Pain Point #1: “Evaluation… is clunky, manual, and time-consuming. Most teams… end up writing prompts, datasets, and rubrics by hand, spending hours setting up tests just to compare models, and redoing everything every time the product changes.”

Opportunity: Auto-Evals From Your Repo (zero-prompt, CI-gated LLM evals)
- A tool that connects to your GitHub and product, infers user-facing capabilities from code/routes/tools, and auto-generates a domain-specific eval suite (prompts, fixtures, rubrics, LLM-as-judge configs) with pass/fail gates in CI. It continuously updates evals as your product surface changes (PR hooks that add/retire tests), and converts real support tickets/analytics into regression cases automatically. Includes “kill switch” deploy block if key metrics drop. 
- Pricing: Starter $499/mo (1 repo, 2 suites), Growth $1,999/mo (up to 5 repos, 10 suites), Enterprise $5k+/mo (SSO, VPC, custom rubrics, SOC2).

First 10 Customers:
- Head of AI/ML at 10–200 person AI product companies (agent builders, copilots, chatops)
- Product Lead for LLM features at B2B SaaS (support bots, doc assistants, sales assistants)
- MLE/Platform Lead at YC/Techstars AI startups iterating weekly
- CTOs at vertical AI apps (legal, healthcare, fintech) who need auditable metrics
- QA/ML Ops leads standing up LLM CI for multi-model routing

MVP in 48 Hours:
- Webflow landing + GitHub OAuth + Typeform to capture product description and endpoints
- Behind the scenes: analyze repo/routes/readme; manually craft eval YAML (OpenAI Evals/Gentrace/HoneyHive format) + a GitHub Actions workflow that runs nightly and on PR
- Store evals in Airtable; output a Looker Studio dashboard; run 2 pilot repos manually
- Ship a PR comment bot that posts pass/fail and blocks merge if thresholds fall

Justification:
- Demand: “evaluation… clunky, manual… writing prompts, datasets, and rubrics by hand… redoing everything every time the product changes.”
- ROI:
  - Teams easily burn 6–12 eng-hours/week on eval maintenance ($600–$1,200/wk at $100/hr), plus costly regressions. Payback <1 month at $499–$1,999/mo.
  - Faster model swaps when providers change quality/price weekly; prevents shipping worse outputs.
- Scalable:
  - Templated rubrics by vertical; self-serve GitHub app; multi-tenant + usage-based compute.
  - Add-ons: autorepair of failing cases, judge calibration, multi-model routing comparisons.
  - $1M ARR = ~50 Growth-tier accounts or ~17 Enterprise; small, high-ARPU base.
- Purple Cow/Controversial:
  - “Zero prompt writing” claim: it mines your code and real user flows to author evals.
  - Opinionated CI gate that can block releases—bold but what serious teams want now.
  - Unfair advantage: turns your own tickets/analytics into continuously renewing regression tests.

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Pain Point #2: “I’ve seen a lot of people have issues with their small businesses getting flagged for something then having their payouts paused… I don’t want to run into any issues after getting setup.” (Post 36)

Opportunity: FailoverPay — payout-hold insurance + multi-processor routing for SMBs
- One checkout, multiple processors behind it (Stripe, PayPal, Square, Adyen Lite). If one flags/holds, instant failover to another pre-approved PSP with tokenized cards; daily auto-sweeps to your bank.
- “HoldShield” risk scoring to warn about MCC/policy triggers; playbooks to lower chargebacks and reserves; optional micro‑advance against held funds.
- Stripe/Shopify “shadow mode” that mirrors charges as $0 auths on your backup PSP for 30 days to ensure readiness.
- Pricing: $99–$299/month + 0.35–0.6% of processed volume; optional advances at 1–1.5%/week while funds are held.

First 10 Customers:
- Shopify/Woo owners in quasi‑risk categories (custom merch, event tickets, supplements, dropship) doing $30k–$300k/month
- CFO/COO of DTC brands burned by a prior hold
- Agencies handling multi-client checkouts who need redundancy

MVP in 48 Hours:
- Landing page + waitlist; manual concierge setup as “Phase 1”
- For first pilots, configure backup PSP accounts, alternate payment buttons, and invoicing rails; add a “break‑glass” payment link on order confirmation if primary PSP pauses
- Airtable to track processor status + alerts; publish a “Payout Hold Playbook” PDF; run initial risk reviews as a service

Justification:
- Demand: “getting flagged… payouts paused” (Post 36). Cash-flow freezes kill small brands overnight—extreme pain, growing in 2024–2025.
- ROI: Avoiding a single 14–30 day payout freeze on $50k–$200k sales can save payroll, ad momentum, and chargeback spirals; redundancy is cheap insurance.
- Scalable: Productize into a lightweight router + token vault over time; standardized onboarding by MCC; upsell risk tools and working-capital advances.
- Purple Cow/Controversial: SMB-grade routing and “shadow mode” readiness typically reserved for enterprise; bundling micro‑advances against holds is rare and highly valued.

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Pain Point #3: “prospects are turning away when he talks about building AI agents.” + “Customers don’t want creativity, they want consistency.” + “QA, unglamorous but relentless, was the only real driver of reliability.” + “I built a system… It works extremely well. But adoption is low.”

Opportunity: Agent GroundControl: the reliability layer for AI automations with SLAs. You don’t “sell agents”—you sell guaranteed workflows.
- Domain-grounded playbooks (support macros, pre‑sales research, trade infringement detection).
- Acceptance Test Suite (ATS) for each workflow: gold datasets, precision/recall targets, guardrails, failover-to-human.
- Live telemetry, drift detection, audit trails; weekly QA reports tied to business KPIs.
- Outcome guarantees: “95% accuracy on ATS or you don’t pay.”
Pricing: $15k 30‑day pilot per workflow, then $5k–$20k/month per team/workflow; performance bonuses on AHT reduction or qualified leads per seat.

First 10 Customers:
- Head of Customer Support at 50–300 seat ecommerce orgs on Zendesk/Intercom (target macros + deflection).
- RevOps leaders at B2B SaaS (10–100 sellers) for pre‑call research and account intelligence.
- Brand Protection/Legal at marketplaces/CPG for trade/IP infringement detection.
- CTO/Founder at 3–20 person AI consultancies stalling on adoption; white‑label GroundControl to close deals.
- Product leaders at dev agencies offering “AI add‑ons” that clients don’t trust yet.

MVP in 48 Hours:
- Pick one high-value boring workflow (e.g., trade infringement detection or pre‑sales research).
- Build a tiny pipeline in Python + OpenAI + a vector store; create a 100–200 case ATS with labeled pass/fail.
- Orchestrate in n8n/Make; log all outputs to Airtable; add a human‑in‑the‑loop step for fails.
- Deliver a pilot to one design partner: baseline vs. GroundControl metrics; commit to an SLA in writing.

Justification:
- Demand: “prospects are turning away when he talks about building AI agents.” “Customers don’t want creativity, they want consistency.” “QA… was the only real driver of reliability.” “It works extremely well. But adoption is low.”
- ROI: Support: 20–40% macro automation with <5% error → 2–4 FTE savings per 50 agents; Pre‑sales: 30–60 minutes saved per opp per rep → incremental pipeline; Brand protection: reduce false negatives that cost 5–6 figures/month.
- Scalable: Verticalized playbooks + reusable ATS + orchestration templates. Each added workflow is a SKU; land-and-expand across teams. White‑label to agencies accelerates distribution.
- Purple Cow/Controversial: Selling SLAs, not “AI magic.” Money‑back if ATS not hit. Contrarian to agent hype; wins trust in a bubble/backlash moment.