We built asynchronous multi-agent systems using the A2A (Agent-to-Agent) protocol. These agents independently retrieve, debate, and execute workflows that accelerate decision-making by 40%.
Our MCP orchestration server routes API calls, caches agent responses, and reduces LLM costs by up to 60%. It also coordinates retries and fallback policies with real-time telemetry.
Through reward modeling and targeted RLHF on high-risk queries, hallucinations were reduced by 48% across enterprise QA tasks. Fine-tuning focused on high-priority customer use cases.
We built explainability layers, rejection classifiers, and usage logging with red-teaming integration. This supports responsible LLM deployment in regulated domains.
We implemented programmatic evals (truthfulness, toxicity, retrieval success) using XSIM, BLEURT, and domain-specific metrics. Our pipeline runs nightly across models and releases.
Tailor LLMs to legal, healthcare, and finance datasets to improve accuracy, reduce hallucinations, and meet domain-specific compliance standards.
Deploy AI agents with intelligent fallback strategies, cost-aware model routing, and real-time monitoring to maximize performance and reliability.
Enable semantically rich document retrieval by embedding internal knowledge bases and powering LLMs with fast vector similarity search.
Boost model training with synthetic data pipelines that simulate edge cases, balance class distributions, and reduce dependency on human labeling.
Embed tracing, rejection logging, and usage analytics to ensure responsible LLM deployments that align with audit, security, and privacy frameworks.