The retrieval core is similar in all four. The screens, permissions and integrations are what differ, and they define the scope.
If two or three of these describe your situation, the retrieval idea is proven and the product work is what remains.
It works for the people who built it and has no sign-in, no roles and no place in your product.
Your security or legal team asked how document access is enforced, and the honest answer was that it is not.
They want to ask questions about their own data in your product, with each tenant's content kept apart.
The wiki, the drive, the ticket system and chat history each hold a part, and employees ask a colleague instead.
Agents dig through manuals and past tickets for every reply, and new hires take a long time to learn where things are.
You have someone who can tune retrieval, but nobody to build the front end, mobile client, permissions and admin tools.
Streaming answers, cited passages you can open in the original document, follow-up questions, filters and a plain search mode for when users want a list.
Single sign-on, roles and per-document access rules applied at retrieval time, so restricted text does not reach the model for a user who cannot see it.
For SaaS products: each customer's content is indexed and queried within its own boundary, with per-tenant settings and usage limits.
Imports from your wiki, file storage, ticket system or database, with scheduled or event-based updates so edits and deletions reach the index.
Screens where your team adds or removes sources, sees indexing status, reviews flagged answers and edits the instructions the assistant follows.
A React or Next.js web app and iOS and Android apps on the same API, or the feature embedded into the product you already have.
Thumbs up and down, a reason field and a queue where an expert corrects weak answers. That feedback becomes the test set for later changes.
Questions asked, unanswered topics, ratings, response time and model spend per user or account, visible to you without asking an engineer.
A list you can use to compare proposals. If a vendor's scope skips one of these, ask who builds it.
Sign-in through your identity provider
Permissions copied from the source systems
Access filter applied during retrieval, not after generation
Audit log of who asked what
Streaming text with a visible progress state
Citations that open the source at the right place
A clear "not found in your documents" reply
Conversation history per user
New and changed files indexed automatically
Deleted files removed from answers
Indexing status and errors shown to admins
Re-index without downtime when the setup changes
Logs of questions, sources and answers
Cost per request and per account
A test set of questions run before each release
Rate limits and spending caps
The application layer runs on a familiar web stack. The vector store, the orchestration library and the model provider are chosen with you for each project: for example pgvector inside PostgreSQL versus a dedicated vector database, a library such as LangChain or LlamaIndex versus plain code, a hosted model versus one you run yourself.
Python
FastAPI
PostgreSQL
TypeScript
React
Next.js
AWS
DockerYou want document Q&A or an assistant as a feature of your product, with tenant separation and usage you can bill for.
Your knowledge sits in several systems and employees lose time finding it. You need one internal tool with your access rules.
Your product idea is an assistant over a specific body of content, and you need a first version real users can sign in to.
Retrieval quality is acceptable and the pilot now needs an interface, permissions, monitoring and a release process.
One place to ask across wiki, drives and tickets.
Draft replies with sources in the agent's workspace.
Customers ask questions about their own data.
Manuals and procedures for people away from a desk.
UI, sign-in and permissions for an existing pipeline.
Access rules enforced in a tool that is already live.
We list who will ask, which systems hold the content, how permissions work there today, and write down sample questions with the answers you expect.
A thin version with a sample of your content, so you can judge answer quality and the screen design before the full build is scoped.
Interface, sign-in, permission filter, connectors and admin tools, delivered in sprints with a demo at the end of each one.
A limited set of users works with it on real tasks. We read the logs and ratings with you and fix what they show.
Release to everyone, with dashboards for usage, quality and cost and a set of test questions that runs before each change.
More content sources, new clients or workflow integrations, added in the order your usage data suggests.
2-week fixed-price start
Fixed price. The build is quoted after it.
For teams that have the content and the idea, and need a scoped plan and a price before committing to a build.
Users, sources and permission model mapped
Prototype on a sample of your documents
Screen designs for the main flows
Architecture and model options with trade-offs
Estimate and release plan for the first version
Backend, front-end, mobile and QA on your application
Monthly, team-based.
For a RAG application that is in development or live and needs steady releases next to your own roadmap.
Python and web engineers, mobile when needed
QA for the app and for answer quality
A demo at the end of every sprint
Usage and cost reviewed with you regularly
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 products 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.”
We are a product development company that builds AI features, not a research lab. For a RAG application that matters, because most of the work is the product around the model.
The same team builds the API, the web app and the iOS and Android clients, so the AI feature is not handed between vendors.
On an AI payroll platform we built PDF data extraction from salary slips with automated validation, and salary calculation errors dropped by 17%.
For QLode, an AI fashion commerce product, we built image and text search and a recommendation engine on a high-performance backend.
70+ products launched for startups, SMBs and product companies.
85% of our clients come back with new work. We are rated 5.0 on Clutch from 5 verified reviews.
We settle who can see what at the start, because it shapes the index and is costly to add later.
Citations are part of the first prototype, not a later feature.
The repository, cloud account, index and model provider keys belong to you from day one.
If plain search or a simple FAQ would solve the problem, we tell you before you pay for a build.
Our team in Poland shares part of the US working day for calls, demos and reviews.
No sales pitch
Tell us about your RAG application
Who will use it, where the content lives today and what access rules apply. We'll reply with a suggested scope, the open questions and a rough estimate.
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