About

Built for the leader who's already past incremental

Archetype helps healthcare leaders across pharma, biotech, payers, provider systems, pharmacies, medical devices, and health-tech move from a portfolio of pilots to a redesigned operating model. Strategy that ships. Implementation that respects what regulated work actually is.

Sundar Ganapathy

Managing Partner

Sundar Ganapathy

Sundar Ganapathy founded Archetype to help pharmaceutical and healthcare executives capture measurable value from agentic AI inside the regulated work, not in pilot, at scale, against the original business case. He spent eight years at McKinsey & Company, most recently leading the Digital Growth for Life Sciences practice, where he advised top-15 pharma CCOs, Chief Digital Officers, and Medical Affairs heads on the operational reality of shipping commercial AI rather than the strategy of commissioning more pilots. His work at McKinsey delivered over $200M in Year 1 impact from at-scale AI solutions across pharma commercial and Medical Affairs, alongside a portfolio of earlier regulated-industry firsts, incubating and launching the first DTC pharmacy owned and operated by a pharmaco among them.

He left to build Archetype because the same pattern kept surfacing at the end of every engagement. He calls it the translation tax: value erodes at every handoff in a legacy consulting engagement because no single voice, strategic, expert, operational, or technical, owns end-to-end operationalization of the business case. The strategy lands. The SME deepens it. Ops scopes it. Tech builds something adjacent to what the original business case implied. Six months in, the deliverable works in demo and the P&L impact has quietly evaporated.

Archetype was built to close that gap inside the highest-stakes career challenge most senior healthcare executives will face: AI enablement in regulated industries. Winning isn't a matter of adopting AI for daily prompting. It requires rewiring the organization and rethinking how work gets done, across the medical-commercial firewall, through MLR review, inside privacy and PV constraints, on top of data architecture most pharmas haven't funded yet. The cost of getting it wrong isn't a failed pilot. It's twelve months of compounding operating-model debt while the orgs that moved early start to pull ahead.

Archetype is built for the executive whose name is on the operating-model commitment in October, and who has 24 months of expected runway to ship it. Not for the leader who wants another pilot. If that's the seat you're in, the next twelve months matter more than the calendar implies.

The resourcing model breaks the mold of legacy consulting, because Archetype doesn't just advise clients on AI, it operates as an AI-native organization. The team that frames the question is the team that ships the work, with autonomous agent teams carrying the workstream structure underneath and a senior leader carrying the client relationship on top.

Put another way: most consulting firms give you a junior team and 1/5th of a peer-level Partner's time. Archetype gives you the peer-level Partner and virtualizes the team overhead. Where agentic leverage isn't enough, we hand-select specialized senior talent and assemble small teams around each engagement, elite gig professionals, fractional executives, boutique agencies and development studios, each proven in delivering growth inside regulated healthcare. The result is the throughput of a full consulting pod without the overhead, the steering-committee theatre, or the partner-to-team dilution.

How the firm runs.

Lean on purpose. The judgment layer stays human

Archetype is built inverted from the traditional consulting pyramid. The senior team that frames the question is the team that ships the work, with an agentic system carrying the workstream structure underneath.

An agentic operating system, not a staffing pyramid

The firm runs on a custom agentic system that handles the work an Engagement Manager and Associate would handle inside a tier-one consulting pod: workstream structure, analysis, first-draft synthesis, longitudinal stakeholder memory across a six-month engagement. Trained on the frameworks that make tier-one consulting work, then sharpened against the regulated commercial reality. The agents do the structural work. The Partner does the judgment work.

Senior subject-matter experts, no staffing committee

When an engagement needs deep domain work, the agents pair with humans who actually carry that domain: ex-Partner-level commercial strategists, Medical Affairs operating-model designers, regulated-data architects, AI engineers who have shipped agentic systems inside MLR review, payer-network operating-model experts, clinical-AI delivery leads from large provider systems. People you'd have asked for at a tier-one firm, without the three-layer summary chain between their thinking and your operating reality.

Strategy and build on the same engagement

Discovery, requirements, design, prototype, and pilot ship inside one team. No translation loss between the recommendation and the working system. No re-procurement to a build firm in month four. The decision-forcing artifact and the working artifact are produced by the same brains, on the same engagement, against the same business case.

What that means for clients

Calibrated recommendations in front of you inside a week of intake, not six. Strategy that ships rather than strategy that hands off. Senior judgment at every layer of the work, without paying for five layers to get to one. Clients spend their time on the decisions that matter, not on managing the handoff layer underneath.

Specialist bench.

AI-native specialists across every healthcare discipline

Each engagement pulls from a curated bench based on what the work actually needs. Every specialist has shipped AI inside regulated healthcare contexts, pharma, biotech, payers, providers, pharmacies, medical devices, or health-tech, not adjacent ones.

  1. 01

    AI engineering

    Agentic systems design, prompt architecture, model orchestration, eval frameworks, and integration with regulated data pipelines.

  2. 02

    Data architecture and governance

    Healthcare data strategy, MDM, regulated permission models, HIPAA-aware infrastructure, consent-aware activation across pharma, payer-claims, EHR, and pharmacy environments.

  3. 03

    Medical Affairs

    Field medical operating models, MSL effectiveness, scientific exchange systems, KOL strategy, medical content workflows.

  4. 04

    Commercial strategy and launch

    Brand strategy, launch architecture, segmentation, omnichannel design, field force effectiveness, commercial operating models across pharma, payer-product, pharmacy, and medical device.

  5. 05

    Regulated-market design

    LMR/PRC, PV-adjacent workflows, claims substantiation, IT security, the medical-commercial firewall.

  6. 06

    Workflow and product design

    User research, requirements writing, design sprint facilitation, prototyping, pilot readiness.

  7. 07

    Change and adoption

    Organizational design, adoption playbooks, internal communications, capability-building inside healthcare orgs.

  8. 08

    Implementation and delivery

    Pilot design, project management, vendor coordination, pilot-to-production transitions.

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