Custom LLM Integration & RAG Implementation
Most teams don't need a new app — they need language intelligence inside the systems they already run. We embed LLMs behind your existing interfaces and APIs, grounded in your private data, with the guardrails production demands.
What it includes
Retrieval pipelines over your documents — SharePoint, Google Drive, Confluence, Notion, S3 — with semantic search, source citations, and permission-aware access.
A structured bake-off across Claude, GPT, Gemini, and open-weight models against your actual tasks, scored on accuracy, latency, and cost per call — not vendor benchmarks.
Injection resistance, PII redaction, output validation, and content policies enforced at the boundary, with red-team test suites you can rerun on every change.
In-product assistants that draft, summarize, and answer inside your CRM, help desk, or internal tools — matching your UI, not a bolted-on chat bubble.
What you get
What lands in your hands at the end, not activity reports.
- Working integration in your product or workflow, behind a feature flag
- Evaluation harness with task-level accuracy and cost benchmarks
- Prompt and guardrail library under version control
- Runbook covering monitoring, fallbacks, and model-swap procedure
Where it is used
- 01A support team answering from thousands of policy documents with cited sources
- 02Analysts querying contracts and filings in plain language instead of keyword search
- 03Automated first-draft responses in the tone and format your team already uses
Tools and platforms we work with
Solutions that use it
The same service, framed by the problem it solves.