
- Buyers wanting retrieval bundled with broader platform modernization
Not for
- A standalone retrieval project with no wider platform scope
- Focus
- Enterprise platform and AI engineering
How this was assessed, and our stake in it
Verified
We rank buying signals rather than company names. No public record shows which firm shipped which retrieval system, and directory listings rest on self-reported profiles. Signals you can test on any vendor are more useful than names nobody can verify.
The signals come from retrieval systems we run in production. Across those builds the pattern held: parsing consumed the most effort, chunking moved answer quality the most, and the vector store choice mattered least.
Hashlogics competes for this work, and we say so plainly. Our retrieval systems are listed below so you can apply these same tests to us.
- Document handling
- What they do with a scanned PDF whose table breaks across two pages.
- Quality proof
- How they show answers are right before your users find out they are not.
- Permission awareness
- Whether retrieval respects who is allowed to see which document.
- Honesty about limits
- Whether they tell you which questions retrieval will answer badly.
Reading a RAG vendor's answers
The same four questions, answered two ways.
| Ask about | A demo answer | A production answer |
|---|---|---|
| Your scanned PDFs | We support PDF | Asks to see twenty of the worst ones |
| Chunking | We split the text | Explains why size depends on your documents |
| Quality | It works well | Describes an eval set and who wrote it |
| Permissions | We can filter | Filters inside the query, not after |
| Limits | It handles anything | Names questions RAG answers badly |
Ranked by what each signal predicts
Test these in order. Most shortlists resolve by the third.
- 01
They ask to see your worst documents first
The clearest sign of real experience
A firm that has shipped retrieval asks for the difficult files before quoting. Scanned contracts, forms with handwriting, tables split across pages, files where the text layer is missing entirely.
The reason is that parsing is where the effort concentrates. Text extracted badly produces confident wrong answers, and no amount of prompt work later recovers information that never made it out of the document.
We build retrieval over insurance audit documents for PremiumAudit and clinical trial criteria for TrialTriage. In both, getting clean structured text out of the source was the largest part of the work.
Best for
- Corpora with scans, forms or inconsistent layouts
- Any project where documents come from many sources
Not for
- Clean text already sitting in a database
- Test
- Send twenty hard files
- 02
An evaluation set they can describe
How wrong answers get caught first
Ask how they will prove the system is right before launch. The strong answer is a set of real questions with known correct answers, agreed with your experts and run on every change.
Retrieval fails in a way that looks like success. The system returns a fluent paragraph drawn from the wrong document. Nobody notices without a test that checks the source as well as the answer.
Ask who writes the questions. In insurance that should be an auditor; in clinical work, a nurse. A set written by engineers alone measures what engineers assumed rather than what your business needs.
Best for
- Answers customers or regulators will rely on
- Systems that keep changing after launch
Not for
- Internal exploration tools with no accuracy requirement
- Ask
- Who writes the questions
- 03
Chunking decided by your documents
The largest lever on answer quality
Ask how they decide chunk size. A good answer refuses a fixed number and talks about your document structure. A clause in a contract and a paragraph in a research report want different treatment.
Splitting text so one idea stays whole is the single largest quality gain in most retrieval systems. Split badly and the model gets half a definition, then answers confidently from the half it received.
Reranking is the natural follow-up. A firm that reranks passages before sending them to the model is doing the step most teams skip. It shows in the results.
Best for
- Structured documents like contracts and policies
- Corpora mixing long and short source material
Not for
- Short uniform records where any split works
- Listen for
- Chunking plus reranking
- 04
Permissions handled inside retrieval
The failure that becomes a disclosure
Ask how the system stops one user seeing another's documents. The answer must place the permission filter inside the retrieval query itself.
Filtering afterwards is not a control. Once a restricted passage reaches the model, it has shaped the answer. Trimming the citation list later hides that rather than preventing it.
Supabase documents row-level security for exactly this, which lets retrieval reuse the policies your application already enforces. We rely on that pattern where per-user isolation matters.
Best for
- Multi-tenant products and regulated document sets
- Anything holding client or patient records
Not for
- Public corpora where everyone sees everything
- Requirement
- Filter inside the query
- 05
A straight answer about what RAG does badly
Expertise shows in the limits named
Ask which questions the system will answer badly. Firms with production experience answer quickly, because they have watched it happen.
The usual list is short and specific. Questions needing a calculation across many records. Questions about what is absent from the documents. Questions whose answer changed last week in a system the corpus does not cover.
A vendor claiming retrieval handles all of those has either not shipped one or is not telling you. Both are useful to learn on the first call.
Best for
- Buyers deciding scope before committing budget
- Projects where expectations need setting internally
Not for
- Teams who have already validated the use case
- Ask
- What will this answer badly
- ParseYour worst documents, not your best.
- ChunkOne idea stays whole.
- FilterPermissions inside the query.
- RerankThe step most vendors skip.
- EvaluateExperts write the questions.
Ask a vendor to walk these five for your documents. The vague box is the risk.
When retrieval is not what you need
Retrieval answers questions from documents, so it is the wrong tool for questions answered by calculation. Counting or totalling across many records is a database job. Retrieval will find a passage and sound confident anyway.
Small corpora deserve a check too. When everything fits comfortably in a model's context window, sending it directly can outperform a pipeline, and it costs far less to maintain.
- 01If the answer is a total your database can compute, query the database.
- 02If the corpus is small and stable, test a direct approach before building retrieval.
- 03If the questions are really about what changed, you want change tracking, not search.
Retrieval over document sets that fight back
PremiumAudit.io
AI automation for smarter insurance premium audits.
Read the case study →
TrialTriage
AI clinical trial matching for oncology nurses and insurers.
Read the case study →
Greenlight
AI ESG and sustainability research platform.
Read the case study →
IRS Escape Plan
AI tax strategy and planning for high earners.
Read the case study →
“I am extremely happy with the results and would highly recommend Hashlogics to anyone.”
Daniel Khin · CEO, PremiumAudit.io
Send us your twenty worst documents
We will tell you what parses cleanly, what needs work, and which questions retrieval will answer badly. Scoping calls cost nothing.
Questions buyers ask
01What should we send a RAG vendor to test them?+
Twenty of your most difficult documents and ten real questions with answers you already know. Firms with production experience will come back with specific observations about the parsing. Firms without will return a generic proposal.
02Does the vendor need to know our industry?+
Useful but not decisive. The engineering transfers across sectors, and the parsing and evaluation work looks similar whether the documents are policies or protocols. What must come from your side is the definition of a correct answer.
03How do we know retrieval is working before we launch?+
Measure against a set of real questions with known answers, and check the source passage as well as the text. A system can produce a fluent correct-sounding answer from entirely the wrong document, and only source checking catches that.
04Can a vendor build this on our own infrastructure?+
Yes, and it is a fair requirement for sensitive documents. Retrieval can run entirely inside your environment, with the vector index in a database you already operate. Ask early, because it shapes the architecture rather than being added later.
05What usually goes wrong after a RAG system launches?+
Documents change format and quality drops quietly. A supplier alters a template, parsing degrades, and answers get worse without any code changing. Ask any vendor how they monitor for that, because it is the most common post-launch failure.
06Why does this guide rank signals instead of naming companies?+
No public record shows which firm shipped which retrieval system, and directory listings rest on self-reported profiles nobody can verify. A signal you can test on any vendor in an hour is more useful than a name you have to take on trust.
