Governed AI for regulated healthcare and life sciences operations
We build governed AI workflows for regulated healthcare and life sciences teams. Outputs are traceable and reviewed when needed. Systems are monitored in production so they stay reliable after launch.
Governed AI means
Governed AI means AI with audit logs, review steps, and monitoring.
- Outputs link back to sources when possible
- High-risk steps require human approval
- Quality is measured over time, not assumed
Choose your path
Government health & payers
UM, appeals, eligibility, and assessments. Throughput, defensibility, and audit trails.
Learn morePharma / Biopharma
GxP ready AI workflows for clinical, regulatory, safety, and medical operations. Built for validation and inspection readiness.
Learn moreMed device / Medtech
Quality system aligned AI for postmarket, investigations, and CAPA. Traceable and controlled.
Learn morePlatforms
Governed agentic features and workflow automation for life sciences and clinician platforms. Audit trails, review gates, measurable performance.
Learn moreWhat we deliver
Document intelligence
Inputs include PDFs, clinical packets, and scientific reports. Outputs include structured fields, reviewer ready summaries, and citations to source pages. Impact: reduced rekeying and faster review.
Governed assistants & agents
Inputs include SOPs, policies, and approved knowledge bases. Outputs include grounded answers, review queues, and escalation reasons. Impact: faster responses with less manual search.
Production delivery
Deployed in your environment with permissioned access, evaluation harnesses, and monitoring dashboards. Outputs include change control plans and audit logs. Impact: quality stays measured after launch.
Who we work with
Teams in healthcare and life sciences that need AI used in production workflows, not prototypes. Typical partners include clinical operations, medical affairs, and data teams responsible for regulated work.
Regulated workflows
You need traceability, auditability, and clear exception handling.
Operational accountability
Success is measured by time saved, reduced errors, and faster turnaround.
High stakes decisions
Review steps and audit logs are part of the system design.
Delivery approach
Define
the workflow, constraints, and success metrics
Design
the architecture, data flows, and governance/controls
Build
in small increments with testing and production readiness
Deploy & iterate
with monitoring, feedback loops, and continuous improvements
Case studies
Anonymized examples available. Request a redacted case study pack.
AI-Powered Clinical Intelligence, Not Just OCR
Inputs: document heavy clinical packets uploaded through https://app.sidegem.com. Outputs: structured fields, reviewer ready summaries, and alerts tied to source pages. Impact: faster ingestion with fewer manual errors and earlier clinical alerts.
Read the case studyAI Chatbot for a Physician Member Platform
Inputs: vetted cardiology articles and a member knowledge base from a cardiology publisher. Outputs: grounded answers with citations and comparisons across publications. Impact: faster clinician prep and better discovery of relevant evidence.
Read the case studyTechnical capabilities
We design permissioned data pipelines, evaluation harnesses, and governance controls with audit logs and monitoring dashboards. Tooling is chosen to fit the workflow, data sensitivity, and deployment environment.
Common tools include AWS, Python, React, Postgres, vector databases (e.g., Pinecone), and modern model/provider ecosystems (e.g., OpenAI and Gemini).
Build something that passes scrutiny
If you have a document heavy workflow in a regulated environment, we can scope a pilot quickly and ship into production with governance built in.