I’m looking for specific, battle-tested workflows rather than generic “we use ChatGPT for research.” Whether you’re traditional, self-funded, partnered, or ETA/independent sponsor—please outline what you did, what it replaced, and what measurable lift you got. Where possible, add screenshots (redacted), prompts, or snippets. 1) Sourcing & Targeting Universe build: How are you using AI to expand/refine lists (NAICS/SIC, niche synonyms, adjacency mapping)? Any workflows combining web scraping + LLM entity resolution? List quality: Precision/recall before vs. after AI? Hit-rate improvement (% targets worth contacting)? Stack: Clay, Apollo, Grata, Grakn/Neo4j, custom scripts, agents? What glued them together? 2) Outreach at Scale (without sounding robotic) Personalization: Prompts/templates that consistently lift reply rates. What’s your A/B result (baseline vs. AI-assisted)? Channel mix: Email, LinkedIn, phone scripts. Any AI-assisted call prep or voicemail drafting that works? Guardrails: How do you keep tone, claims, and compliance tight? 3) First-Pass Diligence & “Kill-Fast” Triage Document digestion: CIMs, broker teasers, websites, reviews, OSHA/EPA/state filings—what’s your RAG/notebook setup? Signals: Top 5 red/green flags you’ve automated (customer concentration, maintenance capex, churn proxies, pricing power). Throughput: Hours saved per deal? Reduction in time-to-no? 4) Financial Modeling & Valuation Assumptions generation: Have you used AI to surface driver ranges (volume/mix, seasonality, freight, wage drift)? QC: Any AI checks on Excel models (error scanning, linkage tests)? Outcome: Did AI change bid discipline or IOI/LOI velocity? 5) Legal, Compliance & Risk Policy: Your non-negotiables (no PII, no upload of docs without NDAs, vendor DPAs, zero-retention modes, local inference)? Tools: Redaction, watermarking, audit trails. Any FINRA/SEC/FTC counsel inputs if relevant? Incidents: Any near-misses (hallucinated facts, mis-cited regs) and how you fixed the process? 6) Post-Close (if you’ve acquired) Operational lift: Scheduling, quoting, pricing analytics, CS call scripts, collections, routing, QA. Margins: Concrete before/after metrics (days-sales-outstanding, on-time delivery, gross margin, SLA adherence). Change management: What training and SOPs actually stuck with frontline teams? 7) Architecture & Cost Your stack diagram: Data sources → ETL → vector store/notebook → LLMs/agents → outputs. Budget tiers: What’s “good enough” at <$200/month vs. $200–$1k vs. $1k–$5k? Latency & reliability: Where did tools break under load or get rate-limited? 8) Prompts, Playbooks & Templates Your top 3 prompts (verbatim, redacted if needed) for: (a) list expansion, (b) email personalization, (c) CIM triage. SOPs: If you have a one-pager your analyst can run from day 1, please share structure. 9) Measured ROI & What You’d Do Differently KPIs that changed: Cost per qualified lead, reply rate, intro-to-IOI %, IOI-to-LOI %, LOI-to-close %, cycle time. Lessons learned: Biggest waste of time? Vendor you’d skip? Where human expertise remains irreplaceable. 10) Context (so readers can benchmark) Search model: Traditional vs. self-funded; solo vs. partnered; geography/industry focus; deal size. Stage: Pre-IOI, active IOIs, under LOI, closed operator. If you can, attach a redacted mini-case (1 page): goal → tool/flow → result → metric deltas. Happy to compile anonymized patterns and share back with the community.