Use agentic AI to target hidden profit in your $50M–$500M business and unlock EBITDA expansion.

SEAS serves two primary audiences — mid-market companies ($50M–$500M revenue) and the PE firms that back portfolio companies in that range — plus the consultants, fractional CFOs and COOs, and advisors who serve them. If you're accountable for EBITDA, or responsible for making AI work in a mid-market operating context, SEAS is for you.

No. SEAS is designed for operating executives and advisors, not engineers. The agent library uses plain-language prompts. The runbooks assume standard business tools (ERP, CRM, Excel). No coding required.

A consulting engagement costs $100K–$500K, takes 3–9 months, requires significant internal bandwidth, and ends when the project ends. SEAS costs $2,997, deploys in weeks, requires no internal project management overhead, and remains in your toolkit permanently. Many consultants buy SEAS to deploy with their own clients.

SEAS complements existing AI adoption. The FLOAT Diagnostic identifies whether your current tools are generating measurable EBITDA recovery or just operational activity. Most companies discover significant leakage persists despite prior AI spend.

The 35-agent library includes 26 industry-specific agents across 13 verticals, covering the most common mid-market and PE-backed company categories: manufacturing, healthcare services, B2B SaaS, distribution, professional services, and more.

MIT's finding wasn't about weak models or small budgets — the failures came from a "learning gap": programs run as technology projects, with no financial baseline, no owner accountable for recovered dollars, and no gate tying each phase to a measured result. SEAS is built around the inverse: baseline first, one gated pilot, hard thresholds at week 8, no scaling until the gate clears. A playbook cannot execute for you — your team still does the work — which is exactly why the guarantee covers the full 90-day deployment window. The same study answers the build-it-ourselves question: companies that bought AI tools from specialized vendors or built them through partnerships reached successful deployment about 67 percent of the time, while internal builds succeeded about a third as often. In the report's own words, external partnerships see twice the success rate of internal builds (Fortune, August 2025; report PDF). Buying a built system and running it through a gate is the approach the evidence favors.

A complete implementation suite of 32 files, and one honest distinction. What ships: five volumes (the 309-page playbook, the Strategic Agents Library, the Agent Prompts Library, the Templates Library and the RAPID SEAS execution manual), nine live Excel workbooks holding 1,711 formulas, two fill-in board and fund-level presentation decks, nine sample datasets, the Project Manager's Runbook and the reference guides, and one installed application, EBITDA PathFinder Pro, which runs offline on your own machine. The 35 agents run as working prototypes inside Claude, ChatGPT or Gemini within 15 minutes of pasting a prompt and your CSV export. For production deployment inside your own systems, each agent's specification and prompt are the build package your team or vendor implements. There is no hosted platform and nothing to subscribe to.

AI washing is the rebranding of ordinary software, chatbots or robotic process automation as "agentic AI", and it is now a named enforcement target: the Federal Trade Commission’s Operation AI Comply (September 2024) and the Securities and Exchange Commission’s actions in 2024 and 2025 treat exaggerated AI claims as deceptive practice, and Gartner estimates that only about 130 of the thousands of self-described agentic AI vendors are genuine. SEAS handles it in four ways. First, the box is inventoried in the answer above: 32 files, one offline application, and agents that run as prompts inside Claude, ChatGPT or Gemini on your own data. I do not claim installed autonomous software, and the frameworks I cover (NIST AI RMF, SOC 2, ISO 42001, the EU AI Act) are covered as content, not held as vendor certifications. Second, every figure on this page is labeled modeled or illustrative and traced to the workbook that produced it; nothing here is presented as a client result. Third, the suite protects you from other vendors’ AI washing: the vendor selection matrix scores agentic maturity ("not RPA, not a chatbot") at 25 percent weight, the vendor SLA addendum ties refunds of 100, 50 and 25 percent to measured accuracy, and the week-8 gate stops anything that has not proven itself on your own transactions. Fourth, you can inspect before you pay (the Read Me First, the Comparative Analysis, the worked case and the sample data are free) and refund within 90 days through Polar, the merchant of record, not through me.

90 days, 100%, no questions asked — matched to the full 90-day deployment timeline, so the guarantee never expires before the work is done. One email and the refund is processed through Polar to your original payment method. Upon refund, your license terminates and all copies must be deleted. Full terms at /terms.html.

Instant digital download via Polar. All components are delivered as PDF and Excel files, immediately accessible after purchase. No waiting, no onboarding call, no scheduling required to buy. A 30-minute verification call is available beforehand if you want one.

The hero band names the two buyers SEAS was designed around. The system is used well beyond them, because every part of it is portable: a diagnostic that runs on a trailing twelve-month profit and loss statement, agent specifications with production prompts, nine live workbooks, and a gated deployment method.

Operating roles: chief executive officers, chief operating officers and owner-operators at mid-market companies; portfolio-company chief executive, financial and operating officers; heads of finance and vice presidents of finance; vice presidents and directors of operations; heads of transformation; chief strategy officers; directors of innovation; project management office directors; and process excellence and Lean Six Sigma leads.

Independent practitioners: fractional chief financial, operating and artificial intelligence officers; interim chief executive, financial and operating officers; independent management consultants; boutique consulting firm partners; boutique artificial intelligence consultancy founders; turnaround and restructuring leads; and digital transformation consultants, who deploy SEAS as a ready-built methodology across client engagements.

Investors and deal professionals: private equity operating group and portfolio operations leads; independent sponsors; search fund operators; family office operating partners and direct investors; growth equity and venture capital operating partners; corporate development directors; mergers and acquisitions and post-merger integration leads; quality of earnings providers; mid-market investment bankers; and independent board directors and advisors.

Technology, risk and governance: chief information, technology, digital and data officers; heads of artificial intelligence and artificial intelligence strategy; chief risk and chief compliance officers; and internal audit and artificial intelligence risk leads, who use the governance dossier, the non-human identity access policy and the NIST AI RMF mapping.

Sector operators: healthcare system chief operating and financial officers; manufacturing vice presidents of operations; business-to-business software chief operating officers; distribution and logistics operations directors; professional services operations leads; and insurance and banking operations directors, each served by the 26 industry agents across 13 verticals.

Academic and research users: business school and finance faculty; professors of operations, accounting, information systems and entrepreneurship; doctoral and master of business administration students; executive education and corporate training programs; university research centres and think tanks studying artificial intelligence adoption, productivity and value creation; and case writers, who use the worked case, the diagnostic method, the agent specifications and the nine workbooks as teaching material and as a documented reference implementation. Classroom or multi-seat academic use needs a separate licence, so write to me before distributing it to a cohort.

Two limits worth stating. Under $50M in revenue the leakage math rarely justifies the effort. And SEAS assumes someone in the room owns the earnings number; without that owner, no playbook survives contact with the quarter.

The gap between AI leaders and everyone else is widening, and it is measured. In BCG's own survey of 1,250 executives (September 2025), the small "future-built" group posted 1.7 times the revenue growth and 1.6 times the EBIT margin of laggards, and planned to spend more than twice as much on AI. McKinsey's 2025 State of AI survey found 88 percent of organizations already use AI somewhere, but only about a third have scaled it and only 39 percent report any EBIT impact. The advantage is going to the few who move from pilot to production. SEAS exists to shorten that move: a diagnostic inside seven days and a measured go/no-go at week 8 instead of an open-ended pilot. The EBITDA figures in the playbook are modeled on a reference company, not a customer result, and capturing anything still takes your team and a pilot budget.

Buyers have started pricing it. Accordion's exit-readiness survey of 200 sponsors and 200 portfolio-company CFOs (November 2025) found 85 percent of buyers now weigh AI-enabled finance capability when valuing a company, and that a rushed, misaligned exit process can cost one to three turns of exit multiple. EY's 2026 Exit Readiness Study reports the share of general partners naming AI as an exit challenge more than doubled in a year, and that buyers now separate "AI activity" from "AI strategy." With Bain counting 32,000 unsold portfolio companies and holds near seven years, a documented, governed plan is what a diligence team looks for. SEAS gives you the documentation: board and fund-level decks, an 18-month gated roadmap, and governance mapped to the NIST AI RMF and the EU AI Act. It does not guarantee a multiple, and its EBITDA numbers are modeled, not audited.

Three, none invented by me. The EU AI Act's transparency obligations have been in force since 2 August 2026; the high-risk rules were deferred to December 2027 and August 2028, so the documentation clock is running before the toughest duties arrive. Fiscal 2027 budgets get set in the fourth quarter, and Deloitte's Q4 2025 CFO Signals survey found 87 percent of CFOs expect AI to be extremely or very important to finance in 2026, so the line item is being decided now. And the SEC and FTC are actively pursuing AI washing, including the first action against a public company in early 2025, which turns a governance record into an asset. SEAS ships its governance content mapped to the NIST AI RMF and the EU AI Act so the record exists before it is asked for. It does not make you compliant by itself; that depends on your systems and your counsel.

Your downside is bounded: 100 percent refund within 90 days from date of purchase, processed by Polar, not by me. The method is built to fail fast if it fails: a 90-minute diagnostic, an eight-week pilot, and a go/no-go gate with measured thresholds, so stopping is an evidence-based decision rather than a sunk cost. On the board conversation, BCG's AI Radar 2026 found half of CEOs believe their job depends on getting AI right, and Grant Thornton's 2026 AI Impact Survey found three in four boards have approved AI investment while only 52 percent have set governance expectations. Arriving with a governed, measurable plan is what boards are asking for and few executives bring. What the refund does not return is your team's time, and SEAS has no customer case studies yet; its numbers are modeled. At $2,997 the purchase also sits under the discretionary approval limit most companies give a director, so the decision is usually yours to make.

The benchmarks for typical AI programs are slow: Deloitte found most organizations take two to four years to reach satisfactory return on a use case, with only 6 percent seeing payback inside a year, and ModelOp's 2025 AI Governance Benchmark found 56 percent of teams take 6 to 18 months to move a project from intake to production. SEAS is structured against that drift. The FLOAT diagnostic runs in 90 minutes inside seven days across three profit pockets; the 35 prompts return working output in about 15 minutes on your own CSV data; the pilot reaches its go/no-go at week 8; and the playbook models first working-capital cash impact inside 90 days with the program net positive by month 8. Those last two are modeled on the reference company, not a promise, and depend on your data quality and execution. What SEAS removes is the months teams spend building scaffolding before anyone measures anything.

Seven days from download to a board-ready case, eight weeks to a measured decision, eighteen months to full deployment. Every step names the file that runs it.

Day 1. Read the Read Me First, then Sections 1 to 3 of RAPID SEAS, the daily execution manual. About three hours.

Day 2. Run the FLOAT diagnostic on your trailing twelve-month P&L: 90 minutes, three pockets (SG&A gap, pricing and contract drift, supply chain and vendor concentration), at least three quantified EBITDA opportunities. Same afternoon, paste the relevant agent prompts into Claude, ChatGPT or Gemini with your own CSV exports and see the leak in your numbers, not the reference company's.

Days 3 to 7. Run the Data Fitness Scorecard on the target workflow (a 500-record audit; 70 percent or better to proceed). Populate the EBITDA Analysis Report and the Simple ROI Calculator for a board-ready number. Shortlist vendors with the Vendor Matrix and send the request for proposal template. Brief the executive sponsor. Populate the 10-slide Board Deck and present it for pilot approval.

Weeks minus 4 to 0, Phase 0. Preparation: baseline metrics recorded, vendor demos scored on the weighted matrix, contract and SLA addendum signed, steering committee and AI Acceptable Use Policy in place. The Project Manager’s Runbook runs this stage week by week.

Weeks 1 to 8, Phase 1. One operational workflow, typically accounts payable, in parallel run, read-only first, humans approving every action. At the end of week 8 the go/no-go gate applies measured thresholds: accuracy above 90 percent, cycle time down 25 percent or more, cost per transaction down 15 percent or more, exceptions under 8 percent. Modeled Phase 1 target for the $150M reference company: $500K to $1M cumulative EBITDA plus working capital freed.

Weeks 9 to 24, Phase 2. Controlled production and tuning to 98 percent accuracy and under 2 percent exceptions, then Gate 2 at week 24. Nothing expands until it clears.

Weeks 25 to 52, Phase 3. Three to four additional workflows, monthly steering, cross-department coordination. The strategic agents (capital structure, tax, contract value capture and the sector agents) layer in from month 7, once the operational layer is producing validated EBITDA.

Weeks 53 to 78, Phase 4. Five or more workflows at scale, quarterly governance audits, and exit preparation from week 68: the governance dossier, unit economics documentation and the CIM section draft. Modeled cumulative EBITDA at month 18: $4M to $6M.

Months 19 to 24 and beyond, Phase 5. Optimization, the bonus systems, and fund-level reporting through the Fund-Level Deck. Modeled cumulative: $6M to $10M.

Throughout. Weekly metrics every Friday, a three-level escalation matrix, exception protocols and sustainment budgeting come from the Runbook; email support from the author for 90 days; a 100 percent refund within 90 days from date of purchase. All EBITDA figures above are modeled on the $150M reference company, not customer results, and depend on your data quality, your team and your implementation budget.

Because the agents come before and after the vendor. Before: each agent ships as a prompt you run on your own CSV data in about 15 minutes, so you prove a leak exists before spending a dollar on a platform. During: each agent's specification (data inputs, decision logic, outputs, KPIs, guardrails) is the requirements document the vendor builds against, with the week-8 gate as its acceptance test. After: the strategic and sector agents are finance and operations analytics no automation vendor sells; they run on your data as prompts and stay yours. The vendor matrix covers only the operational workflows worth automating at production scale, and the SLA addendum ties the vendor's refund triggers to the same measured thresholds.