The Integrated Engineering Advantage
DomainRAG — AI Knowledge Chat for Any Domain
Upload your data. Ask questions in plain English. Get sourced, cited answers.
DomainRAG turns any CSV, Excel, or document collection into an AI-powered knowledge engine. Tabular chunking preserves column context so embeddings actually mean something. Every answer cites its source. No hallucinations, no prompt-stuffing guesswork.
The Problem
Your domain expertise lives in spreadsheets, PDFs, databases, and the heads of senior employees who might leave tomorrow. Generic AI chatbots hallucinate because they don't know YOUR data. Enterprise search returns documents, not answers. Building a custom AI solution takes 12-18 months and costs $500K+. You need domain-specific AI that understands your data and cites its sources.
The Solution
DomainRAG transforms your existing data into an AI-powered knowledge base in weeks, not months. Upload CSV, Excel, TSV, or PDF files through a simple web interface. Our tabular chunking engine preserves column context so embeddings stay meaningful. Azure AI Search indexes everything as vectors. Ask questions in natural language and get sourced, cited answers with specific data points — never hallucinations. Works for any domain.
How RAG Actually Works
Embeddings, cosine similarity, and grounded AI. A 10-minute technical walkthrough of the architecture behind every DomainRAG case study — including the C# code that ranks results across 400,000+ records in milliseconds.
DomainRAG Walkthrough
See it in action — narrated walkthrough of the live Flavor Science demo. Real questions, real retrievals, sourced citations on every answer.
DomainRAG in a Nutshell
Walk through the architecture, the proof points, and the engagement model — one slide at a time. Use the arrows to navigate.
What Makes DomainRAG Different
Six architectural decisions that separate DomainRAG from generic AI chatbots and prompt-engineering consultancies.
Upload Any Data
CSV, Excel, TSV, and PDF files uploaded through a simple web interface. No ETL pipelines, no data engineering, no coding required.
Tabular Chunking Engine
Row-groups of 25 with column headers preserved in every chunk. The architectural insight that makes structured-data RAG actually work.
Domain-Specific AI
GPT-4o answers grounded in YOUR indexed data with sourced citations. System prompts tuned per domain — flavor science, maintenance, compliance, engineering.
Sourced & Cited
Every answer includes specific data points, CAS numbers, row references, file sources. Verifiable, reproducible, never hallucinated.
Ephemeral Indexing
Create an index, run your analysis, securely wipe when done. Pay only for compute time you actually use. Your data never lives permanently in a third-party cloud.
Weeks, Not Months
POC in 2 weeks. Production deployment in 4-6 weeks. Typical enterprise AI timelines are 12-18 months.
The Engagement Model
Three phases. Start with a $10K POC. Prove it works. Deploy to production. Scale through managed service when you're ready.
Proof of Concept
Prove it works with YOUR data — low risk, high conviction.You provide one dataset (CSV, Excel, or database export). We index it, tune the system prompt for your domain, and demo the chat. 30-day access to validate with your team. Full report on coverage, query quality, and deployment roadmap.
- You provide one dataset (CSV, Excel, or DB export)
- We index, tune the system prompt, and demo
- 30-day access to validate with your team
- Written report on coverage and query quality
- Benchmark-based guarantee: POC cost applies to deployment if you proceed
Production Deployment
The full DomainRAG platform, deployed on the RAIISS managed Azure.Complete AI knowledge platform deployed as a managed service: web-based upload, automatic tabular chunking, Azure OpenAI embeddings, Azure AI Search vector index, natural language query with GPT-4o synthesis, sourced citations, ephemeral indexing, Microsoft Identity auth, and domain-specific prompt tuning. Your data is always exportable; platform hosting and maintenance is our responsibility. Enterprise tier available on request for customer-hosted deployment.
- Web-based upload for CSV, Excel, TSV, PDF files
- Automatic tabular chunking with column header preservation
- Azure OpenAI embeddings (text-embedding-3-small, 1536 dim)
- Azure AI Search vector index (HNSW cosine similarity)
- Natural language query with GPT-4o synthesis
- Sourced, cited answers with specific data points
- Source filtering (query against specific datasets)
- Index management: upload, delete, nuclear wipe
- Domain-specific system prompt tuning
- Microsoft Identity authentication
Managed Service
Ongoing SaaS — we run it, you use it.Managed AI knowledge platform with per-user pricing that scales with your team. Unlimited uploads, multiple concurrent indexes, automated scheduled re-indexing, custom system prompts per index, usage analytics, SSO, and SLA-backed uptime.
- Everything in Production Deployment
- Unlimited data uploads and re-indexing
- Multiple simultaneous indexes (per department, project, domain)
- Automated scheduled re-indexing for live data sources
- Custom system prompts per index
- Usage analytics, query logging, adoption dashboards
- SSO integration (Azure AD, Okta, custom)
- Data residency options (US, EU, custom)
- SLA: 99.9% uptime, <5s query response
- Example: 200 users × $30 = $6K/month ($72K/year)
Proven in Production
Two live case studies, same DomainRAG architecture, different domains. Click either to try the demo.
Flavor Science RAG
Food & flavor industry case study · Food Science / Flavor Chemistry
400,000+ records indexed from 5 authoritative public databases (FooDB, USDA, FlavorDB2, FlavorNet, Dr. Duke's). Denormalized join files resolved relational data into flat, human-readable CSV chunks. Domain-specific system prompt tuned for flavor science expertise.
- 400K+ records indexed from 5 authoritative databases
- 8-11x more detailed answers than Google Gemini
- Answers include CAS numbers, compound names, formulation strategies
- Every answer cited to a specific source row with similarity score
Code RAG
Software engineering case study · Software Engineering
The same DomainRAG architecture applied to source code. Semantic chunking splits code by classes, functions, and logical boundaries. Natural language queries return synthesized answers with source file citations. 47 file types supported including C#, TypeScript, Angular, WPF, TwinCAT PLC, and Python.
- 47 file types supported (C#, TS, HTML, PLC, Python, etc.)
- Semantic chunking by language (class/method, export, section)
- Natural language queries return sourced code snippets
- Directly ported to create the Flavor Science RAG
Technical Specifications
DomainRAG vs. Generic AI Builds
Same outcome (queryable knowledge base). Radically different architecture, timeline, and economics.
| Dimension | Custom AI Build / Prompt Stuffing | DomainRAG |
|---|---|---|
| Approach | Prompt stuffing (fails on structured data) | Retrieval-first with tabular chunking |
| Answer quality | Hallucinated, no citations | Sourced citations on every answer |
| Time to deploy | 12-18 months | 2 weeks POC, 4-6 weeks production |
| Up-front cost | $200K – $500K+ custom build | $10K POC, $50-75K deployment |
| Data residency | Vendor cloud, permanent retention | Your Azure, ephemeral indexes |
| Domain tuning | Generic chatbot behavior | Domain-specific system prompts per index |
Need Custom Development Beyond DomainRAG?
If your project needs custom engineering — APIs, web apps, WPF, PLC integrations, or bespoke dashboards — explore RapidSolutions. AI-accelerated custom development built on proven templates, delivered in weeks.
Ready to turn your data into intelligence?
30 minutes on a call. Bring a dataset in mind, a domain question, or just a general "could this work for us?" We'll walk you through the architecture and quote a $10K POC with a benchmark-based guarantee — before we discuss production deployment.