Each one ends with a written recommendation and the reasoning behind it, so your team can challenge it.
If two or more of these describe your situation, a short assessment usually costs less than a wrong first sprint.
One engineer wants RAG, another wants fine-tuning, a third says a long context window makes both unnecessary.
The proposals differ several times over in price and you cannot tell which scope is realistic.
You need to know whether a managed product covers your case or only the easy half of it.
It is right often enough to be promising and wrong often enough that you cannot ship it.
You have to commit to numbers and nobody in-house has taken a system like this to production.
Legal or security wants to know where documents go, which providers see them and what the model can reveal.
We compare RAG, fine-tuning, long-context prompting and conventional search for your use case and say which one, or which combination, fits.
We sample your sources and report what is usable as is, what needs cleaning and what should stay out of the index.
Ingestion, chunking, embeddings, keyword and vector retrieval, reranking and generation, designed for your data and drawn so your engineers can build it.
A second opinion on a pipeline your team or a vendor has proposed or built, with specific findings ranked by impact.
We compare managed RAG products, cloud services and open-source components against your requirements, lock-in tolerance and budget.
How to build a test set from real questions, which retrieval and answer metrics to track and what score means ready to launch.
Document-level permissions at retrieval time, prompt injection exposure and rules for which providers may process which data.
Optional: a small working pipeline on a sample of your documents, so the recommendation rests on measured results and not on opinion.
The outcome is something you can hand to your board, your engineers or another vendor.
The approach we advise and the ones we ruled out
Evidence from your documents and questions
Open risks and how to test them
Plain language for non-engineers
Component diagram and data flow
Shortlisted models, stores and services
Where permissions are enforced
What to log and monitor from day one
Phases with scope and effort for each
Running-cost model by query volume
Launch criteria tied to the evaluation set
A build quote if you want us to do it
Our own engineering stack is below. Vector databases, orchestration frameworks such as LangChain or LlamaIndex, and model providers are chosen with you for each project: we compare the options against your data, hosting rules and budget and do not push a fixed stack.
Python
FastAPI
PostgreSQL
React
AWS
Docker
Kubernetes
TypeScriptYou need an outside view on an architecture before your team spends a quarter building it.
You want an AI answer feature in the product and need to know what is realistic, by when and at what cost.
Your team answers the same questions from the same documents every day and you are weighing an internal assistant.
Your engineers are strong on the product and want experienced guidance while they build their first retrieval pipeline.
Which one solves your problem, or whether you need both.
Whether your corpus is small enough to skip retrieval.
A custom pipeline or a managed product.
A shortlist that fits your hosting and data rules.
An independent review before or after you build.
Effort to build and spend to run, by phase.
We collect real questions your users ask, the answers you expect and what a wrong answer would cost you.
We sample your sources: formats, volume, quality, how often they change and who is allowed to see what.
We compare approaches and, where it settles a doubt, run a small prototype on your documents and score it against the questions.
You get the written report, target architecture, cost model and roadmap, and we walk your team through each of them.
You build it in-house, buy a product, hire us to build it or stop. All four are valid results of the work.
2-week fixed-price start
Fixed price.
For teams that need a decision on approach, architecture and budget before they commit to a build.
Use case and data review
RAG vs alternatives, with reasons
Target architecture and tool shortlist
Quick prototype on a data sample
Roadmap, risks and build estimate
Engineers working alongside your team
Monthly, team-based.
For companies that want the advice carried into the build, with our engineers working next to yours.
Architecture decisions made with your team
Python and backend engineers
Evaluation set and regular quality reports
Design and code reviews
Team size adjusted as the work 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.”
Our advice comes from engineers who build and ship AI features in real products, so it is written to be built, not just presented.
We build web, mobile and backend products with AI and ML features, and the same people do the consulting.
On an AI payroll platform, our PDF data extraction and automated validation led to 17% fewer salary calculation errors.
For QLode, an AI fashion commerce platform for the USA and Europe, we built image and text search and an AI recommendation engine.
70+ products launched for startups, SMBs and product companies.
85% of our clients come back with new work.
If plain search or a simpler setup does the job, the report says so.
Recommendations are tested on your documents and questions, not on a generic benchmark.
We compare tools on your requirements and explain every pick.
The report, diagrams and prototype code are written so another team can take them and build.
Our team in Europe shares part of the US working day for calls and reviews.
No sales pitch
Tell us what you are deciding
Describe the use case, your documents and the options on the table. We'll suggest how to settle the question and send a quote. If you don't need RAG, we'll say so.
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