Governed AI for regulated healthcare and life sciences

Secure, explainable, and accountable AI.

In a regulated business, an answer you cannot trace is a liability, not an asset. Trust means every output shows its work: the records it read, the logic it applied, and the person accountable for the decision. An assistant inherits the permissions of the user in front of it, so it never surfaces data that person is not already cleared to see. When an auditor asks why a number moved, the answer is on the record rather than in someone's memory.

• Access modeled on your existing security policy, not a new set of permissions to reason about.
• Explainable outputs that cite the source records and the steps behind them.
• A named owner and a clear line of accountability for every process the assistant touches.

Policies, risk management, and human oversight.

Governance answers four questions before a single assistant goes live: who can use AI, for what, on which data, and how outputs get reviewed. We write that policy with you, score each use case by impact and likelihood in a risk register your quality and compliance teams recognize, and set a human review gate so nothing reaches a regulated record without a person approving it. The controls map to frameworks you already answer to, including SOX 302/404 and your internal access model.

• A written AI use policy scoped to roles, data classes, and approved use cases.
• A risk register that ranks each use case and defines the review it requires.
• A human review gate on every output that touches a regulated or financial record.

Testing, documentation, and evidence appropriate for regulated environments.

This is the pillar most AI pilots skip, and it is the reason most of them cannot survive an inspection. Validation means test scripts with expected results, documented behavior for each assistant, and the evidence package your quality team and your auditors expect. We build it around 21 CFR Part 11 expectations and GAMP 5, Second Edition thinking, and we install and test in sandbox before anything reaches production. The runbooks stay with you, so the work holds up through staff turnover and repeat audits.

• Test scripts and expected results captured before an assistant reaches production.
• Documented behavior, review logs, and an audit trail for every action taken.
• Validation evidence aligned to 21 CFR Part 11 and GAMP 5, Second Edition.

Integration into enterprise systems and business processes.

A pilot that never reaches the rest of the team returns nothing. Deployment connects the assistants to the systems your work already lives in, NetSuite or any ERP, your CRM, your contract and document repositories, then puts them into the hands of real roles. We roll out in stages, starting with managers, and we include the training and prompting enablement that decides whether adoption holds. This is the same delivery discipline Archer brings to an ERP implementation, applied to AI.

• Connections across NetSuite or any ERP, CRM, contracts, and document systems.
• A phased rollout by role and function, sequenced so each group succeeds before the next.
• Training, prompting enablement, and runbooks so the team keeps running it after we leave.