We don't fill seats.
We ship solutions.
Scoped builds, outcome-priced, instrumented so the team that inherits them can run them. What follows is how an engagement runs, where the boundaries sit, and what we turn down.
What we build
We build digital operational solutions for organizations that need to scale. The pillars (Find, Build, Deploy) describe how we work. The domains (go-to-market and the business processes that connect them) describe where the work lands.
Engagements usually start with a symptom: stalled pipeline, backed-up approvals, untrusted reporting, an AI pilot that never graduated. The first conversation usually reframes the symptom into the underlying constraint. The engagement then ships a bounded solution against that constraint, instruments it so it survives without us, and hands it off documented.
Domains we work in
Sales operations, marketing operations, customer success, RevOps. The most frequent shape of our work, where pipeline meets process and where instrumentation gaps cost the most revenue.
The connective tissue between functions. Approvals, reporting, documentation that gets used, internal tooling, data pipelines, and agentic workflows where they fit.
We extend into adjacent operational domains when the problem fits our shape. We don't extend into domains that would stretch us past it.
How we work
Three pillars, sequenced as a maturity ladder. The order matters. Deploying on top of unresolved bottlenecks or undocumented processes produces worse outcomes than doing nothing.
Strategic Bottlenecks
Most growth ceilings are hidden constraints, not missing headcount. We find the ones worth fixing, and quantify the leverage.
Scaled Processes
Workflows that survive 10x growth without 10x headcount. Documentation people actually trust. Handoffs that don't break.
Agentic Solutions
AI agents on top of clean data and real processes. Not chatbots bolted onto broken workflows.
Solution shapes we build
Categories of work we ship. Read these as a self-test: if you recognize your situation in one of them, we're most of the way to a scoped engagement.
Lead-to-revenue automation
The handoff between marketing and sales is where pipeline either lands or evaporates. We replace ad-hoc routing, manual qualification, and missed SLAs with deterministic lead routing, qualification logic tested against real cases, SLA tracking with breach alerts, and exception handling for cases the model doesn't cover. The handoff stops being where deals quietly disappear.
RevOps consolidation
Scaled organizations accumulate revenue tooling: a CRM, a marketing automation platform, an attribution tool, a CDP, three Notion docs, two spreadsheets, and a Slack channel of tribal knowledge. We map the actual revenue motion, identify which parts of the stack are doing real work, and consolidate into a system that tells one coherent story from inbound to renewal.
Customer onboarding instrumentation
Time-to-first-value drives retention in subscription businesses, and most onboarding is hand-stitched enough to break it. We replace that with documented, instrumented flows that surface completion rates, time-to-first-value, and where new accounts drop off. Customer success teams stop chasing setup status and start asking whether accounts are healthy.
Customer success workflow automation
Health scoring, renewal alerting, expansion-trigger detection, and CSM action queues. We build these as instrumented systems on top of data the org already has. CSMs stop hunting for context before every call and start running plays the system surfaces for them.
Pipeline hygiene and forecasting integrity
Pipeline reports lie when CRM hygiene is poor. We tighten the data layer first: required-field enforcement that doesn't sabotage the rep workflow, stage-transition gating with documented criteria, pipeline-aging alerts. Forecasting accuracy is downstream of data integrity. No forecasting tool fixes that.
Customer support automation with agentic triage
First-touch support handled by an agentic system that resolves known patterns end to end and escalates judgment cases to humans with full context attached. Not a chatbot bolted onto a help center. The system handles the resolvable, hands off the rest, and instruments both ends so quality stays visible and the support team gets cleaner work.
Approval bottleneck removal
Manual approval gates that cap throughput get replaced with automated routing and exception handling, including agentic logic where exception detection is the hard part. The gate stays where it adds judgment; humans handle the exceptions; the system handles everything else, with a documented audit trail.
Reporting consolidation
Scattered metrics across tools (one team's Tableau, another's Looker, a third's spreadsheet) consolidated into a single-source architecture with regression alerts. Decision-makers stop arguing about whose number is right. The dashboard tells one story; the alerts catch when the story breaks.
Data pipeline rebuilds
Brittle integrations between digital systems (CRM, marketing automation, data warehouses, BI tools, internal apps, vendor portals) replaced with tested, alerted pipelines. Failure modes are named, the team owns the runbook, and the 3 AM page-out becomes a 9 AM ticket.
Internal tooling with embedded agents
Custom internal tools where agents do the repeatable work (data lookups, draft generation, research synthesis, quality checks) and humans do the judgment. Built on the org's actual data, not on a vendor's idea of what your data should look like. Usage and outcomes stay visible to the people who own the tool.
Documentation that gets used
Runbooks tied to the systems they document, with metrics on whether anyone is actually following them. The documentation becomes a live layer of the operation instead of a parallel one going stale.
Onboarding and ramp compression
New-hire ramp times reduced through documented, instrumented playbooks. Training built on top of the same documentation that runs the operation, so institutional memory survives staff changes.
What ships
The handoff is the solution plus four things that let it survive without us:
- Automated testing: regressions caught before they bite
- Reporting: the solution's work is visible
- Alerting: failures reach humans before users do
- Documentation: the team owns it, not the consultant
Price is fixed up front and tied to the outcome. Hourly billing isn't on the menu. Afterwards there's a small optional retainer, which is us answering the phone rather than a maintenance contract, and either side can walk with a month's notice.
What we don't do
- Full-scope multi-year transformations. Wrong shape for us. We refer those to firms better positioned to run them.
- Pure advisory without a build component. The instrumentation discipline only works if we're the ones implementing it. Decks-only work goes to firms that do that well.
- AI pilots without operational foundations. Deploying agents on top of unresolved bottlenecks or undocumented processes is worse than doing nothing. We sequence Find and Build before Deploy, even when you wanted to skip ahead.
- Hourly billing. Fundamentally misaligns incentives. We get paid when the thing ships and works.
- Permanent embedded presence. Phase 5 is small and optional, not a fractional COO contract. Practices that need an ongoing operator should hire one.