We score each stage separately. A single overall score tells you the system is bad, not what to change.
Any one of these means you are making decisions about the system without data.
When leadership asks how often the answers are correct, the honest reply is "we don't know".
People open the source document after each reply, so the system saves them no time.
Small changes in wording produce replies that contradict each other.
The answer links to a document that does not say what the answer claims.
You want to switch the model, the embeddings or the vector store and have no way to compare before and after.
Nobody can show who is able to retrieve which documents, or what happens when a document contains instructions for the model.
We collect questions from your logs and your subject experts, write the expected answer and the passage that supports it, and add questions the system should decline.
For each question we check whether the supporting passage is retrieved and how high it ranks. That gives recall and ranking scores for the search step alone.
We score answers for correctness against the expected answer and for faithfulness: every claim must be supported by the retrieved text.
Automated scoring with an LLM judge is fast, but it has to agree with people. We compare it with human grades on a sample and adjust it before trusting its numbers.
Each failed question is assigned to a cause: content, parsing, chunking, retrieval, ranking or prompt. You see which cause accounts for most failures.
We fix the largest causes first: chunking strategy, metadata, hybrid search, reranking, query rewriting, prompts. Each change is rerun against the full test set.
We list the documents that produce wrong answers (outdated, duplicated, contradictory or badly parsed) and tell your content owners what to fix, merge or remove.
We test prompt injection through user input and through documents, check that retrieval respects user permissions, and review what personal data lands in logs and prompts.
The results stay with you: in your repository, running in your pipeline, readable by people who were not in the project.
Scores for retrieval and for answers, per question type
Failed questions grouped by cause, with examples
A ranked list of fixes with expected effort
The test set in your repository, in a plain format
An evaluation run in CI on every pipeline change
Before and after scores for each fix we make
Prompt injection tests and their results
Permission checks on retrieval, per user role
Data-leak paths found, with a fix for each
We evaluate the system you already have, whatever it is built on. The evaluation code is usually Python and runs in your CI. The evaluation framework, the judge model and any tracing tool are chosen with you per project, for example between an open-source library such as Ragas or DeepEval and a small custom harness.
Python
FastAPI
PostgreSQL
Node.js
Docker
AWS
Kubernetes
TypeScriptA RAG feature is in your product and customers report wrong answers. You need to know how bad it is and what to fix first.
Your prototype impressed in the demo and now fails on real documents. You need numbers before the next release or investor update.
Employees stopped using the knowledge assistant. You need evidence to decide between repairing it and replacing it.
You have to sign off on a system that reads internal documents and want its injection and access risks tested first.
A baseline for retrieval and answer quality on your questions.
Know which pipeline step produces most wrong answers.
Chunking, hybrid search, reranking and metadata, tested against the baseline.
A list of documents to update, merge or remove.
An evaluation run on every change, before release.
Prompt injection, permissions and data leaks tested and documented.
We get read access to the pipeline, the index and the logs, and agree which question types and user roles matter most.
We draft questions, expected answers and supporting passages. Your subject experts review them, because only they know what a correct answer is.
We run the current system against the test set and score retrieval and answers separately. You see the numbers before any change is made.
Failures are grouped by cause and ranked by how many questions each cause affects. We agree on the order of fixes with you.
One change at a time, each rerun against the whole set, so every gain and every regression is visible.
The evaluation moves into your CI, with a threshold you choose, and your team gets a walkthrough of how to extend the test set.
2-week fixed-price start
Fixed price.
For teams that need a baseline and a diagnosis before deciding what to fix.
First test set from your questions
Baseline scores for retrieval and answers
Failures grouped by cause
Initial injection and permission checks
Ranked fix plan with an estimate
Engineers working through the fix plan
Monthly, team-based.
For systems that need sustained work: retrieval changes, content cleanup and checks in CI.
Python and backend engineers
QA for test sets and answer grading
Fixes measured against the baseline
Evaluation wired into your CI
Regular demos with before and after scores
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 subject changes from project to project, the way we work does not.
“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.”
We treat a RAG system like any other software that has to be correct: define what correct means, test for it, fix what fails and keep the tests.
On an AI payroll platform we built, automated extraction and validation of salary slips led to 17% fewer salary calculation errors.
For two AI editor apps with 4M+ downloads combined, we rebuilt the AI features to production quality.
Python and backend engineers work next to QA, so the people who measure can also fix.
70+ products launched for startups, SMBs and product companies. 85% of clients come back.
Projects can start in about two weeks.
We change nothing until there is a baseline to compare against.
We do not quote an accuracy target before measuring. Some limits come from the content, and we say so.
We build the test set, your people approve the expected answers.
Test set, evaluation code and reports live in your repository.
Our team in Europe overlaps with part of the US working day.
No sales pitch
Tell us what your RAG system gets wrong
Describe what it is built on, what content it reads and a few questions it answers badly. We'll come back with how we would measure it and a quote for the baseline.
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