A RAG system you can run, change and check without us.
An off-the-shelf assistant is fine for a small, clean set of files. These are the points where teams usually need something built for their data.
The demo on twenty files worked. On the full document set, answers became vague or wrong.
Answers depend on a wiki, a ticket system, shared drives and a database, each with its own format and access rules.
Support, legal, finance or operations staff act on what the system says, so every answer needs a source they can open.
Different teams, customers or tenants must only get answers from the content they are allowed to see.
New documents, edits and deletions have to reach the index quickly, without re-processing everything.
The answers have to appear in your app, with your login, your design and your usage limits, not in a separate chat tool.
We read a sample of your content and your users' real questions, and tell you what RAG can answer well, what it cannot, and what has to be cleaned first.
Pipelines that pull content from your systems, parse it, split it and keep the index in step with the source as documents change.
Hybrid search, reranking, metadata filters and query rewriting, tuned on your questions until the right passages come back.
Prompts and model choice for answers that stay inside the retrieved text, cite it, and say so when the documents do not cover the question.
A test set built with your subject experts, scoring for retrieval and for answers, and a report you can read without an ML background.
Per-user and per-tenant filtering at retrieval time, safeguards against instructions hidden in documents, and control over what reaches a model provider.
An API and infrastructure in your cloud account, with environments, CI, monitoring and a way to roll back a bad change.
A search or chat interface, or an API your own product calls. Web and mobile clients come from the same team.
Each part can fail on its own, so each one is built and tested separately before we judge the whole.
Connectors to your drives, wikis, ticket systems and databases
Parsing of PDFs, scans, tables and slides, checked on your files
Chunking that keeps headings, dates and document titles
Incremental sync: edits and deletions reach the index
Source permissions stored with every chunk
Keyword and vector search combined
Reranking of candidates before the answer step
Filters by date, document type, team or customer
Query rewriting for follow-up and vague questions
Results limited to what the user may see
Answers grounded in the retrieved passages
A link to the source next to each claim
A plain "not found in your documents" when evidence is missing
Model chosen by quality, speed and cost on your questions
Retrieved text handled as data, with checks against hidden instructions
A test set of real questions with expected answers and sources
Retrieval and answer quality scored separately
The test set re-run on every change
Logging of questions, retrieved passages and answers
Cost and response time tracked per request
Python and FastAPI for pipelines and the API, PostgreSQL for data, AWS, Docker and Kubernetes for deployment. The vector store, the orchestration library and the model provider are chosen with you per project: for example PostgreSQL with a vector extension or a dedicated vector database, a library such as LangChain or LlamaIndex or plain code, a hosted model or one you run yourself.
Python
FastAPI
PostgreSQL
AWS
Docker
Kubernetes
TypeScript
ReactYou want document Q&A or search inside your SaaS product, with each customer's data kept apart.
Staff lose hours searching policies, contracts, manuals and tickets spread across systems with different access rules.
Your product idea depends on accurate answers from a specific body of knowledge, and a demo is not enough for customers.
An internal proof of concept exists, but accuracy, permissions or cost keep it from going live.
Check it on a sample of your data first.
Build the full system, end to end.
Turn it into something you can release.
Measure it, find the cause, fix it.
Connect RAG to your app and data.
Outsource RAG development to one team.
We look at real documents and real questions, agree what a correct answer is, and write down what is out of scope.
Together with your experts we collect questions with expected answers and sources, then measure a simple first pipeline against them.
Ingestion, retrieval and generation are improved one at a time, with the test set re-run after each change so you see what helped.
Access rules, the API, the interface or the connection to your product, and security checks on documents and prompts.
A limited group uses it on real work. Their failed questions go into the test set, then we release to everyone with monitoring in place.
New sources, model updates and content changes are handled against the same test set, by us or by your team after handover.
2-week fixed-price start
Fixed price. The build is quoted after it.
For teams that want proof on their own data, and a clear scope, before committing to a full build.
Review of your sources and access rules
First test set from your real questions
Baseline pipeline on a sample of your data
Architecture and running-cost outline
Scope and estimate for the full system
Engineers and QA on your system, month by month
Monthly, team-based.
For the full build and for live systems that keep getting new sources, users and requirements.
Python and backend engineers
Web and mobile developers when you need an interface
QA for answers as well as for the app
Test-set results shared at every demo
Team size adjusted as the scope changes
An AI-powered payroll validation platform that extracts data from salary slips, verifies payroll calculations, and automates reporting and approval workflows for HR and finance teams
An AI-powered fashion commerce platform that helps retailers deliver personalized shopping experiences through virtual styling, AI recommendations, image search, and intelligent product discovery.
Verified reviews from Clutch. The projects differ, the way we work is the same.
“They were incredibly fast and efficient at scoping the project and then delivered within very tight deadlines.”
“I was impressed with how quickly we got a stable and well-designed product.”
“The team met all commitments, aligning with our company's objectives.”
RAG is mostly data engineering, search and testing around a model. That is the kind of work we do on AI products.
For an AI payroll platform we built PDF data extraction from salary slips with automated validation. Salary calculation errors fell by 17%.
For QLode, an AI fashion commerce product, we built image and text search and the backend behind its AI-driven flows.
70+ products launched for startups, SMBs and product companies.
85% of our clients come back with new work.
Projects can start in about two weeks.
We agree how quality is measured before we change anything.
You see which questions fail and why, not just an average score.
Vector store, framework and model are chosen for your case and can be swapped.
If plain search or a simpler tool would do the job, we tell you.
Our team in Poland shares part of the US working day for calls and demos.
No sales pitch
Tell us about your documents and questions
What content should the system answer from, who will ask, and what do you have today? We'll reply with a suggested approach and a quote. If RAG is the wrong tool, we'll say so.
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