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.
Our RAG pipeline development services go beyond basic vector search and document embedding. We engineer hybrid retrieval systems with intelligent chunking, citation verification, multi-tenant isolation, and relevance thresholds ensuring your AI answers accurately from your data and declines honestly when the answer isn’t there. Production-tested across healthcare, fintech, education, and regulatory compliance.
Share your data and use case. Our team will assess the right retrieval strategy, vector database, and accuracy targets for your product.
Trusted in production
RAG Pipeline Development Services
TechEniac provides end-to-end RAG pipeline development from document ingestion and intelligent chunking through embedding, hybrid retrieval, grounded generation with citations, and continuous accuracy improvement.
RAG is fundamentally different from generic AI. Generic LLMs answer from training data the internet. RAG systems answer from your data your documents, your records, your knowledge base. That distinction changes everything about accuracy, trust, and compliance.
The difference between a demo-quality RAG system and a production-grade one is retrieval quality. Anyone can embed documents and run a vector search. Making the RIGHT documents surface for the RIGHT queries with hybrid retrieval, intelligent chunking, citation verification, and multi-tenant isolation is the engineering that separates systems users trust from systems that hallucinate.
Our RAG Development Services
Six capability areas spanning document ingestion, retrieval architecture, grounded generation, and multi-tenant isolation each engineered for production accuracy across regulated and high-stakes domains.
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.
MortgageLens AI ingests 500-page mortgage guideline PDFs, scanned images, and video training modules through a fully automated pipeline that processes a new document in under 4 minutes.
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.
EduAssist AI chunks at slide-level granularity each lecture slide becomes its own chunk with the slide number as metadata, enabling citations to specific slide numbers. MortgageLens AI chunks at section-level boundaries, maintaining regulatory context within each chunk.
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.
MortgageLens AI switched from pure vector search to hybrid retrieval and saw a 15% improvement in retrieval accuracy for regulatory terminology queries. Hybrid retrieval is now our default architecture for every production RAG system.
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.
EduAssist AI achieves 100% citation rate every response traces to a specific course document and slide number. MortgageLens AI cites specific guideline sections with page numbers. ComplianceGuard AI cites regulatory documents with publication dates.
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.
EduAssist AI uses per-course isolated Qdrant collections across 7 universities the chemistry textbook never contaminates history course responses. ComplianceGuard AI uses jurisdiction-partitioned collections federal and state regulations stored separately with client-specific filtering at the agent level.
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.
SolidHealth AI's RAG system improved from 88% to 92% retrieval accuracy in the first 3 months through chunking refinements and embedding model upgrades informed by production query analysis.
Industries We Serve
Our RAG pipeline development services are tailored to the specific data types, accuracy requirements, and compliance frameworks of each industry.
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.
Our Development Approach
The difference between a demo-quality RAG system and one that serves real users in production is engineering discipline at every layer ingestion, chunking, retrieval, generation, and verification. Here is how we build RAG systems that achieve 90%+ accuracy and maintain it as your data grows.
A fully automated ingestion pipeline that processes your documents PDFs, scanned images, Word files, PowerPoint, HTML, video transcripts with quality scoring, deduplication, and metadata enrichment per extracted passage.
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.
Semantically chunked documents stored as vector embeddings in your chosen vector database with chunk strategies customised to your content type and retrieval requirements.
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. EduAssist AI chunks at slide-level granularity. MortgageLens AI chunks at section-level boundaries. The strategy is always dictated by how users will query the data.
A hybrid retrieval system combining dense vector search with BM25 keyword search, fused via Reciprocal Rank Fusion with configurable relevance thresholds that prevent low-quality retrievals from reaching the generation layer.
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. EduAssist AI achieves its 99.3% out-of-material decline rate through this threshold mechanism.
AI responses generated exclusively from your retrieved context with enforced grounding rules, source citations for every claim, and post-generation verification that checks every citation against chunk metadata before delivery.
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. MortgageLens AI cites guideline sections with page numbers. If a citation can't be verified, the user never sees it.
Production monitoring infrastructure that tracks retrieval quality, identifies content gaps, and drives accuracy improvement with every query logged for analysis and every user feedback signal informing refinements.
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.
We’ll assess your data, retrieval requirements, and accuracy targets and recommend the right architecture in one call.
Technology
Per-tenant isolated collections, hybrid search support (dense + sparse vectors), strong metadata filtering, self-hosted or cloud deployment. Our default for multi-tenant RAG systems.
Fully managed vector database with serverless scaling. Lower operational overhead for teams without dedicated DevOps.
GCP-native RAG engine with Gemini embedding models. Used in SolidHealth AI for healthcare-grade deployment.
Highest accuracy general-purpose embeddings. Our default for most production RAG systems.
GCP-integrated embeddings for projects deployed on Google Cloud. Used in SolidHealth AI with Vertex AI RAG.
Model selection based on accuracy benchmarks against your specific domain data not generic leaderboard scores.
Dense vector retrieval + BM25 keyword retrieval combined via Reciprocal Rank Fusion. Our default for every production RAG system.
Configurable minimum similarity scores (default 0.72) below which the system declines to answer rather than generating from low-quality context.
Tenant ID, document type, date range, and category filters applied before vector search ensuring retrieval scope matches the query context.
Grounded generation with mandatory citation enforcement. Strongest instruction-following for strict grounding rules. Used in EduAssist AI.
Complex reasoning over retrieved context. Multi-step analysis across multiple retrieved chunks. Used in ComplianceGuard AI.
Cost-efficient grounded generation for high-volume query environments. Used in MortgageLens AI and SolidHealth AI.
Post-generation check ensuring every cited document and page number exists in chunk metadata. Unverifiable citations stripped before delivery.
Text extraction from native PDF documents.
Scanned document processing with deskewing and contrast enhancement.
Office document extraction with speaker notes and metadata preservation.
Audio and video transcription with timestamp preservation for multimedia knowledge bases.
Enterprise-grade document processing for high-volume ingestion pipelines.
Retrieval strategy, data requirements, vector databases, accuracy control the questions every founder asks before shipping a RAG-powered product.
Every RAG use case is different. The data types, accuracy requirements, isolation needs, and compliance constraints all of it shapes the architecture. That is why we start with a conversation about your data, not a quote.