TIER 2 — CORE AI SERVICE
LLM Integration & Orchestration
The problem isn't picking an LLM — it's making it work reliably inside existing enterprise architecture.
THE REAL CHALLENGE
Integration is harder than selection
We work with OpenAI, Anthropic, Google, Mistral, and open-source models. Recommendations are driven by your use case, cost profile, and data residency requirements — not vendor relationships.
Callbrief.ai runs on Claude Opus specifically because call preparation demands the highest tier of reasoning. Mavenn.ai orchestrates multiple models because consensus reduces hallucination risk. Every architecture decision has a reason.
LLM INTEGRATION SCOPE
What we build
Retrieval-Augmented Generation connected to your enterprise data — documents, databases, EHR systems, knowledge bases. The model answers from your data, not from general training.
Right model for right task. Not single-vendor lock-in. We select models based on your use case, cost profile, and data residency requirements — not vendor relationships.
Monitoring, fallback routing, cost optimization, and the operational discipline that keeps AI systems running after launch.
Domain-specific accuracy through systematic prompt engineering and, where justified, fine-tuning on your data. Measurable improvement, not guesswork.
LLMs deployed within your own infrastructure — on-premise hardware, AWS Bedrock, Azure OpenAI. Complete data sovereignty. Nothing touches the public internet.
DATA SOVEREIGNTY
Local and private deployment
As the claw ecosystem matures — with OpenClaw, NemoClaw, and proprietary agent platforms — multi-model orchestration becomes critical infrastructure. Claws need to route to the right model for each subtask. We build that routing layer.
DISCUSS YOUR ARCHITECTURE