RAG / Knowledge Systems
High-precision retrieval augmented generation with source-grounded accuracy.
We build enterprise RAG systems featuring hybrid semantic search, chunk-aware reranking, recursive document summarization, and strict provenance citation to eliminate hallucinations completely.
Why naive vector search fails on complex enterprise corpora
Basic vector databases retrieve top-k chunks based on semantic similarity, frequently pulling irrelevant fragments, missing numerical tabular data, and truncating crucial surrounding context. True enterprise search requires hybrid indexing.
Naive embeddings missing exact keyword matches (serial numbers, codes, part names)
Chunk fragmentation breaking critical table relationships and multi-page tables
Stale vector indexes lagging behind real-time database updates and permission changes
Hallucinated citations where the model answers from pre-trained weights rather than retrieved docs
Retrieval Architecture Capabilities
Hybrid Dense + Sparse Search
Fusing BM25 keyword matching with dense neural embeddings using Reciprocal Rank Fusion (RRF) for 99%+ retrieval recall.
Cross-Encoder Reranking
Applying secondary cross-encoder neural rerankers (Cohere, BGE) to score semantic relevance against the user's specific intent.
Document Hierarchy & Context Windows
Parent-child chunking and small-to-big context expansion retrieving small sentences for matching while feeding full sections to the LLM.
Strict Citation & Grounding Guardrails
Mandatory citation metadata binding every generated sentence to verified document IDs, page numbers, and exact quotes.
Proven runtimes and industry-standard protocols.
We select tools based on operational reliability, long-term maintainability, and horizontal scaling capabilities.
Vector Databases
Embedding Models
Parsing & Ingestion
Rerankers & Eval
Why companies choose CodeSelf.
We operate as your dedicated technical partner with production codebases and zero markups.
What you receive at handoff.
Every engagement concludes with documented, tested, and deployable production codebases with zero ongoing licensing fees.
How CodeSelf builds and delivers rag / knowledge systems.
Sprint-based delivery with weekly code releases directly into your private staging environment.
Corpus Audit & Schema Strategy
Analyze raw file formats (PDF, DOCX, Markdown, SQL tables) and establish chunking taxonomies.
Ingestion Pipeline & Vector Indexing
Build automated ingestion workers that sanitize text, compute embeddings, and write to hybrid indexes.
Query Transformation & Reranking
Implement query rewriting (HyDE, multi-query expansion) and cross-encoder reranking layers.
Gold Standard Accuracy Evaluation
Measure retrieval recall@k, context precision, and answer faithfulness against annotated Q&A sets.
Key technical and commercial questions.
Who owns the source code and intellectual property for rag / knowledge systems systems?+
Can this technology stack be deployed inside our private cloud infrastructure (AWS/GCP/Azure)?+
How do you ensure zero downtime when updating live production services?+
What happens after the project launch? Do you provide ongoing maintenance and handover?+
Related technologies in our stack.
Generative AI
We build domain-tuned foundation model pipelines, fine-tuned embeddings, and multi-modal generative interfaces that solve concrete business challenges with strict privacy and deterministic guardrails.
AI Agents
We build goal-driven AI agents that parse complex workflows, interact with enterprise APIs, verify intermediate state, and execute mission-critical tasks with robust human-in-the-loop oversight.
Databases
We design, tune, and maintain relational, NoSQL, vector, and time-series database architectures, ensuring high availability, automated backups, and sub-millisecond query latency.
Ready to build or scale your rag / knowledge systems stack?
Schedule an architectural consultation with our senior systems team to evaluate infrastructure, tooling, and delivery roadmaps.