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Allan Cutler is the CTO and Co-Founder of Manta Health, where he leads product and technology strategy across the platform. He brings more than 20 years of experience building and scaling software systems, with a track record that spans startup environments and enterprise organizations, and a consistent focus on turning complex, high-stakes requirements into systems that are reliable and usable in production.
Allan's work at Manta is grounded in a specific technical thesis: coverage keeps breaking not because teams lack process discipline, but because the underlying data is fragmented, inconsistent, and constantly changing. Payer rules update. Formularies shift. Authorization requirements vary by payer, plan, CPT code, and patient history. No manual workflow can keep pace with that variability at scale. That makes coverage a continuous interpretation challenge, not a checklist problem. His focus is building the layer that interprets payer requirements in clinical context before action is taken.
He is responsible for Manta's AI-native, compliance-first platform architecture and the healthcare integrations that power eligibility verification, prior authorization, appeals, and patient financial clearance workflows. The platform connects to 1,300+ payers, submits authorizations via fax, phone, and portal with minimal human oversight, and surfaces payer-specific documentation requirements at the point of ordering, not after a denial has already been received.
Allan writes on AI architecture in regulated healthcare environments, payer interoperability standards including the Da Vinci PAS framework, and the technical underpinnings of building automation that works in the real constraints of ambulatory practice operations.
Coverage data is inherently fragmented and unstable. A payer's prior authorization requirements for a specific CPT code can differ by plan, by state, and by clinical indication, and those requirements change without systematic notification to the practices that depend on them. Most practices compensate by maintaining manual knowledge bases or relying on staff who have learned payer behavior through experience. That knowledge doesn't transfer, doesn't scale, and breaks when staff turns over. Manta's job is to make that interpretation continuous, automated, and embedded in the workflow before the order goes out.
The platform connects across 1,300+ payers and maintains payer-specific logic for authorization requirements, documentation criteria, and submission protocols. When a practice schedules a procedure, Manta resolves whether prior authorization is required for that specific payer, plan, and CPT combination in real time, prepares the documentation package, and routes the submission through the appropriate channel, whether that's a portal, fax, or phone-based workflow. The determination that used to take a specialist 22 minutes happens in seconds.
It means AI is embedded in the core workflow rather than layered on top of it. Manta doesn't produce a report a human then acts on. The AI reads payer requirements, interprets clinical documentation, identifies gaps before submission, drafts appeal letters when denials come back, and tracks authorization status without a human initiating each step. The compliance and safety requirements in healthcare are real constraints on how that AI operates, and building within those constraints from the start is different from retrofitting an existing system.
Da Vinci PAS is a FHIR-based standard developed by HL7 that enables prior authorization requests and responses to be exchanged electronically between providers and payers. CMS-0057-F requires Medicare Advantage and other payers to implement it by 2027. For specialty practices, it means a meaningful reduction in the phone and fax volume that currently consumes authorization staff time, and a structured data layer that makes automated PA workflows more reliable. Manta is building against that standard now so practices aren't reacting to the compliance deadline.
The failure mode is automation that handles the easy cases and pushes edge cases back to staff without context. A tool that auto-submits clean prior auths but returns ambiguous payer responses with no guidance doesn't reduce burden, it redistributes it. The standard Manta builds to is that every exception should come back with enough context for a non-specialist to resolve it quickly. That requires the system to understand the payer requirements well enough to explain what's missing, not just flag that something is wrong.
Say goodbye to faxes, lengthy phone calls, and tedious RCM admin.
