AI Workflow Automation · The Trust Layer
Cross the chasm from impressive pilot to trusted production.
The trust layer for regulated AI: a prompt and model registry, an evaluation harness, and monitoring and compliance, so an agent is measurable, auditable, and defensible, not a demo nobody can put in front of a regulator.
Book an initial consultation Start with an AI Readiness Audit
Our AI pilot is impressive, but how do we trust it in production, in a regulated setting?
The gap between a demo and production is governance: knowing which prompt and model are live, proving the system behaves on real cases, and catching drift before it reaches a decision. In life sciences that gap is also a compliance requirement. This package builds the registry, evaluation, and monitoring that make an AI system auditable and safe to run.
What's included
Prompt and model registry
A registry of which prompts and models are in use, versioned, so you always know what is live and can roll back.
Evaluation harness
An evaluation harness with realistic test cases and rubrics, so you can prove the system behaves before and after every change.
Monitoring and compliance
Production monitoring for drift, errors, and cost, with the audit trail a regulated life sciences setting requires.
Responsible-AI gates
Quality and safety gates wired into the path to production, so nothing ships without passing them.
How it works
- 1
Register and version
We stand up the prompt and model registry so what is live is known and reversible.
- 2
Build the eval harness
We build evaluation with realistic cases and rubrics, so behavior is provable.
- 3
Monitor and audit
We add production monitoring and the audit trail that makes the system defensible.
What you walk away with
- A versioned prompt and model registry with rollback
- An evaluation harness that proves behavior on real cases
- Production monitoring for drift, errors, and cost
- The audit trail and gates a regulated setting requires
Frequently asked
- Do we need this for every AI project?
- Quick wins may not need the full layer, but anything that touches a decision, a customer, or regulated data does. It is what lets a life sciences company put AI in front of an auditor with confidence.
- When should we add it?
- Before production, and ideally designed in alongside the agent or system rather than bolted on. It pairs naturally with AI Agent Build and Agentic Orchestration.
Make your AI defensible
Book a consultation to build the registry, evaluation, and monitoring that make production AI trustworthy in a regulated setting.
Book an initial consultation Start with an AI Readiness Audit
Where this leads next
AI Agent Build
Build the production agent this governance layer makes trustworthy.
Explore the packageAgentic Orchestration
Govern and evaluate a multi-agent system before it runs in production.
Explore the packageBrowse the full AI Workflow Automation program
Governance is the trust layer over the whole stack. See how it makes assistants, workflows, and agents production-ready.