Nutrition AI · WhatsApp Product
Atleet Buddy WhatsApp Nutrition Assistant
A book-grounded WhatsApp assistant with safer answers, subscriptions, and admin visibility.
- LLMs
- React
- Supabase
- Dashboard UI
- Usage Analytics
- Role-Based Access
All services
Knowledge & Retrieval
RAG & Knowledge Systems for real operations.
We build knowledge systems that ingest, structure, and retrieve the right information at the right time. Results are grounded, traceable, and safe for business-critical use.
The problem
What this solves
Companies accumulate enormous amounts of knowledge: SOPs, product docs, support tickets, PDFs, policy documents, and internal wikis. Most of it is impossible to find quickly, never updated consistently, and not accessible to the people who need it most.
The system
What we build
A retrieval-augmented generation (RAG) pipeline that ingests your documents, cleans and structures the content, builds a vector search layer, and serves accurate, source-cited answers. The system is built to be maintained: you can update content, add new sources, and monitor answer quality over time.
Applications
Common use cases
Internal knowledge bases for operations teams
Customer-facing support assistants
Policy and compliance Q&A systems
Employee onboarding and HR helpdesks
Product documentation assistants
Regulated industry knowledge management
In production
Built with this service
Manufacturer Sales · AI SaaS
FlowRMS Manufacturer Rep Operating System
A vertical SaaS operating layer for CRM, quotes, orders, commissions, and documents.
- React
- Node.js
- Dashboard UI
- CRM
- ERP Workflows
- AI Document Processing
- Role-Based Access
What you get
- Document ingestion and data cleanup
- Retrieval pipelines with citations
- Access control by team and role
- Continuous content refresh and QA
Outcomes
- Faster answers for teams and customers
- Reduced dependency on subject-matter experts
- Consistent, cite-backed responses
- Higher adoption of internal knowledge
What we need from you
- Access to the documents, SOPs, or knowledge sources
- A sense of who will query the system and how
- Any existing categorization or structure you have
- Examples of common questions the system should answer
- Permission rules if access needs to be role-based
- A point of contact who owns the content
Getting started
How the engagement usually starts
Fixed-scope after audit
RAG Knowledge System Sprint
We audit the content, clean and structure the documents, build the retrieval pipeline, connect a conversational interface, and create an admin workflow so your team can keep the knowledge current.
Let's explore this
Want to explore RAG & Knowledge Systems for your business?
Start with a free AI Systems Audit. We will review your workflow, identify what is worth building, and recommend the right starting point.