SERVICE — 002 / RETRIEVAL & KNOWLEDGE
Retrieval & Knowledge
RAG done properly. Embeddings, vector search, and knowledge graphs that ground your models in real, current context — so answers cite your data instead of hallucinating around it.
// WHAT YOU ACTUALLY GET
Grounding, from raw documents to cited answers.
Ingestion
Document & data parsing
Chunking strategies
Metadata extraction
Incremental sync
Embeddings
Model selection & tuning
Vector stores
Hybrid dense + keyword
Knowledge graphs
Entity & relation extraction
Graph + vector retrieval
Structured reasoning
Provenance & citations
Quality
Retrieval evals
Freshness & TTL
// ANATOMY OF A RETRIEVAL PIPELINE
From a pile of documents to a grounded, cited answer.
Parse PDFs, DBs, APIs, transcripts
Smart splitting, vectorized
Hybrid store with metadata
Dense + keyword recall
Precision over raw recall
Answer tied to sources
RETRIEVAL STACK
pgvector / Pinecone
vector store
Neo4j
knowledge graph
Embedding models
hosted or local
Rerankers
precision layer
Eval harness
recall & precision
// THE ENGAGEMENT
From first call to shipped system.
01
Map the system
We start with architecture, not prompts. Where data lives, what has to be reliable, and what "done" actually means.
02
Build the stack
API, retrieval, models, and infrastructure assembled as one coherent system — not a notebook glued to a UI.
03
Harden & evaluate
Evals, observability, and failure modes. Reliability engineering applied to non-deterministic systems.
04
Ship & operate
CI/CD, monitoring, and a real maintenance path. The system goes live — and stays live.
START SMALL
Not sure it's even an agent problem yet?
Begin with a fixed-scope discovery sprint. You walk away with a real architecture, a build plan, and an honest read on feasibility — yours to keep, whether or not we build it together.
Let's build something that actually ships.
Tell us what you're trying to build. We'll tell you straight whether — and how — agentic systems get you there.
Start a conversation