Workflow Automation.

We ground every response in your real data using retrieval-augmented generation, set hard guardrails on what the model can and cannot say, and log every interaction for review. For high-stakes use cases we add a human-in-the-loop step before any external action is taken.

By default, neither OpenAI nor Anthropic train on API traffic. We configure data-retention to the minimum the use case requires, and for clients with strict residency requirements we deploy through Azure OpenAI or AWS Bedrock so data stays in-region. PIPEDA and SOC 2 alignment is part of the default deployment checklist.

Most of our pilots aim to save 5–15 hours per person per week on a defined task within 90 days. We measure baseline hours before the pilot ships so the return is provable. If the pilot does not move the number, we kill it instead of scaling it.

Both. We pick the model based on the task — Anthropic and OpenAI for reasoning-heavy work, Llama and Qwen for cost-sensitive deployments or on-prem requirements. The architecture is model-agnostic so swapping later is a config change, not a rewrite.