DaisyAI FDE Manifesto v1

Internal Strategy Document Thomas & Michael - January 2025

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Executive Summary

We are pivoting DaisyAI from a pure SaaS play to a hybrid model: product + forward deploy engineering services. This manifesto articulates our vision for building an FDE-first company in healthcare AI.

Our thesis: The bottleneck for enterprise AI isn't models—it's implementation. Healthcare organizations have the budget and the pain, but lack the talent to transform their operations with AI. We will be that talent.

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1. What is Forward Deploy Engineering?

Forward Deploy Engineering (FDE) is a delivery model pioneered by Palantir where engineers embed directly with customers to build production systems. Unlike consultants who deliver recommendations, FDEs deliver working software.

The Core Insight Selling software isn't enough. If the buyer can't drive meaningful usage or solve real operational problems, the software won't stick. FDEs bridge this gap by rolling up their sleeves to do the work themselves.

What FDEs Do

• Embed on-site with customers (days to months)
• Write production code on customer infrastructure
• Map actual workflows (not documented processes)
• Build trust with end users
• Feed learnings back into core product

What FDEs Are Not

• Not consultants (we build, not advise)
• Not sales engineers (we implement, not demo)
• Not body shops (we improve our product, not just bill hours)

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2. Why FDE is the Bottleneck for Enterprise AI

The Implementation Crisis

The data is stark:

• 95% of AI projects fail to create business value (MIT 2024)
• 74% of companies struggle to scale AI efforts
• Only 1% have reached full AI maturity
• 800% growth in FDE hiring demand

AI models are commoditizing. What's scarce is the expertise to deploy them into messy, real-world enterprises.

The Process Reality Problem

Standard operating procedures are corporate fiction: static, incomplete, and wildly outdated. AI systems need behavioral fidelity—how the work actually gets done—not the idealized version.

This is why generic AI implementations fail. They're built against documentation, not reality.

FDEs fix this by:

1. Discovering actual processes through user shadowing
2. Integrating AI into existing tools (not ripping and replacing)
3. Building trust that reveals process nuance

The Talent Gap

Healthcare organizations face an impossible hiring challenge:

• A capable AI engineer costs $300-400K fully loaded
• Most orgs can't attract or evaluate this talent
• Even if hired, one engineer can't transform an organization

FDE services let orgs rent expertise they can't buy.

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3. Why Healthcare, Why Now

Back-Office Automation is Exploding

Three functions are reaching 90%+ automation potential:

Revenue Cycle Management (RCM)

• Claims processing and denial management
• Prior authorization handling
• Patient billing and collections

Utilization Management (UM)

• Medical necessity reviews
• Care coordination
• Discharge planning

Care Management

• Patient intake and triage
• Clinical documentation
• Population health analytics

Regulatory Tailwinds

CMS and payers are mandating electronic prior authorization. Organizations must digitize or fall behind. This creates urgency we can leverage.

Switching Costs Create Moats

HIPAA compliance, EHR integrations, and workflow complexity create natural switching costs. Once we're embedded, we're hard to replace.

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4. DaisyAI's Unique Position

What We Have

Working Product Our SaaS platform is live with 7 specialized AI agents for utilization review. This proves we can build production healthcare AI—not just talk about it.

Domain Expertise We've been building for healthcare UR/UM since inception. We understand:

• Clinical workflows and terminology
• HIPAA and compliance requirements
• Nurse and physician user needs
• Payer-provider dynamics

Wharton Network We have warm paths into enterprise buyers through:

• Alumni in healthcare leadership
• VC connections (Field Ventures, etc.)
• Prior relationships (Bain, McB, etc.)

What We're Building

The Hybrid Model Like Palantir, we'll use services to drive product adoption:

• Services get us into accounts
• Product creates recurring revenue
• Each engagement improves the platform

The Virtuous Cycle

[FDE Engagement] → [Customer Success] → [Product Improvement]
       ↑                                        ↓
       └────────── [New Capabilities] ←─────────┘

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5. Target Market & Client Identification

Ideal Client Profile

Organization Type

• Regional health systems (3-20 hospitals)
• Specialty payers and TPAs
• UM/UR outsourcing companies
• Large physician groups with value-based contracts

Size Sweet Spot

• $50M - $500M in revenue
• 500 - 5,000 employees
• Large enough to have budget, small enough to move fast

Pain Signals

• Manual review processes causing bottlenecks
• Nurse burnout and turnover
• Prior auth backlogs creating patient harm
• Digital transformation initiatives underway
• Recent funding or leadership changes

Entry Points

Disqualifying Signals

• Bureaucratic procurement (12+ month cycles)
• No budget authority below C-suite
• "Innovation theater" without execution mandate
• Already deep in competing implementation

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6. Go-to-Market Strategy

Phase 1: Relationship-Led (Jan-Mar 2025)

Objective: Land 2-3 meaningful engagements through warm intros.

Tactics:

• Activate Wharton alumni network
• Leverage VC introductions (Field Ventures contact)
• Convert warm leads (Jose, Dr. Bauman, Premira)
• Offer free pilots to build case studies

Success Metric: 5+ serious conversations by Jan 31.

Phase 2: Content + Outbound (Q1-Q2 2025)

Objective: Generate inbound interest and scalable pipeline.

Tactics:

• Publish thought leadership (LinkedIn, Substack)
• Build public project portfolio
• Targeted outreach via Apollo campaigns
• Conference presence (HFMA, AHIP)

Success Metric: 10+ qualified leads per month.

Phase 3: Repeatable Sales (H2 2025)

Objective: Standardize engagement process and pricing.

Tactics:

• Documented discovery process
• Standard SOW templates
• Case study library
• Referral program with early clients

Success Metric: 3+ concurrent engagements, $500K+ contracted.

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7. Engagement & Pricing Models

Tiered Offerings

Discovery Sprint - $15-25K

• 2-week intensive assessment
• Process mapping and opportunity sizing
• Roadmap and ROI projection
• Deliverable: Written recommendation + demo prototype

Implementation Project - $75-200K

• 2-4 month fixed scope
• Production deployment of specific capability
• Training and change management
• Deliverable: Working system + handoff documentation

Embedded FDE - $20-30K/month

• Ongoing engineering capacity
• Continuous improvement and expansion
• Strategic technology partnership
• Deliverable: Quarterly business reviews + roadmap

Hybrid: SaaS + Services

• Platform license ($250/user/month)
• Implementation services (bundled discount)
• Ongoing support retainer
• Best of both: Recurring revenue + high-touch delivery

Pricing Philosophy

• Never race to the bottom - Compete on value, not cost
• Land with services, expand with product - Mirror Palantir model
• Minimum viable contract: $50K - Smaller deals don't justify FDE model
• Target average: $150K - Sustainable for 2-person team

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8. Success Metrics & Validation

January 2025 Goals

Quarterly Health Indicators

Leading Indicators

• Pipeline value ($)
• Conversations per week
• Content engagement rates
• Warm intro requests

Lagging Indicators

• Contracted revenue
• Repeat engagement rate
• Customer satisfaction scores
• Revenue per engagement

Anti-Patterns to Watch

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9. Open Questions

These require further discussion and iteration:

1. Equity vs. Cash: Should we offer equity participation in client outcomes?
2. Partner Strategy: When do we bring in subcontractors vs. do it ourselves?
3. Geographic Focus: Remote-first or target specific regions?
4. Specialization Depth: How narrow should our healthcare focus be?
5. Exit Path: Build to sell services business, or build to scale product?

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10. Next Actions

This Week (Jan 7-13)

• Finalize SaaS product for launch
• Send Apollo campaigns to existing contacts
• Reach out to warm connections (Jose, Dr. Bauman)
• Publish first LinkedIn thought leadership piece

This Month (January)

• Land first discovery sprint
• Generate 5+ serious conversations
• Premira meeting (Jan 21)
• Refine manifesto with learnings

This Quarter (Q1)

• 2-3 paying engagements
• First case study published
• Repeatable discovery process documented
• Decision on fundraising vs. bootstrapping

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Appendix: Key Sources

See palantir-analysis.md for detailed Palantir research.

Essential Reading:

• Pragmatic Engineer: Forward Deployed Engineers
• Emergence Capital: FDEs are the Gold Miners
• SVPG: Forward Deployed Engineers

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Version 1.0 - January 7, 2025 For internal alignment. Not for external distribution.