Search internal knowledge
Let teams find relevant policies, procedures, and product documents through a natural-language interface.
Retrieval-augmented generation
RAG development for AI grounded in your data.
As a RAG development company, Techtribe builds retrieval-augmented generation systems that connect language models to information your product or team relies on. We scope the data, users, and expected answers first, then design retrieval and evaluation around the real workflow.
One Islamabad-based team · direct founder access · code in your repository
Where it fits
What this service covers.
Ground product answers in customer data
Retrieve information from an agreed set of product or customer sources so responses can use relevant context instead of general model knowledge alone.
Ground agents in operating procedures
Retrieve relevant policies and procedures for an agent working through an order, invoice, or dispute workflow, as demonstrated in Techtribe’s FlowGen Labs engagement.
Engineering approach
Designed around the product and its constraints.
We choose the implementation after understanding the user flow, existing systems, and operating requirements. The exact stack and boundaries are agreed during scoping.
Data ingestion and indexing
Plan source formats, ingestion and update paths, document processing, chunking, metadata, and indexes around the data you can provide.
Retrieval and access controls
Shape search and context selection, and align retrieval permissions with the users and data boundaries in the product.
Evaluation and operations
Use representative questions to review retrieval quality, track failures, and decide when the system should abstain or ask for clarification.
Technology
A stack selected for the system.
RAG needs an application layer, retrieval components, and a maintainable data path. Our published FlowGen Labs work shows this combination in an enterprise AI product; we choose specific tools to fit each system.
Python, FastAPI
Service layer for retrieval, orchestration, and application APIs.
TypeScript, Next.js
Web interfaces for teams and product users working with retrieved information.
Vector search
Retrieve relevant passages from indexed documents and enterprise procedures.
AWS, Terraform
Cloud infrastructure and repeatable deployment for the agreed environment.
Choose the right scope
Start with the smallest scope that fits.
Package fit depends on the workflow, integrations, and production requirements. We confirm the scope, price, and ship date before work starts.
2 weeks
Basic MVP
$2,500fixed
Founders who need something real to show users or investors, fast.
A narrow retrieval workflow over an agreed data source to validate the user experience and answer quality.
- One core user flow, working end to end
- One AI feature: agent, chat over your data, extraction or automation
- Clean, responsive UI on our design system
- Deployed on a live URL you can share
- Source code in your repo from day one
- 30 days of free support
Not included: Production hardening (evals, monitoring, fallbacks), custom brand design, more than one integration. Everything built here carries forward into Production MVP.
4–6 weeks
Production MVP
$7,000fixed
Teams launching an AI-first product, or a serious AI module, that real customers will depend on.
A customer-facing or operational launch that needs a designed interface, integrations, evaluation, and monitoring.
- UX and UI design in your brand, before we write code
- Production architecture: deterministic flow control, typed tool contracts, model routing with fallbacks
- Web app and backend deployed on your cloud, in your repo
- Auth, billing hooks and admin basics
- Up to 3 integrations into your existing systems
- Eval set and regression tests
- Cost, latency and quality monitoring
- Weekly live demo
- 30 days of free support
Rolling · 30 days' notice
Dedicated Team
$8,000per month
Teams that shipped and want to keep building, or companies that need a senior AI pod without hiring one.
Continued work across multiple sources, product workflows, or evolving evaluation and operations needs.
- 2 engineers (1 senior, 1 mid), full-time on your product
- 1 dedicated project manager: your single point of contact
- Founder oversight on architecture and quality
- Daily standup, weekly demo, shared Slack
- 4+ hours overlap with US Eastern, full day with Europe
- Code in your repo, infra in your cloud, nothing to migrate
- Swap or add engineers with 2 weeks' notice
- 30 days of free support if you pause or wrap up
Relevant work
See FlowGen Labs in practice.
Techtribe contributed RAG and vector search over enterprise procedures and documents for FlowGen Labs agents that operate inside SAP workflows.
How it works
A clear path from scope to shipped software.
01 · Scope the data and users
Identify the source material, who can access it, the questions users need answered, and how success will be judged.
02 · Design the architecture
Agree on data ingestion, indexing, retrieval, permission boundaries, evaluation, and where the system fits in your product.
03 · Build and evaluate
Implement the agreed retrieval path, test it against representative questions, and share a weekly working demo.
04 · Ship and support
Connect retrieval to the product workflow, deploy to the agreed environment, and support the shipped scope for 30 days.
FAQ
RAG development questions.
What does RAG do?
RAG retrieves relevant information from selected data sources and supplies it as context to a language model. It can help an application answer questions using information that is specific to a product or organization.
What data sources can a RAG system use?
The sources depend on the project. During scoping, we review the formats, access methods, update frequency, and permissions for the material you want to make available.
How do you handle data access permissions?
We define who can see which data as part of the system design, then align retrieval with those boundaries. The right implementation depends on the existing identity model and source permissions.
How do you evaluate retrieval quality?
We agree on representative questions and relevant source material, then inspect whether retrieval returns useful context. The evaluation set can be expanded as real usage reveals new cases.
Which package fits a RAG project?
Basic MVP can validate one focused retrieval workflow. Production MVP suits a broader launch with integrations and evaluation. Dedicated Team supports ongoing product work. See the packages page for current pricing and inclusions.
Book a free call
Have a RAG use case in mind?
30-minute free call. Tell us what information people need to retrieve, where it lives, and how the answers will be used. We’ll scope a practical first version.
- You talk to a founder, not a salesperson.
- You leave with a recommended package and a rough plan.
- Prefer email? howdy@techtribedev.com