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Zapata AI SPAC Financial Model

• Two core products sold under the Orquestra® platform: Slide 4, 18, 19
• Zapata AI Prose™ - LLM compression and fine-tuning for enterprise text applications; compressed models require 300x fewer tokens to match uncompressed model performance Slide 13.
• Zapata AI Sense™ - Quantum-inspired generative models for complex numerical/mathematical problems (optimization, simulation, derivative pricing).
• Key performance claims (all from internal Zapata data):
• 8,400x speedup vs Monte Carlo simulation for multi-asset options pricing Slide 14.
• Up to 10x–1,000x speed-up on LLMs and other large models Slide 26.
• Compressed GPT2-XL outperforms GPT2-Small (same model size) in F1 accuracy Slide 13.
• GEO (Generator Enhanced Optimization) tied or outperformed state-of-the-art solvers in 71% of configurations for BMW manufacturing scheduling Slide 17.
• Platform: Orquestra® is hardware- and cloud-agnostic; supports CPU/GPU/TPU/QPU; integrates with AWS, Azure, GCP, IBM Cloud, and on-prem. Slide 18.

• TAM: $1.3T (Generative AI Software and adjacencies by 2032) Slide 21 - source: Bloomberg Intelligence, June 2023.
• SOM: $366B by 2032 ($280B Generative AI Software + $86B Generative AI IT Services) Slide 21.
• McKinsey estimate: 63 Gen AI use cases across 16 business functions could deliver $2.6T–$4.4T annual P&L impact for enterprise Slide 21.
• Key verticals targeted: Automotive, Chemicals & Materials, Defense, Energy & Utilities, Finance, Logistics, Pharma Slide 23.

• Model: Recurring subscription; contracts recognized ratably. Slide 22.
• Contract length: 6+ month agreements with intent for multi-year subscriptions. Slide 22.
• Bundled offering: software license + scientific algorithm expertise (not pure SaaS; includes professional services component). Slide 22.
• Two sales motions: Slide 22

1. Direct - C-level relationships, global sales force.
2. Partner ecosystem - consulting firms (Top 5 Global Consultancy), cloud/software (Microsoft Azure, NVIDIA), hardware (IBM, IonQ), academia (MIT, University of Toronto).

• Land-and-expand: initial application foothold → expand ARPA (Average Revenue Per Account). Slide 22.

• Customers (named): Andretti Autosport, BASF, BBVA, bp, DARPA, top 5 food & bev company (unnamed), leading Clinical Research Organization (unnamed). Slides 4, 20.
• Partners (named): AWS, NVIDIA, IBM, IonQ, Microsoft Azure, top 5 global consultancy (unnamed). Slide 4.
• Total funding raised to date: $64M Slide 25.
• Headcount (as of Aug 28, 2023): 60 employees total; 41 scientists & engineers; 24 PhDs. Slide 25.
• IP: 50 US patent families & applications (100+ worldwide as of June 2023); ranked #5 most active in quantum-inspired AI patents (2020–2021, EPO). Slide 12.
• Revenue, ARR, growth rates, customer count, churn, NRR: Not in deck.
• DARPA: 2 awards to benchmark utility of quantum computing. Slide 20.
• Gartner Cool Vendor recognition. Slide 23.

• Moat framing: Slides 9, 11, 12, 19
• Proprietary quantum-inspired generative AI algorithms (not reliant on quantum hardware - works on classical hardware today).
• Globally ranked IP portfolio: #5 in quantum-inspired AI patents 2020–2021; portfolio mix: Algorithms & Software 38%, Generative AI 30%, Differential Equations & Optimization 22%, Hardware Optimization 10%.
• Founded by Harvard quantum computing PhDs; 24 PhDs on staff; ~85K total academic citations (includes founder Alan Aspuru-Guzik's ~57.5K citations).
• Hardware/cloud agnostic platform vs. Big Tech's locked-in, one-size-fits-all LLMs.
• Named competitive positioning: positioned against OpenAI/ChatGPT-style general-purpose LLMs as "Industrial" specialist. Slide 9.
• Direct competitors: Not named in deck.

Zapata AI leadership: Slide 25

• Christopher Savoie, Ph.D. - CEO; inventor of NLU behind Apple's Siri; formerly Nissan, Verizon.
• Yudong Cao, Ph.D. - CTO; 10 years AI & quantum; 30 patents; 2.4K+ citations; Harvard/Purdue.
• Mimi Flanagan - CFO; IHS Markit, Jenzabar, Qlik, Wyndham.
• Tim Stanley - VP Global Sales; 25+ years enterprise sales.
• Nicole Fitchpatric - General Counsel.

Andretti Acquisition Corp. (SPAC) leadership: Slide 5

• Bill Sandbrook (Chairman & Co-CEO): Former Chairman/CEO U.S. Concrete (25x market cap, 24x EBITDA, 35+ acquisitions).
• Michael Andretti (Co-CEO): IndyCar Champion, Founder/CEO Andretti Autosport.
• Matt Brown (President & CFO): Former CEO Rocky Mountain Industrials, former CFO Forterra, former CFO U.S. Concrete.

SPAC: Andretti Acquisition Corp. raised $230M oversubscribed SPAC IPO (NYSE: WNNR). Slide 5.

Transaction / Use of Funds: Slide 24

• Sources: Cash in Trust $50M + Bridge Financing $20M + Zapata Rollover $200M = $270M total.
• Uses: Cash to Balance Sheet $48M + Transaction Expenses $12M + Zapata Rollover $200M + Growth Capital $10M = $270M.
• $48M going to balance sheet (operating runway).
• $10M earmarked as "Growth Capital."
• Bridge: convertible notes at 15% discount to DeSPAC; up to $14.5M new + $5.5M existing Zapata convertible debt.

Investors (pre-SPAC): Ahren, Alumni Ventures, BASF, Bosch, Comcast Ventures, The Engine, Honeywell, ITOCHU, Merck, Pillar, Pitango, Prelude Ventures. $64M total raised. Slide 25.

• Archetype + why: SPAC / de-SPAC deal model - not an operating forecast model. This deck is a de-SPAC investor presentation, not a growth-stage pitch. The appropriate financial model is a SPAC / de-SPAC transaction model covering: (1) sources & uses and pro-forma capitalization, (2) dilution waterfall across redemption scenarios, (3) post-close balance sheet bridge, and (4) a lightweight operating scenario bridge (revenue ramp to illustrate when the company could hit key milestones). If the goal is an operating model for Zapata AI as a standalone company, a B2B SaaS / enterprise software ARR model is the secondary recommendation - but all revenue numbers would be assumed, as the deck discloses zero actual financials.

• Forecast horizon & granularity:
• Deal model: point-in-time (at close) + 1-year post-close sensitivity.
• Operating bridge (if built): Annual, 5 years (2024–2028), monthly for Year 1.

• Key drivers & assumptions:

*SPAC deal model:*

• SPAC trust size: $84.2M (7.9M shares × $10.66/share) Slide 24 note.
• Assumed redemption rate: 40% base case Slide 24 note → $50M cash in trust after redemption.
• Bridge financing: $20M convertible note at 15% discount to DeSPAC price Slide 24.
• Zapata pre-money equity value: $200M Slide 24.
• Pro-forma equity value: $331M Slide 24.
• Pro-forma enterprise value: $283M ($331M equity − $48M cash + $0 debt) Slide 24.
• Redemption sensitivity: 0%, 20%, 40%, 60%, 80%, 100% - varies cash to balance sheet and public shareholder %
• Founder share earnout / promote: 5.8M shares (17.5%) - no earnout condition disclosed; modeled as fully vested at close.
• Warrant dilution: excluded per slide note; model should add a warrant schedule.

*Operating bridge (supplementary):*

• Initial ARR / revenue: $0–$5M at close (pre-revenue scale; no figures disclosed); set as open variable.
• ARR growth rate: 100%+ YoY early years, decelerating to 50% by Year 3, given enterprise AI category growth and land-expand model.
• Average contract value (ACV): $500K–$2M based on enterprise C-level direct sales and 6+ month contracts; no data in deck.
• Gross margin: 60–70% - typical for enterprise AI/SaaS with significant scientific services component; lower than pure SaaS.
• R&D / S&M / G&A as % of revenue: heavy burn pre-revenue; model with headcount-based opex build (60 FTEs × avg fully-loaded cost).
• Cash runway: $48M balance sheet post-close Slide 24; 24–36 months at current burn, flex variable.
• Customer count: 7–10 active paying customers at close (based on named accounts).
• NRR / expansion: 110–130% - land-expand model stated; no data.

• Scenarios (Base / Bull / Bear - which variables flex):
• Bear: 80% redemption (less cash), slow revenue ramp ($3M ARR Year 1), high burn, Series A needed in 18 months.
• Base: 40% redemption ($48M post-close cash), $5M ARR Year 1, 24-month runway.
• Bull: 10% redemption (more cash), fast enterprise wins ($10M ARR Year 1), gross margins above 70%.

• Required sheets / outputs:

1. Transaction Summary - Sources & Uses, Pro-Forma Cap Table (Zapata / Public / Founder / Bridge breakdown).
2. Redemption Sensitivity Table - cash to balance sheet, pro-forma equity value, and ownership % by redemption level (0–100%, 10% steps).
3. Dilution Schedule - warrants, bridge conversion at DeSPAC, PIK interest on bridge note.
4. Pro-Forma Balance Sheet (at close).
5. Operating Bridge (assumptions-driven) - ARR build, opex by function, EBITDA, cash burn, runway.
6. Valuation Bridge - EV/ARR implied at various revenue scenarios.