LLM Integration Services

End-to-end retrieval-augmented generation built on your actual data sources: PDFs, databases, APIs, SharePoint, Confluence, and Notion, not a prototype that only works on sample documents. We handle ingestion, chunking strategy, embedding selection, and vector store indexing in Pinecone, Weaviate, pgvector, or Qdrant. Hybrid search with cross-encoder re-ranking consistently beats dense-only retrieval on real query sets, so that is our default. Retrieval quality drives RAG output quality, so we measure it explicitly with the RAGAS evaluation framework before launch.

Language models that call your APIs and internal tools to complete tasks: look up a customer in Salesforce, create a Zendesk ticket, send a Slack message, all from a natural language instruction. We design function signatures so the model's tool selection stays accurate, and build handlers with retries, timeouts, and structured errors the model can reason about. Consequential actions like sending emails or triggering payments get human-in-the-loop checkpoints: the model proposes, a human authorises.

LLMs configured to reliably produce structured JSON that maps directly to your data model, so contract clauses, invoice fields, and document classifications land in your database without manual parsing or cleanup. We enforce schemas with OpenAI Structured Outputs or Anthropic tool use, plus a Pydantic validation layer that retries when output passes JSON parsing but fails business rules. Low-confidence fields route to a human review queue instead of auto-posting. Typical invoice extraction accuracy: 94-97% single-pass, 97-99% with the validation and retry layer.

AI agents that reason through multi-step tasks autonomously: research a supplier, summarise risk flags, draft a due diligence memo, and route it for review, all from a single instruction. We build on LangGraph, whose explicit graph structure makes agent behaviour predictable and testable rather than emergent from unconstrained loops. Human-in-the-loop checkpoints sit before consequential actions, and LangSmith or Langfuse traces every reasoning step and tool call. Failure modes like infinite loops and hallucinated tool inputs are addressed in the architecture, not discovered in production.

System prompts engineered for consistency, accuracy, and token efficiency, not written once and assumed to work. Every prompt is evaluated against a labelled dataset of 50-200 representative inputs before production, replacing subjective "it looks right" checks with measured format compliance and instruction-following rates. Prompts live in version control alongside their evaluation results, and regression tests run on every deployment. When OpenAI, Anthropic, or Google ships a new model version, we re-evaluate your prompts against it before migrating.

Evaluation frameworks that measure whether your LLM integration is working correctly, and alert you before users discover it isn't. The evaluation dataset is built from real production queries, labelled with expected outputs by domain experts. Automated evals run on every deployment, failing any release that drops accuracy beyond the defined threshold. LangSmith or Langfuse instruments every call with latency, token counts, and error rates, plus cost attribution per feature or tenant, so "LLM costs are too high" becomes a specific, fixable number.