Lead Solutions Architect Skills Challenge
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 —…
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What You'll Be Doing
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.
What You'll Accomplish
Design a technically credible solution architecture that integrates mpathic's products into a HIPAA-compliant AWS environment
Navigate complex multi-stakeholder deal dynamics including a cautious technical gatekeeper and split budget authority
Develop a 4-week POC scoped to deliver measurable value before a fixed model release deadline
Build repeatable SA function assets and playbooks from a single enterprise engagement
Demonstrate critical evaluation and iterative use of AI tools in a regulated, high-stakes context
How Your Work Will Be Scored
What to Submit
Solution Architecture Brief
Format: .pdf, .doc, .docx, .rtf, .txt, .md
Technical Discovery & Solution Design Brief
Create a written brief (1–3 pages) that you would use to prepare for and lead a technical discovery and solution design session with Clarion's stakeholders. This brief should include:
- Discovery Plan: The specific questions you would ask during technical discovery, organized by stakeholder and topic area. Your questions should demonstrate understanding of Clarion's environment and surface the information you need to design the right solution.
- Proposed Solution Architecture: A description of how mpathic's products and services (Observing Agent API, clinician-led red teaming, human data and benchmarking, mpathic Studio) would integrate into Clarion's existing infrastructure to address their safety evaluation and monitoring gaps. Include enough technical detail that Clarion's ML platform team could evaluate feasibility.
- Technical Validation Plan: Your recommended approach for proving value to Clarion — what would you propose as a POC or pilot, how would you scope it, what success criteria would you define, and how does it map to advancing the deal?
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README Document with Sections A, B, and C
Format: .pdf, .doc, .docx, .rtf, .txt, .md
Written Document
Section A — Deal Strategy & Stakeholder Navigation
Explain your strategy for advancing this deal through the multi-stakeholder dynamics described above. Specifically address:
- How would you bring Marcus Chen (ML Platform Lead) from cautious to supportive — without undermining the work his team has already done?
- How would you handle the technical objection that Clarion's in-house evaluation framework already covers their needs?
- What is your recommended sequence of stakeholder engagements, and why?
Section B — SA Function Playbook Extract
As mpathic's first Solutions Architect, every deal is also a function-building opportunity. Based on this Clarion engagement, describe:
- What repeatable assets would you create from this engagement that could be reused for future healthcare or enterprise AI safety prospects?
- How would you structure the SA engagement model for deals like this — what does the SA do at each stage, and how does the SA partner with Sales?
- What is the first asset you would prioritize building, and why?
Section C — AI Usage Log (Mandatory)
This is not a trick. We want to see how you work with AI — not whether you used it.
In a short section of your README, document your AI collaboration process. For each significant interaction with an AI tool, briefly note:
- What you asked the AI to help with
- What it gave you
- What you kept, changed, or rejected — and why
Three interactions documented is sufficient. The log does not need to be exhaustive.
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Video Walkthrough
Format: .mp4, .mov, .webm
Record a video walkthrough and upload it directly on the Provn platform as an MP4 or MOV file. Structure your video as follows:
- Summary (60–90 seconds): Clarion's core problem and your recommended solution in plain language — as if you were opening a call with their VP of Engineering.
- Architecture Walkthrough (2–4 minutes): Walk through your proposed solution architecture. Explain your key design decisions, how mpathic's products map to Clarion's needs, and what trade-offs you made.
- Deal Strategy (1–2 minutes): Explain how you would navigate the stakeholder dynamics — particularly how you would bring the ML Platform Lead on board and structure the technical validation to build momentum.
- Mandatory AI Question (1–2 minutes): Walk me through one moment where you disagreed with, pushed back on, or redirected what the AI gave you — and what you did instead. Name the specific moment. Explain what the AI produced that didn't meet the bar, what you did differently, and why.
- Reflection (30–60 seconds): What would you do differently with more time or more context about Clarion?
Communication presence is part of this role. We assess technical credibility, ability to translate between technical and business audiences, and confidence in leading a customer conversation. Speak naturally — we score the quality and clarity of ideas, not accent, filler words, or pacing.
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