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Service / LLM and RAG SolutionsCustom copilots over your private corpus. Document Q&A, semantic search, structured extraction, and full agentic workflows. We design retrieval architecture, build evaluation harnesses, and ship a system that handles your data, your scale, your accuracy bar.
Specific things we deliver under this service. Most projects combine three or four of these into one system.
Search and chat over Confluence, Notion, Google Drive, SharePoint, and PDFs. Answers with citations and source links.
Reps quote in seconds across product catalogs, pricing tiers, and contract terms. Cuts quoting time from hours to minutes.
Public RAG chatbot for documentation, knowledge bases, and product help. Trained only on approved sources.
Structured data pulled from invoices, contracts, forms, and reports. Validated against schemas with confidence scores.
Multi step LLM agents that call tools, query databases, send messages, and chain operations under guardrails.
Continuous quality monitoring. Hallucination detection. Drift detection. Production telemetry tied to model versions.
If any of these match your current operations, this service is probably the right entry point.
| Frameworks | LangChain, LlamaIndex, custom Python, occasionally raw |
|---|---|
| Vector DBs | Pinecone, Weaviate, Qdrant, pgvector, Vespa |
| Models | OpenAI, Anthropic, Cohere, open source via vLLM or Bedrock |
| Embeddings | OpenAI, Cohere, BGE, E5, fine tuned on your domain |
| Hybrid search | BM25 plus dense, reranking with Cohere or custom |
| Evaluation | Ragas, custom golden sets, LLM as judge, human eval |
| Hosting | AWS, GCP, Azure, on prem when required |
| Pricing | $12k to $50k build plus monthly inference |
It depends entirely on retrieval quality. With well chunked data, hybrid retrieval, and a good reranker, we typically achieve 90 to 95% answer accuracy on internal corpora. We measure this rigorously, not by feel.
Yes, when it actually helps. Most use cases are better solved with retrieval plus prompting. Fine tuning is reserved for cases with consistent output structure or strong domain language. We tell you which one applies.
No. We deploy in your cloud, use enterprise API tiers where data is not retained for training, and can run fully on premise with open source models when sensitivity requires it.
Citation requirements, confidence thresholds, refusal patterns for unknown topics, and evaluation harnesses that catch regressions before they ship. Hallucination is an engineering problem, not a mystery.
Re indexing on document updates, model upgrades when frontier models advance, evaluation runs on golden sets, and quarterly architecture reviews. Optional retainer covers all of this.
First call is 30 minutes. We will tell you whether this service is the right fit, and if not, which one is.