Production-Ready RAG Data
in Weeks, Not Months
Proprietary taxonomy tools + ML-Ops expertise = reliable retrieval at 50% lower cost
Why Your RAG System Isn't Production-Ready
Your RAG system hallucinates because your data is a mess
- $200k+ invested in RAG infrastructure (vector DBs, LLM APIs, fancy UIs)
- Informal taxonomies and inconsistent classifications
- Poor metadata = unreliable retrieval
- Garbage in = garbage out
Real cost: Failed pilots, lost stakeholder trust, wasted engineering time
Data preparation is a bottleneck killing your AI timeline
- RAG projects stall for 6-12 months on data prep work
- Hire expensive data engineers ($180k+ each)
- Use offshore commodity shops (poor quality)
- Do it yourself (takes forever)
Real cost: Opportunity cost, expensive consultants, or delayed go-live
Purpose-Built for RAG Data Quality
Production-ready RAG data in weeks, not months
- Proprietary taxonomy standardization tools (not manual work)
- ML-Ops trained teams who understand embeddings and chunking strategies
- Fixed-price, fixed-timeline delivery
- Quality metrics and before/after retrieval accuracy
Enterprise quality at 50-60% lower cost
- Smart team structure: offshore ML-Ops expertise + senior oversight
- Efficient tooling reduces person-hours required
- No vendor lock-in: deliver knowledge + code, not just hosted service
- Transparent pricing: know total cost upfront
Proven Across Industries
Legal Document Intelligence System
Challenge: Law firm needed to retrieve and apply thousands of national laws, regional regulations, and case precedents accurately when drafting legal documents
Impact: Built RAG system with vector database integrating legal corpus, automated document analysis, and multi-agent workflow for complex document drafting
Hardware Product Discovery Bot
Challenge: Electronics distributor's product catalog (embedded systems, dev kits) was unstructured, making customer queries slow and requiring manual sales intervention
Impact: Implemented BoardBot with hybrid LLM + semantic search routing, vector database, and iterative feature extraction to handle vague product queries
Enterprise Chatbot Integration
Challenge: IT services company needed sophisticated chatbot with real-time communication, engaging persona, and seamless integration with existing systems
Impact: Built Dataman chatbot with React frontend, Django backend, vector database knowledge storage, and WebSocket real-time communication
Simple, Transparent Process
Assessment
2 weeks
- Audit your document corpus
- Identify taxonomy gaps
- Deliver implementation roadmap
Implementation
6-12 weeks
- Standardize taxonomies
- Prepare and validate corpus
- Test retrieval accuracy
Support
Ongoing
- Maintain data quality
- Monitor RAG performance
- Adapt as system evolves
Start with a Fixed-Price Assessment
- Complete document corpus audit
- Taxonomy gap analysis
- Data quality scorecard
- Detailed implementation roadmap
- Cost and timeline estimates
Money-back guarantee if you're not satisfied with deliverables