The future of work is being written now.

AI Revenue Architecture is the practice of modeling your revenue system as a dynamic system with feedback loops, diagnosing where growth is structurally leaking, and building the AI infrastructure that makes the fix permanent. It combines four disciplines: Winning by Design Bowtie revenue modeling, Nelson Repenning's Dynamic Work Design from MIT, systems dynamics modeling, and Claude Code / MCP agentic infrastructure. The result: you get a parameterized model of your revenue engine, a structural diagnosis, and working AI systems, not a slide deck and a set of recommendations.

We diagnose and redesign the revenue systems of B2B SaaS companies using four integrated disciplines: Winning by Design Bowtie revenue architecture, Nelson Repenning's Dynamic Work Design and capability trap framework, systems dynamics modeling, and Claude Code / MCP agentic infrastructure. Every engagement starts with a structural diagnosis, not a checklist, and produces a root cause finding your team didn't have before.

Management consultancies deliver strategy decks. RevOps agencies configure tools. We sit between: we model your revenue system as a dynamic system with feedback loops, identify the highest-leverage intervention point using math, then build the process redesign and AI infrastructure to fix it. Our deliverables are parameterized systems dynamics models and working AI infrastructure, not slide decks and spreadsheets.

If growth has stalled and you do not know why, start with the Revenue System Diagnostic ($24,000, 10 days). If you are about to invest in AI, start with the AI Revenue Readiness Audit ($18,000, 8 days). If you want a low-commitment first step, the Executive Briefing ($3,500, half day) is built for a single decision-maker and credits against any engagement within 90 days.

A capability trap is a self-reinforcing organizational pattern identified by MIT's Nelson Repenning. Under pressure, teams default to "working harder" instead of investing in improvement. Short-term results look fine, but capability erodes. More problems emerge, requiring more firefighting, leaving less time for improvement. In revenue teams, this means heroic individual performance masks systemic design failure, and AI automation accelerates the wrong motion. The capability-trap module of our Revenue System Diagnostic maps these dynamics specifically.

Primarily B2B SaaS companies between $10M and $200M ARR. Our productized sprints serve the $10M–$80M range. Retainer engagements (Signal through Enterprise tier) extend to $200M. The common thread: revenue system complexity that has outpaced the tools and processes managing it.

Sprints are fixed-scope, fixed-price diagnostic and build engagements: 8 to 15 business days, $18,000 to $55,000, with the Executive Briefing at $3,500. They produce a specific deliverable and a clear recommendation. Retainers are ongoing ($8,500–$65,000/month) and provide continuous Bowtie monitoring, systems dynamics model maintenance, AI infrastructure builds, and strategic advisory. Most retainer clients start with a fixed-scope engagement first.

We build Claude Code + MCP server infrastructure connected to your CRM, CS platform, and BI tools. This includes custom Claude Skills for your revenue team (deal review, pipeline health, expansion signals), automated Bowtie reporting, and agentic process automation. You own all the infrastructure: it runs on your accounts, documented in runbooks your team can maintain.

Yes, if you have 8+ quarters of clean pipeline data. The Forecast Architecture Build replaces stage-weighted pipeline guesswork with a systems dynamics model parameterized from your actual conversion rates, deal velocity distributions, and segment data. It produces P50/P75/P90 probabilistic forecasts. Data quality determines start date; if your data needs cleaning first, we'll tell you and may redirect to the Agentic RevOps Layer.

Winning by Design created the Bowtie framework, and we build on it. What we add: parameterized systems dynamics models built from your data (not benchmarks), Dynamic Work Design methodology for task-level process redesign, and agentic AI infrastructure (Claude Code + MCP servers) that automates the ongoing monitoring. Think of WbD as the architectural blueprint and dynamic.work_ as the structural engineering and automation layer. Many of our clients have done WbD training; we extend that foundation into a running system.

Yes. During the evaluation process, we will connect you with a reference at a similar ARR range and challenge profile. We do not publish case studies with client names (our diagnostic findings are confidential by nature), but we will make a direct introduction so you can ask whatever you need to ask.

In every engagement to date, the diagnostic has identified at minimum one structural root cause with a quantified impact estimate. If the Revenue System Diagnostic fails to identify a structural finding with a quantified revenue impact, we will tell you within the first 3 days and scope the engagement down accordingly. We do not run out the clock on an engagement that is not producing value.

For diagnostic sprints: 3-5 hours of leadership time (kickoff + readout) plus 4-6 hours of stakeholder interviews spread across the team. For build sprints: 6-10 hours total, mostly in 2-3 working sessions. We do the heavy lifting: the modeling, analysis, and infrastructure build. Your team provides data access, context, and decision-making authority.