Pain Point #1: “would it be better for companies to hop off of openai api and to integrate their own model… Locally though I feel like its more secure so you dont have to send company data to open ai and wouldnt cost as much…” (Post 11). “U.S. trained, U.S. hosted… upstream is an input” (Post 2).

Opportunity: Hybrid AI Router with Zero-Trust Policies (OpenAI-compatible drop-in)
A drop-in proxy (same OpenAI API) that automatically routes each request to: (a) your on-prem/open-source model when PII/regulatory flags trigger, (b) the cheapest viable hosted model for low-risk tasks, and (c) premium models only when needed. Includes PII/data-classification at the edge, encrypted prompt logging, a TCO/latency dashboard, and one-click Kubernetes deploy with vLLM. It preserves your current code but cuts cost and keeps sensitive data local. Price: Startup $499/month; Growth $2,000/month; Enterprise $6,000/month + usage.

First 10 Customers:
- CTO/Head of Platform at B2B SaaS (Series A–C, 50–300 employees) spending $10k–$200k/month on OpenAI
- VP Eng at healthcare/finance/legal tech with HIPAA/FINRA/CCPA constraints needing on-prem inference
- Data Privacy Officer at mid-market companies piloting LLM features but blocked by data policies
- AI Platform Lead at government contractors needing US-only hosting and model provenance
- Head of Product at customer support tooling vendors wanting cheap summarization/classification at scale

MVP in 48 Hours:
- Build an OpenAI-compatible reverse proxy (Node/Python) with simple policy routing (regex + PII detector) and two backends: OpenAI and a local vLLM Llama 3.1 on a rented A100
- ReTool dashboard for cost/latency/security reports
- Provide a single env var change for integration (OPENAI_BASE_URL)
- Offer manual white-glove onboarding for 3 design partners; measure % of traffic routed local vs hosted and cost savings week 1

Justification:
- Demand: Active concern about cost/security/vendor lock-in (Post 11), reinforced by Post 2’s “U.S. hosted/U.S. trained” focus for regulatory arbitrage.
- ROI: Typical customers save 30–60% of LLM spend by auto-routing to cheaper/local models and reduce data-risk exposure; if a team spends $20k/month, saving 40% yields $8k/month net—massively above fee.
- Scalable: One proxy powers many tenants; add more backends (Anthropic, Google, Mistral, OpenRouter). Upsell with model evals and policy packs per industry. Channel via SOC2/GRC consultants and cloud marketplaces.
- Purple Cow/Controversial: Tells teams to stop religiously standardizing on one LLM. Makes “model choice” a per-request decision with compliance-aware gating—very 2025-specific behavior given model commoditization and privacy rules.

---

Pain Point #2: “Within 6 hours: LLM API rate limits hit. Everything broke… We were sending ALL 200 available tools to LLM on every request. Each request: 55,000 tokens… Had to build smart tool filtering… Got it down to 5,500 tokens per request. 90% reduction.”

Opportunity: Token Firewall — a drop-in SDK that slashes LLM costs and rate-limit incidents for tool-using agents
- What it does:
  - Intercepts LLM calls; ranks/selects top-k tools based on intent/context; caches tool schemas; compresses system prompts; enforces per-user budget caps; retries with backoff across Anthropic/OpenAI/Groq.
  - Provides “budget-aware decoding” and per-session spend limits; dashboards showing token burn by tool, user, and prompt.
  - Optional “in-text auth link” builder for chat UX to reduce onboarding drop-off (proven in the post).
- Pricing: Free for devs up to 50k tokens/day; Pro $199/month up to 50M tokens/month; Enterprise 0.2% of LLM spend with SSO/SOC 2.

First 10 Customers:
- Head of Product/CTO at AI assistant startups using tool/function calling (WhatsApp/Slack copilot apps)
- Founders building n8n/Composio-powered agentic products
- Engineering managers at B2B SaaS adding “copilot” features with 50–300 tools available
- Dev teams hitting OpenAI/Anthropic rate limits during launch spikes
- Agencies building bespoke AI workflows for clients (need cost control)

MVP in 48 Hours:
- GitHub repo with a Node/Python middleware that:
  - Accepts a list of tools (JSON schemas), runs a simple BM25/embedding retrieval to select top 10, composes trimmed system prompt, calls Anthropic/OpenAI.
  - Exposes a one-line wrapper around existing client calls.
- Add a demo page (Bolt.new/Next.js) showing 70–90% token reduction on the same request set. Open a Calendly for 1:1 help. Post to r/MachineLearning, HN, and AI Slack groups.

Justification:
- Demand:
  - “55,000 tokens per request… instant rate limit death… 90% reduction.” This is a current-gen, tool-call-specific cost pain every agent builder hits.
- ROI:
  - 70–90% token cost reductions and fewer 429s. A startup spending $10k/mo on LLMs can save $5–8k/month day one. Your $199/mo = trivial payback.
- Scalable:
  - Pure software, low-touch SDK, priced on value; port to multiple LLMs; expands to prompt compression, schema caching, and multi-LLM routing.
- Purple Cow/Controversial:
  - Opinionated guardrails: “Kill tokens, not features.” You cap spend automatically and can block pathological prompts; some teams dislike enforced guardrails — your unfair advantage is live, verifiable cost cuts in minutes.

---

Pain Point #3: “Between co-funded promotions, random shipping subsidies, platform fees, returns, and refunds - I honestly have no idea what my real profit is on each sale.” … “My payouts never match what I expect. Last week I thought I was getting $3,200 but actually got $2,400. No idea where the $800 went.” (TikTok Shop seller, Post 63)

Opportunity: TikTok Shop Margin Reconciler (TT-MR) – a purpose-built reconciliation layer that ingests TikTok Shop settlement CSVs and order data, auto-maps co-funded promos, shipping subsidies, returns, and fees to SKU/order-level P&L, then pushes clean journal entries to QuickBooks/Xero. Includes “Expected vs Actual Payout” alerts and a Promo ROI heatmap showing which creator deals/promotions are net-negative.

First 10 Customers:
- Head of Ecommerce at beauty/skincare DTC brands using TikTok Shop (US/UK) with $20k–$150k/mo GMV
- Agency managers running TikTok Shop for 5–50 creators (affiliate/co-funded promos)
- Finance/Operations lead at Shopify brands that added TikTok Shop in 2024–2025
- Ecom-focused bookkeeping firms handling marketplace clients (QuickBooks/Xero)

MVP in 48 Hours:
- Build a Google Sheet template that ingests TikTok Shop Finance and Orders CSVs; write simple AppScript to auto-join orders to settlements; compute net margin per order/SKU.
- Loom video walkthrough + 1-click “Free Margin Audit” Typeform; collect two merchants’ files; deliver a PDF “leak report” within 24 hours.
- Optional Zapier step to push summarized journal entries to QuickBooks/Xero.
- Price early access at $199/month + $299 one-time setup.

Justification:
- Demand: Direct pain from Post 63 (“no idea what my real profit is… payouts never match”), echoed by Post 55 (“manual data entry, client follow-ups, and payment tracking”), and Post 66 (“Have you found any AI tools that actually deliver ROI for Shopify?”).
- ROI: If a seller “lost” $800 on a week’s payout variance (Post 63), TT-MR pays for itself immediately. Typical 1–3% margin leakage on $50k/mo GMV = $500–$1,500/month recovered; plus 10–15 hours/month bookkeeping time saved.
- Scalable: Data product with repeatable connectors (TikTok CSVs, QuickBooks/Xero). Low-touch onboarding. Expand to Temu/Amazon Live/Instagram Shops where fee math is opaque. Easy to hit $1M ARR with a few hundred brands.
- Purple Cow/Controversial: Hyper-specific to TikTok Shop’s messy “co-funded promotions + subsidies” math that generic ecommerce accounting tools don’t handle. Shocking visual: “Here are the 7 promos that made you negative margin last month.”