Scoping & question set
We agree the document set, 50–150 real questions with expected answers, and what “good enough” means for your use case.
AI & Machine Learning
RAG proof of concept on your own documents
Know whether it works on your documents before you pay for a full build.
Demos on sample PDFs prove very little. Contracts, regulations and technical manuals have tables, cross-references, scanned pages and exceptions that break generic assistants. In two to four weeks we build a working prototype on a representative slice of your own documents and measure how well it answers — so the decision to go further rests on evidence.
An honest answer in weeks, not a six-month experiment.
A proof of concept answers one question: can retrieval-augmented generation answer your team’s real questions from your real documents, accurately enough to be useful? We agree the questions and the success criteria with you at the start, build the smallest system that can answer them, and test it against those criteria — not against questions we picked because they work.
You get three things at the end: a prototype your team can try, a benchmark report that shows accuracy on the agreed questions with the failures named and explained, and a recommendation. If the answer is go, the report doubles as the plan and estimate for production. If it is no-go, you learn why for the price of a few weeks instead of a failed project.
RAG Proof of Concept
What we offer
Document processing
Parsing that respects the structure of your documents — sections, tables, clauses, scans — because retrieval quality starts here.
Working prototype
A simple chat or search interface over your documents, with answers that cite the passage they came from.
Benchmark report
Accuracy on the agreed questions, retrieval quality, response time and cost per question, with each failure case shown and explained.
Go / no-go recommendation
A straight recommendation, and for a go: the architecture, effort and budget for a production system.
Your choice of hosting
Hosted models under terms that exclude training on your data, EU-region cloud, or open models inside your own environment.
When it’s the right fit
- You want an AI assistant over contracts, regulations or manuals but are not sure it can be accurate enough
- A vendor demo looked good, and you want to see results on your own documents before buying
- Leadership needs evidence and a budget before approving an AI project
- An earlier chatbot attempt gave vague or wrong answers and you want to know why
How we deliver
- 01Week 1: scope & dataAgree the questions and success criteria, receive a representative document sample under NDA, and set up processing.
- 02Week 2: buildRetrieval, answer generation with citations, and a simple interface for your team to try.
- 03Week 3: measureRun the full question set, analyse every failure, and tune what can be tuned within the scope.
- 04Week 4: report & decisionWalk-through of the prototype and benchmark, and a go or no-go recommendation with a production plan.
Technology
Tools we use for RAG Proof of Concept
- Python
- FastAPI
- pgvector
- Elasticsearch
- Docling
- OpenAI
- Anthropic Claude
- AWS Bedrock
- Open-source LLMs
Frequently asked questions
Why a fixed price?
Because the scope is fixed: a defined document sample, an agreed question set and a report at the end. You know the cost before we start, and there are no open-ended hours.
How many documents do you need?
A representative sample rather than everything — typically a few hundred pages that include the hard cases: tables, scans, cross-references and exceptions. A good sample predicts production behaviour far better than a large easy one.
What happens after the proof of concept?
If the result is go, the report already contains the architecture, effort and budget for production, and the prototype becomes its starting point. You are free to build it with us or with anyone else.
Is our data safe?
We sign an NDA before any document is shared, can work in EU-region infrastructure or entirely in your environment, and use model providers whose terms exclude training on your data. Everything is deleted at the end unless you ask us to keep it.
Next step
Tell us what you want to build.
A free 30-minute consultation with an engineer — no obligation, reply within one business day.
Contact us