mpathic delivers end-to-end safety evaluation across the model lifecycle—from policy development and curation of mpathic ExpertsTM to ground truth datasets and real-time feedback loops. We help AI builders train, evaluate, and calibrate models for nuanced human behavior and high-risk environments, so they perform safely in the real world.
Solutions Architect Brief: Clarion Health Systems The Scenario You are the first Solutions Architect at mpathic.ai, a clinician-founded AI safety company. mpathic delivers end-to-end safety evaluation across the AI model lifecycle — including clinician-led red teaming, human data and benchmarking, ground truth datasets, real-time safety monitoring (Observing Agent API), and the mpathic Studio analytics platform. Your customers are ML platform teams, safety/alignment leaders, and data organizations at companies building and deploying AI in high-risk settings. You have been brought into a deal by mpathic's Head of Sales. Here is the situation: The Prospect: Clarion Health Systems Clarion Health Systems is a large, US-based digital health company that operates an AI-powered clinical decision support platform used by over 40,000 healthcare providers. Their platform helps clinicians with differential diagnosis suggestions, treatment planning, and patient communication. Clarion has 400+ employees, a 60-person engineering org, and a dedicated 8-person AI Safety & Quality team. The Problem Clarion's AI Safety & Quality team has flagged a growing concern: as they've expanded their AI models to handle more sensitive clinical scenarios (mental health screening, pediatric care guidance, substance use risk assessment), they've seen a sharp increase in edge-case failures. In the past quarter, their internal review process identified 47 instances where the AI provided responses that their clinical advisory board rated as "potentially harmful" — up from 12 the prior quarter. Their current safety evaluation process is largely manual: a team of 3 internal clinicians reviews a sample of flagged conversations weekly. This approach is not scaling. Clarion's VP of Engineering and their Head of AI Safety have both expressed urgency about improving their evaluation and monitoring infrastructure before their next major model release (scheduled for 10 weeks from now). Current Infrastructure Their AI platform runs on AWS (EKS, RDS, S3, CloudWatch). All patient data must remain within their HIPAA-compliant VPC. They have an existing in-house evaluation framework (built over 14 months by their ML platform team) that runs automated safety checks on model outputs using a rules-based taxonomy. The ML platform team lead is proud of this work and considers it a competitive advantage. Their clinical advisory board sets safety standards but has no direct involvement in the technical evaluation pipeline. The AI Safety & Quality team reports to the VP of Engineering, but budget authority for new vendor tools sits with the Chief Medical Officer's office. Clarion recently completed SOC 2 Type II certification and will not engage with vendors who cannot demonstrate equivalent compliance. Deal Dynamics | Stakeholder | Role | Disposition | |---|---|---| | Dr. Priya Nair | Head of AI Safety | Internal champion. Initiated outreach to mpathic after a conference presentation on clinician-led red teaming. | | Marcus Chen | ML Platform Lead | Cautious. His team built the existing evaluation framework and views external tools as a potential threat to their roadmap. Has not yet agreed to a technical evaluation meeting. | | Sarah Kim | VP of Engineering | Wants a solution fast but is concerned about integration complexity and timeline risk. | | CMO's Office | Budget Authority | Controls budget but will defer to Dr. Nair's technical recommendation. | From mpathic's Head of Sales: "This could be a six-figure annual deal. Dr. Nair is bought in, but we need Marcus on board or this stalls. Sarah needs to believe we won't slow down their launch." Constraints Your solution and strategy must honor these constraints. They reflect Clarion's real operating environment. HIPAA + VPC Boundary All patient data must remain within Clarion's HIPAA-compliant AWS VPC. No data can leave their environment for processing, evaluation, or monitoring. Your architecture must account for this — any component that requires data egress is a non-starter. Augment, Don't Replace Clarion's ML platform team has invested 14 months building their in-house evaluation framework. Your proposed solution must augment and extend their existing infrastructure — not replace it. Marcus Chen's support depends on this. Any architecture that positions mpathic as a replacement for their framework will kill the deal. 4-Week POC Window Clarion's next major model release is in 10 weeks. Any POC or pilot must demonstrate measurable value within 4 weeks to be approved before the release. Scope your technical validation plan accordingly — a 12-week pilot will not get approved. Multi-Stakeholder Budget Authority Budget sits with the CMO's office, but technical approval requires the VP of Engineering's sign-off. Dr. Nair (Head of AI Safety) is the champion but does not control budget or technical approval. Your deal strategy must navigate all three.
THE SCENARIO mpathic has just closed a contract with a Fortune 50 pharmaceutical company to deploy its Observing Agent API — an AI safety monitoring platform that evaluates the quality and safety of AI-assisted clinical trial interactions in real time. The deployment spans five clinical trial sites across three countries: Two sites in the United States (HIPAA, SOC 2 Type II compliance required) One site in the United Kingdom (UK GDPR, NHS data governance standards) Two sites in Germany (EU GDPR, Data Protection Impact Assessment required) The AI system at each site monitors live conversations between clinical coordinators and trial participants — including sensitive discussions about medical eligibility, side effects, and adverse events. Monitoring accuracy directly affects whether safety signals are detected in time. There is no acceptable gap in clinical oversight during the transition from pilot to production. The program must complete full deployment across all five sites within 90 days of contract execution. You are the Senior TPM. The CTO (Brian) has final technical authority. The CEO (Dr. Grin Lord) is the executive sponsor and is directly engaged with the client's VP of Clinical Operations. Constraint: mpathic's engineering team is four people. The clinical science team owns annotation protocols and quality thresholds but does not report to engineering. The client's clinical operations team controls site access scheduling and has its own program manager — who has never worked with an AI safety vendor before. Three days before go-live at the first US site, your engineering lead flags a potential data integration issue: the client's EDC (Electronic Data Capture) system is returning inconsistent participant identifiers across API calls. It may be a configuration issue. It may be a data pipeline bug. The root cause is not yet known. You own the decision on whether to delay go-live. CONSTRAINTS Honor all of the following — strong candidates adapt to them; generic AI output will ignore them. Engineering team is four people. Your plan cannot assume unlimited engineering bandwidth for parallel site configuration. The clinical science team controls annotation quality thresholds and expert protocols. They are not in your reporting line. Your coordination plan must reflect this. The client's clinical operations PM has no prior AI safety vendor experience. Your communication framework must account for this — do not assume they understand mpathic's technical architecture. mpathic is HIPAA, SOC 2 Type II, and EU GDPR compliant. Your compliance gates must reflect actual regulatory requirements per jurisdiction — not a generic compliance checklist. The go-live decision is yours. The CTO has authority but expects you to bring a recommendation, not a question.
