FAQ Appendix

FAQ

Q1. What problem are we solving first and how soon do we validate it?

A. Unlock SMB/franchise demand with self-serve onboarding, instant quotes/booking, and fast payouts. We validate in 0–3 months via a closed alpha in 2 metros with seeded supply and clear stage gates (liquidity, onboarding/payout timings, leakage, conversion).

Q2. How do we measure progress without existing baselines?

A. Foundation phase instruments baselines (MTTR, first-time fix, time-to-fill, onboarding/payout times, leakage, CSAT). We track deltas versus baseline and use phase stage gates to decide scale-up.

Q3. How does this drive top-line growth and margin expansion?

A. Top-line: opens SMB/franchise GMV with minutes-long sales cycles. Margin: automates onboarding, billing, reconciliation, and optimizes pricing/take-rate while improving provider earnings through routing/batching.

Q4. Will this cannibalize enterprise?

A. We fence it: distinct pricing and SLAs, separate catalogs and entitlements, ring-fenced ops, and a graduation path. Enterprise retains dedicated coverage and guarantees; SMB prioritizes speed and transparency.

Q5. What are the first investments we must make?

A. KYC/KYB and payments/payout stack, self-serve onboarding v1 with doc auto-extraction, instant quote MVP, supply seeding in 2 metros, anti-leakage controls, and KPI instrumentation.

Q6. How will pricing and take-rate be set?

A. Price guidance by category/region/time, with controlled experiments to balance fill-rate and contribution margin. Take-rate sits in a target band; route/batch tools and fast payouts sustain provider economics.

Q7. How do we ensure provider quality and safety at scale?

A. Tiering (Starter→Pro→Elite), continuous verification, safety/brand modules, predictive quality/risk scores feeding routing and incentives, and dispute/mediation workflows.

Q8. How do we prevent fraud and off-platform leakage?

A. Vendor-verified identity and payments, escrow/holdbacks, anomaly detection, in-app comms, masked contacts, QR/job codes, clear T&Cs and clawbacks.

Q9. How are payouts handled?

A. Instant/next-day payouts with compliance; split payments for parts/labor; reconciliation and chargeback handling; transparent earnings and fees.

Q10. What if pilots underperform?

A. Stage gates drive decisions. If targets miss, iterate features/signals, narrow scope, or pause scale. Maintain manual overrides and continuous A/B testing to de-risk.

Q11. How do franchisors fit alongside franchisees?

A. Franchisors get integrations (asset models, brand standards, reporting) and SLAs; franchisees get self-serve onboarding and negotiated pricing tiers. Shared intelligence benefits both.

Q12. Build vs. buy?

A. Buy commoditized components (LLMs, identity, payments, monitoring); build differentiators (matching, provider scoring, domain ontology, pricing optimization); partner where speed matters.

Q13. Where do we roll out first?

A. Start with 1–2 categories in 2 metros using the beachhead framework (e.g., QSR beverage equipment, refrigeration). Expand when liquidity/SLO and unit economics hit targets, then add categories/geos.

Q14. How do we drive provider adoption in a low-tech industry?

A. Keep it simple and valuable:

• Work, not portals: steady jobs without sales/marketing burden.
• Fast money: instant/next-day payouts and transparent earnings.
• Low-friction app: tap-to-accept, photo upload, auto-checklists; minimal paperwork.
• Predictable routes: batching and nearby jobs to maximize earnings/hour.
• Fewer hassles: parts/compliance prompts, in-app support, and dispute protection.
• Earn more by performing: tiered entitlements and priority access for reliable providers.

Q15. Will you integrate with provider systems like ServiceTitan?

A. Phased approach:

• Early (alpha/beta): Keep flows simple in our app (check-in/out, photos, proof-of-work, payouts) to ensure data quality and prevent leakage; offer basic exports.
• Mid-term: Lightweight connectors for job ingest/export, schedule updates, invoice/payout reconciliation, and document sync (via webhooks/Zapier/API).
• Long-term: Certified integrations with leading FSM/ERP tools, with entitlements, field mapping, and audit logs. Principle: don’t duplicate their workflows; integrate where it reduces friction without sacrificing trust/safety or platform economics.

Q16. Should we hold funds (offer a wallet) to capture float or enable instant payouts?

A. Near-term, use provider instant payouts via payment partners (e.g., debit push/RTP) and weekly ACH (free). Wallets/stored value introduce money transmission, safeguarding, reconciliation, escheatment, KYC/AML, and audit requirements. If pursued later, do it with a sponsor bank and program manager using FBO accounts; any yield/float sharing is contractual and typically offsets program costs, not a core profit engine. Better near-term monetization: transparent instant payout fees and early-pay discounts.

Q17. How do we quote when the customer can’t diagnose the issue?

A. Use estimate ranges and a diagnostic + NTE flow: show expected bands by job type/geo/SLA and what’s included; collect photos/video to improve accuracy; schedule a diagnostic visit with a not-to-exceed cap; any scope expansion requires in-app approval before work.

Q18. How do we prevent price gouging or bait-and-switch?

A. Publish rate guardrails and NTE policies; auto-flag outlier quotes/invoices; require photo proof-of-work and parts validation; enforce conduct via tiering and suspensions for violations; protect customers with dispute mediation and quality guarantees.

Q19. What exactly is new/different versus existing FM platforms?

A. Three core differentiators: (1) Instant quote → booking for standardized commercial equipment, not just ticket routing and callbacks; (2) AI policy rails (estimate ranges, NTE approval workflows, outlier detection, proof-of-work) that prevent surprise invoices and scope creep; (3) Provider economics that actually work: instant/next-day payouts, nearby job batching, transparent fees that preserve provider earnings while reducing hassle.

Q20. How does the AI work throughout the job lifecycle, and what are the guardrails?

A. Before the job:

• Photo/video intake → structured work order
• AI estimates by job type/geography/SLA
• Diagnostic workflows with NTE when scope is unknown

During the job:

• SLA-aware matching (urgent jobs → nearby fast providers)
• Travel-time minimization and route batching
• Site history and equipment playbooks

After the job:

• Outlier flagging on price/time anomalies
• Proof-of-work validation (photos, parts receipts)
• Automated invoicing/tax/payouts

Guardrails:

• Explainable routing/pricing features (no black-box)
• Hard NTE enforcement on scope changes
• Human review for safety/fraud/outliers
• Full audit logs

Q21. How does data compound into a durable competitive moat?

A. Every job produces structured data:

• Issue type, asset details, failure mode
• Travel time, parts used, labor time
• Resolution approach, quality outcome
• Customer/provider satisfaction

That data improves four critical systems:

• Estimate accuracy: Better pricing → higher conversion, fewer disputes
• Routing intelligence: Right tech, right time, right location → faster fixes
• Quality prediction: Identify high performers, coach/remove poor ones
• Risk models: Fraud detection, outlier flagging, anomaly prevention

The Flywheel: Better data → lower friction/cost → more usage → more/better data → stronger moat.

It’s a self-reinforcing loop tied to real outcomes (MTTR, first-time fix, NPS, disputes), not just clicks. The more jobs, the smarter it gets, the harder to replicate.

Q22. What are the detailed alpha metrics, stage gates, and decision mechanisms?

A. Los Angeles QSR F&B pilot with weekly WBR tracking:

Core Metrics:

• Liquidity: ≥70% jobs filled ≤30 min
• Payout speed: P95 ≤24h (target ≤6h instant)
• Conversion: quote→book ≥35%
• Leakage: ≤3%
• Disputes: median ≤3 days
• MTTR: ≤14h median, first-time fix +3–5 pts vs. baseline

Decision Rule: If any SLO breaches 2 consecutive weeks → rollback/hold/change and re-evaluate before expanding.

Weekly WBR Tracks Seven Leading Inputs:

1. Liquidity (fill rate, time-to-match)
2. Quote→book conversion
3. Travel time per job
4. Payout P95 and instant adoption
5. Leakage rate (to competitors/direct)
6. Estimate accuracy (quote vs. invoice delta)
7. Dispute rate and resolution time

Decision Framework: Roll forward, hold/iterate, or rollback based on trends and stage-gate performance.