Sython AI Services
We build retrieval and hybrid-search systems that make LLM outputs easier to verify and operate. That includes lexical and dense retrieval, reranking, citations, graph-structured knowledge when relationships matter, and multi-step retrieval when one lookup is not enough.
On this site, CAG means context-augmented generation: packing curated source text into long model context. We treat RAG and CAG as architecture choices that need the same evaluation discipline.
Design source-specific ingestion and chunking approaches to maximize retrievability and preserve semantic meaning.
Combine lexical, dense, and reranker stages—the usual hybrid-search stack—to improve recall, precision, and hit-rate@k for difficult enterprise queries.
Pair retrieval with answer-level groundedness checks, abstention policies, and staged rollouts so higher recall does not silently increase unsupported generations.
Design citation formats, chunk-to-source traceability, and integrity checks so enterprise users can audit answers against originals.
Choose and tune vector databases (pgvector, Pinecone, Weaviate, Qdrant, Milvus, Chroma) based on scale and ops constraints.
For long-context models, optimize context packing, truncation policy, and relevance ordering. Where providers expose prompt caching or KV reuse for stable prefixes, validate cost and correctness together so caching does not mask regressions.
We adapt architecture to your constraints, model family, and compliance profile.
Vector databases and extensions—distinct from orchestration frameworks above.
A product team needed trusted answers across docs, tickets, and runbooks. We implemented hybrid retrieval with reranking and source attribution, improving factuality in benchmarked staging workloads and reducing unsupported responses before rollout.
Use RAG when external knowledge changes frequently or corpus size is large. Use context-augmented generation (our CAG meaning) when a stable, curated corpus fits effectively in long context windows and you can evaluate quality with the same rigor as retrieval-heavy stacks.
We compare operational complexity, latency, cost, and feature needs. For many teams already on Postgres, pgvector is a strong baseline before specialized stores.
Agentic RAG lets the model issue follow-up retrievals or tool calls based on intermediate results—useful when a single vector lookup is not enough. Graph RAG (or knowledge-graph-augmented retrieval) helps when answers depend on explicit relationships between entities, not just similarity to chunks.
We help teams move from demos to measurable business outcomes with robust quality, latency, and cost controls.