RAG that retrieves the right thing, not just a thing.
Production-grade retrieval systems built on vector databases, knowledge graphs, and hybrid search — not just chained LLM prompts.
Start where you are.
Build toward the frontier.
We meet you at your current maturity level and build a clear path forward — from foundational implementation to research-grade capability.
Naive RAG
- Document ingestion & chunking pipelines
- Embedding model selection & optimization
- Vector DB setup (Qdrant, Weaviate, Chroma)
- Basic similarity search interface
- Retrieval quality baseline measurement
Advanced RAG
- Hybrid search (dense + sparse, BM25)
- Reranking with cross-encoder models
- Query expansion, HyDE & rewriting
- Multi-hop & parent-child retrieval
- Precision / recall evaluation framework
Agentic RAG
- Graph-enhanced retrieval (FalkorDB, Neo4j)
- Dynamic tool selection & routing
- Forward-looking active RAG (FLARE)
- Self-correcting retrieval pipelines
- Sub-500ms latency at 10M+ document scale
Shipped artifacts, not slide decks.
Every engagement ends with working software, documented systems, and a team that knows how to extend them.
End-to-end retrieval pipeline
Ingestion, embedding, indexing, retrieval and generation — fully wired and production-ready.
Evaluation & observability layer
Precision, recall, latency, and faithfulness tracked continuously — not just at launch.
Vector DB deployment & tuning
Optimized index configuration, payload filtering, and scaling strategy for your data volume.
Chunking strategy documentation
Documented rationale for every chunking and embedding decision — so you can tune it later.
Your FinTech & Accounting
AI stack
starts with one call.
Book a 30-minute strategy session. We'll map your specific opportunity in fintech & accounting ai, identify the highest-leverage starting point, and tell you exactly what an engagement looks like.