Industries

Three audiences. One operating discipline.

Different verticals, different pressure points, different commercial language. The diagnostic, the framework, and the engagement models are the same. We work with associations and nonprofits, PE-backed and growth SMBs, and other knowledge-work-intensive organizations.

01

Associations & Nonprofits

Trade associations, professional associations, foundations, and 501(c) organizations. Member experience, AMS and CRM, ecommerce, LMS, website, and AI foundation.
02

PE-Backed & Growth SMBs

Portfolio companies and founder-led growth SMBs. RevOps integration, exit readiness, and operational scalability without proportional headcount.
03

General & Knowledge Work

Healthcare-adjacent organizations, mid-market services firms, and other ops-heavy knowledge-work operators with manual processes and siloed data.
Vertical 01

Associations & Nonprofits

Trade associations, professional associations, foundations, and 501(c) organizations. Most run on a legacy AMS that doesn't talk to the CRM, on membership renewal and event registration that are still substantially manual, and on a small operations team carrying significant key-person risk. AI is a board topic, but the data infrastructure to support it doesn't exist yet.

The pattern we see

  • Disconnected systems. The AMS is legacy. The CRM is newer. They share data through manual exports or outdated middleware. Membership data lives in three places and disagrees with itself.
  • Manual membership lifecycle. Renewals are chased by hand. Onboarding emails go out manually. Lapsed-member outreach depends on someone remembering to pull the report.
  • Event ops grind. Registration, badges, reporting, and post-event follow-up consume disproportionate operations time. The annual event eats months of bandwidth.
  • Reporting that takes weeks. Board books are pulled together by hand from four systems. By the time the report is current, the data isn't.
  • Key-person risk. The longest-tenured operations person holds critical knowledge in their head. If they leave, the engine stops.
  • AI without infrastructure. The board wants to know "what are we doing with AI?" The honest answer is: nothing useful, until the data is integrated and the processes are documented.

How we engage

Engagements span three pillars — the member-facing experience, the operational systems behind it, and the data foundation that lets AI ride on top.

01

Member experience

Tier design, lifecycle automation (renewal, onboarding, lapsed outreach), gated content, member analytics, communication strategy and execution, ecommerce, learning management, and website redesign — built around the member journey.

02

Operational systems

Implement and optimize best-in-class platforms — AMS, CRM, marketing automation, sales, customer success. Integrate the operational layer into a single source of truth. Modernize event ops. Document workflows so knowledge stops living in one person's head.

03

AI & agent foundation

Integrated data, documented processes, clean member records, instrumented workflows. The infrastructure that makes agentic AI a practical option, not a slide.

Common scope

Member portal & gated content · Tiers & lifecycle automation · Member analytics · Ecommerce · LMS · Website redesign · Communication strategy · AMS ↔ CRM integration · Sales & customer success ops · Event ops · Board reporting · AI foundation

Typical engagement

Retainer (most common) · Fixed Price for single-system migrations · Agentic AI as a Service for ongoing agent deployment + measurement

Decision authority

CEO/Executive Director · COO · CIO · Board chair sponsorship

Vertical 02

PE-Backed & Growth SMBs

Private equity portfolio companies and founder-led growth SMBs. Revenue operations don't connect. Reporting can't survive a due diligence pull. Operational processes don't scale without proportional headcount. The technology stack accumulated through three rounds of CEOs and two acquisitions is somebody else's problem until it's yours.

The pattern we see

  • Disconnected revenue ops. Marketing automation doesn't sync to the CRM. The CRM doesn't sync to billing. Sales-to-finance reporting is stitched together by hand each month.
  • Reporting that doesn't survive due diligence. Numbers look fine in dashboards but can't be reproduced from source data. Audit trails are missing. The story falls apart under inspection.
  • Headcount-dependent scale. Adding revenue requires adding ops headcount at the same rate. Operating leverage isn't there. Margin compression is structural, not cyclical.
  • Inherited tech debt. The stack accumulated under prior leadership. Nobody knows why half the integrations exist or whether they still work. Renewal calendars surprise people.
  • Ops talent gap. The org doesn't have the in-house ops talent to run the kind of integration and automation work that would unlock leverage.
  • Exit readiness. The PE thesis included an exit window. The operational layer is not ready for that conversation.

How we engage

  • Integrate the revenue stack. Marketing ↔ CRM ↔ billing ↔ finance, in a way that holds up under scrutiny.
  • Build PE-grade reporting. KPI infrastructure that's reconcilable, audit-trail-complete, and defensible in due diligence.
  • Eliminate the manual workflows that block scale. The processes that require a person, become processes that don't. Operating leverage gets unlocked.
  • Document for exit readiness. Process maps, SOPs, and architecture diagrams to a standard that supports growth, exit, or both. The operational layer becomes a strength in a sale process, not a finding.
  • Portfolio-wide Agentic AI as a Service for PE firms. Agents deployed across portcos with one operational signal underneath. Comparable, defensible, repeatable. The single source of truth for portfolio operating performance — and the leverage layer that scales it.

Common scope

Revenue ops integration · Sales-to-finance pipeline · Exit readiness infrastructure · Operational scalability · Portfolio-wide Agentic AI as a Service · EBITDA-impacting automation

Typical engagement

Retainer for portcos · Fixed Price for single-thread builds · Agentic AI as a Service for portfolio-level agent deployment + rollup

Decision authority

Operating Partner (PE firm) · CEO · CFO · Founder · Board

Vertical 03

General & Knowledge-Work Organizations

Healthcare-adjacent organizations, mid-market services firms, and other knowledge-work-intensive operators. The vertical changes; the pattern doesn't. Manual processes that grew with the organization. Siloed data spread across four systems that were each "the right answer" at the time. Technology underutilized because nobody had the bandwidth to configure it properly. Key-person risk on the people who hold it all together.

The pattern we see

  • Manual ops that grew with the org. The processes work, but they don't scale and they don't survive turnover. A growing organization runs into the wall the same way every time.
  • Siloed data. Four systems, four versions of the truth. Reporting requires manual reconciliation. Decisions get made on stale or inconsistent data.
  • Underutilized tech. The platforms have features that would help. Nobody has the bandwidth to configure them. The license fees keep coming; the value doesn't.
  • Bottleneck people. One operations person, one IT person, one finance person — each holding pieces of a system in their head that nobody else can run.
  • "AI" as a board question. Leadership is being asked what they're doing about AI. The honest answer requires data infrastructure that doesn't exist.

How we engage

  • The framework is the same. Diagnose the operational layer. Phase the work. Deliver the high-leverage wins first. Build toward a measurable, AI-ready foundation.
  • Document and automate. The processes that should run themselves, run themselves. The processes that require human judgment get documented to a standard that survives turnover.
  • Consolidate the data layer. Single source of truth. Reconcilable reporting. Decisions on current data, not last quarter's spreadsheet.
  • Reduce key-person risk. The system runs the work; people don't hold it together.
  • Build toward AI readiness. The infrastructure question, not the model question. When the data is integrated and the processes are documented, agentic AI becomes a practical option.

Common scope

Process documentation · System integration · Reporting and analytics layer · Workflow automation · Key-person risk reduction · AI readiness baseline

Typical engagement

Retainer for multi-function transformation · Fixed Price for bounded scopes · Agentic AI as a Service for ongoing agent deployment + measurement

Decision authority

CEO · COO · CIO · Head of Operations · Founder

Self-diagnosis

You might need us if any of this sounds familiar.

Across all three verticals, the signals overlap. If you're nodding at three or more of these, the Opportunity Engine is the right next step.

  • Reporting takes weeks because data lives in too many systems and nothing reconciles cleanly.
  • Critical operational knowledge lives in one or two people's heads — and you know exactly which two.
  • Your software is paid for, but the features that would help most aren't configured because nobody has the time.
  • You've talked about AI in board meetings or leadership offsites, but the data infrastructure to actually deploy it doesn't exist yet.
  • Manual workflows are eating disproportionate operations time, and adding headcount is the only solution anyone has proposed.
  • Integrations between core systems exist but they break, drift, or need a person to nurse them along.
  • You acquired or merged with another organization, and the technology consolidation is still on the to-do list eighteen months later.
  • Due diligence (real or hypothetical) would not be a comfortable process given how reporting is currently put together.
  • Membership renewal, customer onboarding, or quote-to-cash takes longer than it should and depends on tribal knowledge to execute.
  • You're a PE operating partner and you want one operational picture across the portfolio, not five different reports in five different formats.

If this sounds like your organization, start here.

The Opportunity Engine is a 33-question diagnostic that produces a real read on your operational maturity, your readiness to execute, and your AI horizon. The output is the foundation everything else gets built on.

Take the Opportunity Engine → Book a discovery call