ORIGIN AI®
We design and deploy Retrieval-Augmented Generation systems, agentic knowledge pipelines, and locally-hosted models that give your AI reliable, accurate, business-specific intelligence — without hallucination.
Unlike basic RAG which does a single retrieval pass, Agentic RAG systems deploy autonomous AI agents that reason about the query, decide what to retrieve, when to retrieve it, and whether the answer is good enough — iterating until confidence is high. The result is dramatically more accurate, multi-step answers over complex, fragmented knowledge bases.
We build systems where specialised agents — a retriever agent, a critic agent, a synthesis agent — collaborate through orchestration frameworks like LangGraph, CrewAI, and AutoGen. Each agent handles what it does best, producing answers that no single-agent pipeline could match.
Agentic systems re-write queries when initial retrieval is poor, use CRAG (Corrective RAG) to discard irrelevant chunks, and apply SELF-RAG to assess whether retrieved context actually answers the question — dramatically cutting hallucination rates versus naive pipelines.
Not every problem needs an agentic pipeline. We match the right retrieval architecture to your data complexity, latency requirements, and accuracy targets — from the simplest naive RAG to knowledge-graph-augmented generation.
The baseline architecture: embed documents, store in a vector DB, retrieve the top-k chunks at query time, and pass them to an LLM. Fast to build, works well for simple Q&A over structured, clean corpora.
Extends naive RAG with query rewriting, HyDE (Hypothetical Document Embeddings), hierarchical indexing, contextual compression, and cross-encoder re-ranking. Substantially higher accuracy for messy, large-scale corpora.
A plug-and-play framework where every component — retriever, reranker, reader, memory — is swappable. Enables rapid experimentation and easy A/B testing of different retrieval strategies without rebuilding the pipeline.
Combines a knowledge graph (entities + relationships) with vector search. Queries traverse the graph to find connected concepts that pure similarity search misses — essential when relationships between entities matter as much as the entities themselves.
Fuses dense vector retrieval with sparse keyword search (BM25) through Reciprocal Rank Fusion. Captures both semantic similarity and exact keyword matches — dramatically outperforming either approach alone, especially for technical queries with precise terminology.
KAG goes beyond retrieval: it deeply integrates structured knowledge (ontologies, domain schemas, formal logic) with LLM generation. The model doesn't just retrieve facts — it reasons over a machine-readable knowledge representation. Higher precision, fewer hallucinations, verifiable answers.
For regulated industries and privacy-first organisations, we deploy quantised open-source models — Llama 3, Mistral, Phi-3, Gemma — entirely on-premise or in a private VPC. No API calls to OpenAI. No data leaving your control. Full auditability, lower long-term cost, and performance that rivals hosted models for domain-specific tasks.
Run Llama 3, Mistral, Phi-3, CodeLlama, and 80+ models locally with a single command. We configure Ollama on your server, integrate it with your RAG pipeline, and expose it via a secure internal API.
GPU · CPU · M-series MacFor production deployments needing high concurrency, vLLM's PagedAttention engine delivers up to 24× higher throughput than naive serving. We deploy vLLM on your NVIDIA GPU cluster with OpenAI-compatible endpoints.
A100 · H100 · RTX SeriesFor teams that want a GUI alongside API access — LM Studio lets your analysts and developers test models locally, compare outputs, and integrate with existing tools without touching the command line.
Windows · macOS · LinuxTell us your data sources, use case, and accuracy requirements. We'll design the right RAG architecture and build it — from ingestion pipeline to production API.