We make each of these with you, in writing, before the build starts. They decide most of the cost and most of the answer quality.
If a few of these describe your situation, the missing piece is often engineering around the model, not a different model.
It answers well from twenty hand-picked documents and has never been connected to the real systems.
Nobody could explain how the assistant respects document permissions or where the data is sent.
They could build the pipeline, but not this quarter and not alongside the roadmap.
Real questions are shorter, vaguer and full of internal terms the test questions never had.
Hybrid search, reranking, query rewriting, agents: every article recommends something else, and each adds cost.
Users cannot find what exists, and support answers the same questions from the same documents every day.
We list your sources, their formats, APIs, update frequency and access rules, and mark which ones are ready to index and which need cleanup first.
A written design: retrieval method, index layout, model per step, where each component runs and how it connects to your product. You approve it before we build.
Connectors to the systems you already use, with incremental sync so edits and deletions in the source reach the index without a full rebuild.
Each document type is split along its own structure: headings, tables, ticket threads, code blocks. Chunks keep their title, source link and date.
Search, filtering, reranking and prompt assembly as a service with its own API. As a RAG pipeline development company we build it so every stage can be measured and replaced separately.
User and group access is synced from the source systems and applied as a filter at search time, so restricted text is not passed to the model.
A test set of real questions with expected sources and answers, run on every change. You see retrieval quality and answer quality as numbers, not impressions.
The feature goes into your product behind a flag, first for your team, then for a share of users, with logging, cost tracking and a fallback to what you have today.
Everything runs in your accounts and is documented so your own engineers can operate and change it.
Connectors with scheduled or event-based sync
Index and retrieval service in your cloud
API your product calls, with source citations
Deployment scripts and environment setup
Test set built from your real questions
Baseline results recorded before launch
Checks that run on every pipeline change
Dashboard for latency, cost and failed answers
Architecture document with the reasons behind each choice
Runbook for re-indexing and adding a source
Walkthrough sessions with your engineers
List of next improvements, ranked by effect
The pipeline is usually a set of Python services next to your existing backend. The vector store (an extension in the PostgreSQL you already have, or a dedicated vector database), the orchestration library and the model provider are chosen with you for each project, based on your data, hosting rules and budget.
Python
FastAPI
PostgreSQL
AWS
Docker
Kubernetes
TypeScriptYou want answers from customer data inside the product, with strict separation between accounts.
Policies, contracts and procedures are spread across tools, and staff lose time looking for them.
The proof of concept convinced everyone. Now it has to meet real data, permissions and traffic.
Agents answer from the same help articles and past tickets every day and need them in one search.
Get a design that fits your sources and budget.
Build connectors and sync for the systems you use.
Move from a file folder to real data.
Make retrieval respect existing access rules.
Integrate behind a flag with a fallback.
Documentation, tests and a runbook.
We review sources, access rules and the product code where the feature will live, and collect real user questions for the test set.
We write the design and build the simplest version that works on a sample of your data. Its test results become the baseline for every later change.
Connectors, parsing, index, retrieval and permission filters, built source by source. Each addition is checked against the test set.
The retrieval service is connected to your product behind a feature flag, with logging of questions, sources used, latency and cost.
Internal users first, then a share of customers. We fix what real questions reveal before the feature is opened to everyone.
Documentation, runbook and sessions with your engineers. We stay on for further sources and tuning if you want us to.
2-week fixed-price start
Fixed price. Implementation quoted after it.
For teams that need an architecture and a realistic estimate before they commit a budget.
Audit of sources and access rules
Architecture document with options compared
Prototype on a sample of your data
First test set and baseline results
Scope and estimate for the implementation
Backend and AI engineers working in your repositories
Fixed price for an agreed scope, or monthly.
For teams that know what they want built and need people to build and roll it out.
Connectors and ingestion for your sources
Retrieval pipeline with permission filters
Integration into your web or mobile product
Evaluation checks on every change
Staged rollout and handover to your team
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 work differs from project to project, the way we run it 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.”
Adding RAG to a live product is mostly backend, data and integration work, with AI on top. That is the kind of engineering we do.
For an AI payroll platform we built PDF data extraction from salary slips with automated validation. Salary calculation errors fell by 17%.
For QLode we built image and text search and an AI recommendation engine on a multi-retailer integration architecture.
We build web, mobile and backend products, so the feature is integrated into your app, not left as a separate demo.
70+ products launched for startups, SMBs and product companies.
85% of our clients come back with new work.
We add to the systems you run and avoid new infrastructure unless the test results call for it.
We start with the plainest pipeline and add reranking, rewriting or agents only when the test set shows a gain.
Access rules are part of the design, not a patch after security review.
Code, index, prompts and test sets live in your repositories and cloud accounts.
Our team in Europe shares part of the US working day for calls and reviews.
No sales pitch
Tell us where your data lives
List the systems, the product you want the answers in and what has been tried so far. We'll reply with a suggested architecture direction and a starting point. If plain search would do the job, we'll say so.
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