Teaching Students to Evaluate Tech Startups: Case Study Pack (BigBear.ai, Holywater, The Orangery)

Hook: Turn your class's pain into practice — real startups, real trade-offs

Teachers and students juggling tight syllabi, grading load, and the need for relevant, hands-on lessons often ask: how do we give learners the tools to evaluate startups without inventing scenarios from scratch? This case study pack answers that need. It uses three 2025-2026 stories — BigBear.ai, Holywater, and The Orangery — to teach fundraising analysis, IP strategy, revenue risk, and product-market fit in a single comparative module.

Overview: What students will learn first

Start with the big picture. By the end of this module students will:

• Compare different startup financing paths and implications for control and runway.
• Evaluate IP assets and licensing strategies for media and AI firms.
• Quantify revenue concentration and regulatory risk using simple metrics.
• Assess product-market fit for disparate models: gov-facing AI, mobile-first streaming, and transmedia IP studios.
• Present a 10-minute investor memo with clear recommendation and three KPIs to monitor for 12 months.

Why these three stories matter in 2026

Late 2025 and early 2026 brought clearer economic and regulatory contours for tech startups. Governments tightened procurement standards and AI governance conversation matured, making FedRAMP and compliance meaningful signals. Streaming and vertical-video platforms saw renewed investor interest as short-form episodic content scaled globally. Meanwhile transmedia IP studios gained leverage as Hollywood and streamers sought pre-tested IP to reduce adaptation risk. These three companies embody those 2026 trends.

Quick snapshot

• BigBear.ai: Debt elimination and a FedRAMP-approved AI platform acquisition signal an attempt to reset, but declining revenue and client concentration raise revenue risk and government dependency.
• Holywater: Fresh $22M funding to scale AI-powered vertical streaming — product-market fit bet on mobile-first serialized short-form, with Fox as strategic backer.
• The Orangery: A European transmedia IP studio holding graphic-novel IP and signing with WME — focused on licensing, adaptation sits, and transmedia monetization strategies.

Comparative framework: How to evaluate startups in class

Use the following framework across all three cases. It keeps analysis consistent and teaches transferable evaluation skills.

1. Fundraising and capitalization: round size, investor type, dilution, preferred terms, and strategic value of investors.
2. IP strategy: ownership, exclusivity, scope, defensibility, and licensing runway.
3. Revenue risk: concentration, contract types (recurring vs one-time), payor mix, and regulatory dependence.
4. Product-market fit and distribution: evidence of demand, unit economics, channel strategy, and retention metrics.
5. Regulatory and operational risk: compliance requirements, supply chain/vendor risk, and geopolitical factors.

Detailed comparative analysis

1. Fundraising: runway versus strategic value

Key classroom takeaway: not all capital is equal. Teach students to score funding on cash, runway, and strategic options.

• BigBear.ai: Debt elimination is a reset move. It reduces leverage risk and may restore investor confidence. But funding that arrives via acquisition or debt restructuring can come with covenant legacies or contingent liabilities. Use questions: how many months of runway remain at current burn? How much additional working capital is needed to stabilize revenue declines?
• Holywater: A $22M round led with Fox backing provides both capital and distribution muscle. Students should evaluate whether the funds are for growth capex, content production, or M&A. Strategic investors can accelerate distribution and open licensing channels, but may also exert content direction — assess governance terms.
• The Orangery: IP studios often rely on pre-sales, agency representation, and licensing deals. Signing with WME is non-dilutive value: it increases chances for adaptation deals without immediate fundraising. Classroom task: build a cap table scenario where IP monetization funds content development versus equity capital.

2. IP strategy: ownership, scope, and monetization

IP is central to Holywater and The Orangery, and relevant to BigBear.ai for platform models and proprietary datasets. Teach students to analyze IP beyond patents: trademarks, copyrights, data licenses, and creative rights.

• BigBear.ai: For AI firms, IP often includes proprietary models, labeled datasets, and government approvals. FedRAMP approval is an operational credential that signals security and can be a competitive moat for public-sector contracts. But IP risk includes model explainability, training data provenance, and potential restrictions under evolving AI regulation. Classroom exercise: map the company's IP assets and score them on defendability.
• Holywater: Content discovery powered by AI creates a layered IP picture: user-generated contributions, studio-owned content, and algorithmic recommendations. Critical questions: who owns derivative content generated or enhanced by AI? How do licensing contracts manage creator revenue shares? Use a mock license term sheet to teach negotiation leverage.
• The Orangery: Pure-play IP studio advantages include exclusive rights to characters, sequels, and adaptation windows. WME representation increases option and adaptation probability. Students should practice valuing IP pipelines using comparables, revenue share models, and milestone-based payments common in film/TV deals.

3. Revenue risk: concentration, predictability, and seasonality

Revenue risk is where many analyses fail. Teach quantitative indicators that are easy for students to calculate.

• Key metrics to estimate: top-5 customer revenue share, % recurring revenue, gross margin on core service, and contract length.
• BigBear.ai: Government contracting often brings high revenue per client but concentration risk. Falling revenue combined with dependence on a handful of contracts increases downside sensitivity to budget cycles and procurement changes. Classroom mini-project: compute break-even if top client reduces spend by 30%.
• Holywater: For streaming startups, early revenue often comes from ads, partnerships, or content licensing. Revenue predictability improves with subscriptions or multi-year distribution deals. Students should build a 12-month revenue model comparing ad-led vs subscription-led scenarios and identify the KPI that unlocks profitability (e.g., Average Revenue Per User, ARPU).
• The Orangery: Revenue here can be lumpy — option fees, advances, royalties. Teach students to convert lumpy revenue into risk-adjusted monthly equivalents using probability-weighted pipelines and to stress-test scenarios where a major adaptation is delayed.

4. Product-market fit: evidence and signals

Product-market fit in 2026 is judged differently than in 2016. Attention economics, creator-driven trends, and AI personalization demand fresh KPIs.

• Evidence of product-market fit: retention cohorts, LTV:CAC > 3, organic virality, improving unit economics over time.
• BigBear.ai: Fit is judged by mission alignment with government needs, deployment success, and renewal rates. FedRAMP helps close procurement bottlenecks, but falling bookings means students should question whether the product matches current agency priorities.
• Holywater: Fit is mobile-native serialized content and algorithmic discovery. Look for completion rates for episodes, repeat session frequency, and creator satisfaction scores. Instruct students to design an experiment to validate whether microdramas increase retention versus longer episodes.
• The Orangery: Fit here is audience-to-adaptation conversion — do graphic novel fans translate into streaming audiences or box-office buyers? Use adaptation conversion rates and fan engagement metrics to estimate adaptation ROI.

Classroom-ready exercises and assignments

Below are modular activities you can drop into a 50-minute class or expand into a multi-week project.

Activity A: 50-minute investor memo

1. Split students into three groups; assign one company each.
2. 10 minutes: 5-minute rapid SWOT and 5-minute KPI selection.
3. 20 minutes: Build a 1-page investor memo with recommendation: invest, pass, or watchlist.
4. 15 minutes: 3-minute pitch + 2-minute Q&A from peers.

Activity B: 2-week case project

• Deliverables: 8-slide investor deck, 3-year financial model (simple), and a 600-word memo on IP risks.
• Resources: public news, SEC filings (if any), sector reports, and provided baseline templates.
• Assessment: clarity of assumptions, realism of KPIs, quality of sensitivity analysis.

Suggested grading rubric (100 points)

• Thesis clarity and recommendation: 20
• Financial plausibility and modeling: 25
• IP and regulatory analysis depth: 20
• Presentation and argumentation: 15
• Use of sources and citations: 10

Teaching notes: class prep and data sources

Prep time: 60-90 minutes. Provide students with the short news excerpts and a one-page factsheet per company. Recommended open data sources include company press releases, industry reports on short-form video and AI procurement, and film/TV licensing comparables. For most exercises a simplified financial template suffices.

• Introduce FedRAMP and why it matters for government contracting in 2026. For complementary regulatory context and cross-border considerations see EU data residency rules.
• Bring sample licensing term sheets for media deals and option agreements to class. Modern signing and contractual workflows are discussed in places such as e-signature evolution.
• Use cohort retention charts and hypothetical LTV:CAC scenarios to teach streaming economics.

Sample instructor answers and model KPIs

Provide model answers so students know expectations.

• BigBear.ai model KPIs: % revenue from top-3 clients, trailing 12-month revenue growth, renewal rate, gross margin on services, number of FedRAMP customers engaged.
• Holywater model KPIs: daily active users (DAU), completion rate for first-episode cohort, ARPU, content cost per minute, churn after 30 days.
• The Orangery model KPIs: number of option agreements, probability-adjusted revenue from adaptations, direct-to-fan merchandise sales, IP extension rate into other formats.

Discussion prompts to spark debate

• Is FedRAMP approval a sustainable moat for AI startups, or merely a procurement checkbox? What could erode its advantage?
• Does strategic capital from a media conglomerate accelerate product-market fit or create dependency? Compare Holywater with a non-strategic funded peer.
• For IP-first companies like The Orangery, when is it better to license broadly versus pursue in-house production?

Advanced strategies and 2026 predictions for students to debate

Invite learners to forecast where each company might land in 18 months. Encourage evidence-based predictions grounded in KPIs.

• Prediction 1: Companies with government-facing AI platforms that secure FedRAMP and diversify client base will attract strategic M&A interest in 2026-2027. But consolidation risk will rise as large incumbents build in-house capabilities.
• Prediction 2: Vertical streaming platforms anchored in AI-driven personalization and owned IP will pivot to hybrid monetization (ads + microtransactions + creator revenue shares) as ARPU pressure persists.
• Prediction 3: Transmedia IP studios that combine strong fan-engagement data with agency representation will command better adaptation economics and shorter time-to-deal windows. For a practical checklist on pitching IP, see the transmedia IP readiness checklist.

Practical rule for students: quantify your thesis. If your recommendation is 'invest', list three KPIs that must improve and by when. If they don't, your recommendation flips to 'pass'.

Common pitfalls and instructor warnings

• Avoid over-weighting press headlines. News can be noisy; always check primary signals like recurring revenue and renewal metrics.
• Don’t conflate strategic partnership announcements with distribution execution. A Fox backing or WME deal helps, but conversion is not guaranteed.
• Watch for hidden dependencies: government contracts often include long procurement cycles and political exposure; IP studios rely on successful adaptation marketplaces.

Extension activities and assessment variations

• Role-play negotiation: students act as startup CEO, strategic investor, and legal counsel to negotiate a content license or a distribution partnership.
• Data deep-dive: use synthetic cohort data to compute retention curves and run simple LTV sensitivity analyses. Also assign portfolio projects that map directly to streaming KPIs.
• Ethics module: discuss AI training data provenance for Holywater and BigBear.ai, and copyright attribution for The Orangery adaptations. Pair this with readings on privacy and content provenance.

Wrap-up: actionable takeaways for instructors and students

• Standardize evaluation using the five-part framework: fundraising, IP, revenue risk, product-market fit, and regulatory risk.
• Teach with current KPIs: LTV:CAC, top-customer concentration, cohort retention, and probability-adjusted licensing pipeline.
• Use strategic investor involvement as both a resource and a potential constraint in scenario planning.
• Always require students to list three specific, measurable indicators that will validate or invalidate their recommendation within 12 months.

Materials checklist for instructors

• One-page company factsheets for BigBear.ai, Holywater, The Orangery.
• Financial template with revenue scenarios and sensitivity sliders.
• Sample licensing term sheet and FedRAMP / e-sign explainer.
• Rubrics and slide templates for student deliverables.

Final thoughts and call-to-action

Comparative case studies like this one give students a rare cross-sector view: government AI platforms, AI-driven streaming, and IP-first studios each teach different lessons about value creation, risk, and timing. Use this pack to move beyond hypothetical cases and train students to make investor-grade judgments in 2026's rapidly changing landscape.

Ready to bring this pack into your classroom? Download the free instructor kit, slide templates, and student worksheets at classroom.top/casestudy-pack and schedule a live walkthrough with our editors. Adapt the scenarios, run the exercises, and share your top student presentations — we feature standout lessons and give feedback to help scale classroom impact.

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