Hire RAG developers

Hire RAG developers. Answers that cite their sources.

Q: What is RAG?

Retrieval-augmented generation. Before the model answers, the system searches your documents for the passages that matter and hands them to the model. The answer comes from your content, with a link back to the source.

Q: Do I need a RAG specialist or a general AI engineer?

Often the same person. Our AI engineers build RAG alongside agents and LLM features, so you can hire one engineer for the whole feature. Tell us what's in your documents and we'll say which profiles fit.

Q: RAG or fine-tuning?

For most products, RAG first. It works with documents that change, shows its sources and keeps your data out of model weights. Fine-tuning helps with tone and narrow formats. Sometimes the answer is both.

Q: Our documents are messy PDFs and scans. Will that work?

Usually, with the right parsing. Your engineer uses layout-aware and vision-based extraction for complex PDFs, and tests it on your hardest documents first.

Q: How do you know the answers are right?

The engineer builds an evaluation set from your real questions and runs it on every change. In production every query is traced, and weak answers become new test cases.

Q: Where does our data go?

Only where your rules allow. We work on accounts you control, can remove personal data before anything reaches a cloud model, and can use open-weight models on your own infrastructure when data can't leave.

Hire remote AI engineers who have built retrieval-augmented generation on real documents: parsing, search, cited answers and the tests that show it's right. They join your team within a week, and the first 7 days are free.

Start your 7-day free trial Read the BoardLens case study

What they build. The parts that decide quality.

Most RAG problems aren't model problems. They're messy PDFs, poor chunking and a search that misses the one paragraph that matters. That's where your engineer spends their time.

Tools we use

Agentic RAGHybrid searchRerankersLayout-aware parsingunstructured.ioLlamaParsePineconepgvectorLangGraphLangfuse

What they cover

Document parsing that keeps tables, headings and layout, including scans and complex PDFs.

Hybrid search (keywords plus vectors) with a reranker, so the right passage comes first.

Answers that cite their exact source, so users can check them.

Permissions carried through, so people only retrieve what they're allowed to see.

An evaluation set built from your real questions, run on every change.

Personal data removed before anything reaches a cloud model, where your rules need it.

Document AI they've built. On real documents.

boardlens.ai

AI platform · Governance · Boards and funds

An AI platform that turns long board packs into clear briefs, risks and answers.

BoardLens uses RAG over long board packs. Board Brief puts the essentials in one view, next to market signals and the follow-ups between meetings. In beta, where BoardLens reports about $7B in assets under management.

In betaRead the case study →

go-legal.ai

Legal AI · United Kingdom

A legal AI platform that drafts, reviews and explains documents for UK individuals and small businesses.

The live platform parses legal documents with unstructured.io and uses Pinecone for retrieval. LangGraph agents sit behind a multi-agent contract review that flags risky clauses and explains them.

Live; full rebuild in progressRead the case study →

AI · A European wellness groupDocument intelligenceVision-first extraction of complex PDF reports, cross-checked by a second model. Personal data is removed on a local service before any cloud AI sees it. Tested against a 10-document adversarial set.Proof of concept · client not named

Results BoardLens reported. In their words, on Clutch.

These numbers come from BoardLens's own review on Clutch.

Agentic AI for board directorsBoardLens · Jan to Jun 2025 · 5.0 / 5

< 120 sto analyse a 40-page board pack (P95)

80%+of invited directors ran a report in week one of the private beta

~60%less manual slide review (self-reported, 12 users)

8.9 / 10usability score (10 participants)

$0.60or less in model cost per analysis

0P0 or P1 findings in an external penetration test

How your engineer builds it. Measured at every step.

Audit

Start with the documents.

What you actually have: formats, scans, tables, how often it changes, and who may see what.

Evaluate

Write the test before the pipeline.

Your engineer collects real questions with known answers. That set tells them whether each change helps or hurts.

Build

Parse, index, retrieve, answer.

Parsing and chunking tuned to your documents, hybrid search with reranking, and answers that cite their source.

Monitor

Watch it in production.

Every query is traced. Weak answers become new test cases, and the cost of each query is tracked.

Your engineer joins your repo and standups, and your lead approves every change. How we work →

Engineers you can hire. Profiles you can check.

Arslan HaroonSenior Full-Stack AI Engineer · Tech Lead

Tech lead who has led 10+ AI-driven products, from agentic systems to RAG architectures.

Agentic AI systemsLLM workflowsRAG architecturesSee the full profile →

Ali Abyer NasirLead Software Engineer

Leads backend, platform and cloud work: RAG pipelines, LLM integrations and voice-agent workflows.

RAG pipelinesLLM integrationsVoice-agent workflowsSee the full profile →

Questions we get asked.

Retrieval-augmented generation. Before the model answers, the system searches your documents for the passages that matter and hands them to the model. The answer comes from your content, with a link back to the source.

Often the same person. Our AI engineers build RAG alongside agents and LLM features, so you can hire one engineer for the whole feature. Tell us what's in your documents and we'll say which profiles fit.

For most products, RAG first. It works with documents that change, shows its sources and keeps your data out of model weights. Fine-tuning helps with tone and narrow formats. Sometimes the answer is both.

Usually, with the right parsing. Your engineer uses layout-aware and vision-based extraction for complex PDFs, and tests it on your hardest documents first.

The engineer builds an evaluation set from your real questions and runs it on every change. In production every query is traced, and weak answers become new test cases.

Only where your rules allow. We work on accounts you control, can remove personal data before anything reaches a cloud model, and can use open-weight models on your own infrastructure when data can't leave.

More ways to hire

Documents nobody can search?

Tell us what's in them and who needs answers. We'll say honestly what RAG can do with them.

Book a free call