AI Knowledge Systems & RAG
Retrieval-Augmented Generation (RAG) done right is an engineering challenge of document chunking, embedding strategy, and precision retrieval. We build grounded knowledge systems that answer exclusively from your verified data, eliminating hallucinations.
What We Deliver
- Document ingestion and chunking pipelines for PDFs, Notion, Confluence, and Drive
- Hybrid search combining dense vector embeddings with sparse BM25 keyword matching
- Inline source citations and confidence-scored answer generation
- Evaluation datasets and benchmarks to scientifically measure accuracy over time
Verifiable Source Citations
Every response provides direct links and page references to the underlying source document. If corroborating evidence does not exist in the indexed files, the system politely declines to answer.
How we build and deploy.
Structured engagement from initial process audit to live production monitoring.
Document Corpus Audit & Structuring
We inspect your files—file types, table density, domain jargon, and access permissions—to choose the optimal chunking strategy.
Embedding Benchmark & Indexing
We test multiple embedding models against your actual terminology and index vectors into pgvector or Qdrant.
Hybrid Search & Cross-Encoder Reranking
We build a multi-stage retrieval chain combining semantic vector search, BM25 exact match, and cross-encoder reranking.
Citation Layer & Confidence Filtering
We configure the synthesis model to include inline citations and enforce strict confidence cutoffs.
Evaluation Benchmark & Production Deploy
We create a golden test dataset of domain questions, measure retrieval precision, and deploy with monitoring.
Operational challenges we eliminate.
Your existing chatbot confidently makes up incorrect answers
We enforce strict retrieval grounding: answers are generated solely from retrieved chunks with source citations.
Standard vector search returns irrelevant noise for specific technical terms
We deploy hybrid search and cross-encoder reranking, ensuring specific names and codes match accurately.
Document updates never make it into chatbot answers
Our incremental ingestion pipelines listen to file changes and re-index updated documents within minutes.
Under the hood.
Deep architectural rigor built for software engineers and technical decision-makers.
Hybrid Dense + Sparse Search
Combines vector embeddings for conceptual understanding with BM25 keyword matching for exact part numbers and acronyms.
Hierarchical Parent-Child Chunking
Retrieves granular small chunks for precision while feeding broader parent context to the LLM for coherent synthesis.
Cross-Encoder Reranking
Reranks the top 25 retrieved passages using cross-encoders to ensure top-5 relevance before generating answers.
Incremental Vector Synchronization
Automated webhooks detect document edits, recalculating embeddings for modified files without costly full re-indexing.
Where this applies.
Internal Employee Knowledge Assistant
Helps staff search thousands of company SOPs, HR policies, and technical wikis inside Slack or web.
Customer-Facing Technical Docs Search
Assists software developers and clients with instant, code-accurate answers from API documentation.
Compliance & Policy Auditing
Cross-references internal operating procedures against regulatory requirements with exact paragraph citations.
Contract & Agreement Research
Enables legal and finance teams to query complex supplier agreements and service contracts.
Related business use cases.
Customer Service Automation
Resolve Tier-1 support inquiries instantly with grounded AI agents and smooth human escalation.
Internal Knowledge Assistants
Give your team instant, cited answers from internal SOPs, technical documentation, and project wikis.
Verified delivery standards.
Underwriting Acceleration
Finject MCA brokerage CRM with AI statement parsing
Brand-Compliant Social Reach
PostAutoPilot distributed social automation platform
Client Value Delivered
Over 200+ projects shipped across SaaS, AI, and workflow automation
Intellectual Property Guarantee
Clients own 100% of all custom code, prompt pipelines, and databases upon launch
Common questions about ai knowledge systems & rag.
Q.What document formats can the system ingest?
PDFs, Word documents, Google Docs, Notion, Confluence, Markdown, HTML, CSVs, and scanned documents via OCR preprocessing.
Q.How large can our document library be?
Our architectures easily scale to hundreds of thousands of documents using sharded indexes, batched embeddings, and relational PostgreSQL metadata.
Q.Can employees see documents they do not have clearance for?
No. We integrate role-based access control (RBAC) so the retrieval engine filters chunks according to the requesting user's identity.
Real software we have shipped.
Have a process that should work better?
Bring us the bottleneck, the brittle build, or the idea. We'll give you a direct read on what to do next.