Services · RAG Pipeline Development
TechEniac builds production RAG (Retrieval-Augmented Generation) pipelines that ground every AI response in your proprietary data documents, databases, knowledge bases, and records. Instead of your AI relying on general internet knowledge, RAG ensures every answer comes from your verified sources, with citations traceable to specific documents and pages.
Trusted to build
Capabilities
Automated ingestion pipelines that handle every format your data exists in PDFs, scanned documents (OCR), Word files, PowerPoint presentations, HTML pages, video transcripts, and audio recordings. Each format is processed through its optimal extraction path with quality scoring, deduplication, and metadata enrichment.
Documents split into coherent information units using semantic chunking that preserves natural content boundaries paragraphs, sections, slides rather than arbitrary character-count splits. Chunk strategies are customised per document type and use case, with overlap to ensure no information falls between boundaries.
Production RAG systems that combine dense vector search (semantic similarity) with BM25 keyword search (exact term matching), fused using Reciprocal Rank Fusion. Pure vector search misses specific terms regulatory codes, product names, exact error messages. Hybrid retrieval catches both meaning and precision.
AI responses generated exclusively from retrieved context with system prompts enforcing grounding rules, source citation for every factual claim, and post-generation verification that checks every cited document and page number against chunk metadata. Unverifiable citations are stripped before delivery.
Per-tenant vector isolation ensuring one tenant's data never appears in another tenant's responses. Collection-per-tenant for strong isolation requirements. Metadata-filtered shared collections for cost-efficient multi-tenancy. Jurisdiction-partitioned collections for regulatory data organised by domain rather than client.
Production RAG pipelines that improve through real-world usage query logging, user feedback signals, retrieval quality metrics, and knowledge gap detection. Every query that retrieves low-relevance results identifies content missing from the corpus. Every thumbs-down signals a chunking or retrieval refinement opportunity.
Delivery process
We build format-specific extraction paths: PyMuPDF for text PDFs, Tesseract OCR with OpenCV pre-processing for scanned documents, python-docx and python-pptx for Office formats, web scraping with content extraction for HTML, and Whisper transcription for audio and video. MortgageLens AI ingests 500-page mortgage guideline PDFs, scanned images, and video training modules processing a new document in under 4 minutes. Every extracted passage includes metadata: document name, page number, section heading, and publication date.
We use semantic chunking that preserves natural content boundaries rather than arbitrary character splits. Typical chunks are 200–500 tokens with 10–20% overlap. Embeddings are generated using OpenAI text-embedding-3-large or Gemini text-embedding-004 and stored in Qdrant or Pinecone. For multi-tenant products, we implement per-tenant vector isolation each tenant's data lives in its own collection or namespace.
Pure vector search misses exact terms regulatory codes, product names, error messages. BM25 catches these. Reciprocal Rank Fusion gives highest priority to chunks ranked highly by both methods. A relevance threshold (default 0.72) prevents the AI from generating answers when the best-matching chunks are below acceptable quality. MortgageLens AI saw a 15% accuracy improvement when we added BM25 to vector search.
Retrieved chunks are injected into the LLM's context with system prompts enforcing three rules: answer only from the provided context, cite specific sources for every factual claim, decline to answer if the context doesn't contain relevant information. Post-generation, every cited document and page number is verified against the chunk metadata. Unverifiable citations are stripped. EduAssist AI achieves 100% citation rate.
RAG pipelines improve dramatically in the first 3–6 months. We instrument every system with query logging, user feedback signals (thumbs up/down, corrections, escalations), retrieval quality metrics, and knowledge gap detection queries that consistently retrieve low-relevance results indicate content missing from the corpus. SolidHealth AI improved from 88% to 92% retrieval accuracy in 3 months through chunking refinements and embedding model upgrades informed by production data.
Industries served
Patient health records from 25,000+ providers chunked, summarised, and vectorised. AI reasons across medications, lab results, conditions, and vital history simultaneously. HIPAA-compliant with FHIR integration for automated record ingestion.
Mortgage guideline navigation, regulatory compliance monitoring, and financial document analysis. Multi-format ingestion handles 500-page PDFs, scanned images, and video modules. Citations reference specific guideline sections and page numbers.
Course-specific tutoring from uploaded materials. Per-course isolated vector collections prevent cross-course contamination. Every response cites specific documents and slide numbers. Academic integrity modes decline questions outside course content.
Policy documentation retrieval for claims processing and underwriting. RAG-grounded responses ensure every claim decision references specific policy language. Integration with legacy CMS systems (Guidewire, Duck Creek) for automated data flow.
Product catalogue knowledge bases for AI-powered customer support and shopping assistants. Per-store isolated indexes ensure one merchant's data never appears in another's responses. Multi-format ingestion handles product catalogues, FAQ pages, PDF manuals, and policy documents.
Tech Stack
| Vector databases | Qdrant (per-tenant isolation, hybrid search), Pinecone (fully managed), Google Vertex AI RAG (GCP-native) |
| Embedding models | OpenAI text-embedding-3-large, Gemini text-embedding-004, Domain-specific selection |
| Retrieval architecture | Hybrid search (dense + BM25 via RRF), Relevance thresholds, Metadata filtering |
| Generation & citation | Claude Sonnet (grounded generation, mandatory citation), GPT-4o (complex reasoning), Gemini 1.5 Pro (cost-efficient), Citation verification |
| Document processing | PyMuPDF, Tesseract OCR + OpenCV, python-docx / python-pptx, Whisper, Google Cloud Document AI |
Why TechEniac
Vector search + keyword search combined catching both semantic meaning and exact terms that pure vector search misses.
Every cited source is checked against chunk metadata. Unverifiable citations are stripped before delivery.
When the answer isn't in your data, the AI says so rather than fabricating a plausible-sounding response.
Book a free 30-minute strategy session. We’ll review your product idea, discuss architecture options, and map a realistic path from idea to launch.