AI implementation for growing businesses
Help your team use AI on real work. Connect, automate, and build the systems that create more capacity—with human judgment, clear ownership, and results you can measure.
Start where your business is today. Equip the team, solve a known bottleneck, or improve a system already in use.
01 / Equip your team
Put AI to use on real tasks, with practical workflows your team keeps.
Session lengths and schedule agreed before booking. Custom software, integrations, and subscriptions are separate. No automatic credit toward a build.
See Sprint scope02 / Build a working system
A useful system, built around a defined business problem.
Automations, integrations, and custom systems are priced to their scope. Software and usage costs are separate. A Sprint is optional.
See implementation scope03 / Improve it over time
Keep an existing workflow useful as your team and tools change.
Larger builds, monitoring, and broader rollout support need a separate scope. Software and usage costs remain separate.
See partnership scopeStart with a free introductory call. If the work needs deeper discovery, an optional Margin Scan can help define it. You can also bring a clear scope straight to implementation.
Let’s talk about your businessMargin is more than profit.
The costliest repetition is buried in inboxes, handoffs, spreadsheets, and the workarounds nobody documents.
A demo gets applause. Then it misses the real data, permissions, edge cases, and tools that make the business run.
Without an owner, review rules, training, and a scorecard, adoption fades and the old process quietly returns.
If this sounds like your shop
You do not need all eight. If three or more sound familiar, start with a conversation. We will help you choose a useful first step, with optional discovery when the work needs a deeper look.
We keep picking “all of the above.”
People use AI alone; no shared standard.
No single source of truth for how we work.
Sheets sit beside systems we already pay for.
Multiple tools, same job, little shared context.
Leadership waits on weekly pulls from CRM, ads, and finance.
A prior tool or freelancer faded without ownership.
We want AI drafts with people approving what matters.
Mostly 1, 2, 8Explore the AI Workflow Sprint
Mostly 3, 4, 6Discuss implementation scope
Mostly 5, 7, or all of the aboveStart with a free intro; consider a Margin Scan
A conversation starter, not a diagnosis. If a workflow already works, an Ongoing Partnership may be the useful next step.
The model is rented and replaceable. What decides whether AI pays is everything around it: the workflow as it really runs, the data the system can reach, the tools it connects to, the human review on consequential steps, the evals that prove it still works, and the updates as new models arrive. That deployment layer is the practice we built — not a tool rollout, but work output your business can verify.
Trace. Choose. Build. Prove.
The work moves through four gates. Each one creates something the business can inspect, own, and use—even if it chooses not to buy the next phase.
Observe the workflow. Interview the people inside it. Baseline time, volume, errors, delay, cost, and risk before choosing technology.
Output Current-state trace + measured baselineRank value, feasibility, data, risk, and adoption. Recommend a build only when there is a measurable path to recovering the investment within 12 months.
Output Build / buy / simplify / leave-alone decisionBuild against representative cases, connect the existing tools, protect access, and design the human review and exception path before going live.
Output Working system + evaluation set + review pathTeach the operator to understand the workflow, use the system, verify its output, override it, and recover. Compare the result with the baseline, document ownership, then decide whether to operate, revise, stop, or expand.
Output Before / after scorecard + runbook + next decisionStart with observable work
Use a conservative capacity estimate to decide whether a deeper baseline is worth the effort. Real value also includes cycle time, errors, throughput, customer experience, and revenue—not just labor hours.
From bottleneck to working system
Each engagement has an agreed scope, an inspectable output, and a decision. You know what we are doing, what we need from you, and what would earn the next phase.
Tell us about your team, a recurring task, or a system you are ready to build. We review the fit and agree the right first engagement.
Start with hands-on AI practice, focused discovery, or an already scoped build. The people doing the work help shape the solution.
Prioritize what to improve, configure, connect, or build. Agree scope, price, success measures, and ownership before implementation.
Implement with working reviews, representative cases, permissions, human controls, exceptions, and rollback.
Test the system, train operators, compare with the baseline, and document ownership. Decide whether to operate, revise, stop, or expand.
AI WORKFLOW SPRINT
$1,500 fixed · up to five peopleIMPLEMENTATION
Scope and schedule agreed in the proposalBuild the system. Build the judgment.
AI should remove low-value work without weakening the people accountable for the outcome. The operator learns what the workflow does, what good looks like, where it fails, and how to take control.
Explain the inputs, decisions, and definition of a correct result.
Run the assisted workflow efficiently on representative work.
Recognize incomplete, suspicious, or incorrect output before it travels.
Override the system, repair the work, and escalate the right exceptions.
Turn real operating feedback into a stronger workflow and runbook.
We do not recommend live rollout until a named operator can explain the workflow, use the system, verify its output, override it, and recover from an agreed failure scenario.
Role-specific practice, failure drills, manager coaching, and readiness reviews can support a validated workflow. We agree the people, sessions, and deliverables within the implementation or partnership scope. Broader rollout is priced separately.
01 / KNOW WHAT YOU ARE BUYING
02 / KNOW WHAT GOOD LOOKS LIKE
Selectivity protects the result
USEFUL BEFORE YOU HIRE US
26 field notes and 15 practical playbooks for choosing a first workflow, avoiding bad builds, and installing useful systems with human judgment intact.
AI strategy
First workflow
AI strategy
Before we touch the workflow
No. The right answer may be to configure a tool you already own, connect two systems, improve the process before adding AI, or build a focused custom layer. The opportunity map makes that decision explicit. A Margin Scan is available when the business needs deeper discovery.
Not by default. Each engagement documents the data flow, models, subprocessors, retention settings, credentials, and access boundaries before production use. Client-owned accounts and least-privilege access are preferred.
It should do the opposite. The Operator Standard requires the people who own the workflow to understand what good looks like, use the system, catch exceptions, override it, and recover when it fails. We do not recommend live rollout until a named operator can demonstrate those abilities on agreed scenarios.
Yes. The AI Workflow Sprint helps your team use AI on real tasks while we identify useful next steps. You keep reusable workflows, safe-use guidance, and a prioritized opportunity map even if we never build anything together. If you already have a clear implementation scope, we can start there. Broader rollout support can be scoped into implementation or an ongoing partnership.
For the Margin Scan, the Decision Clarity Guarantee covers at least three relevant opportunities, one ranked first move, and a written implementation decision by business day 7. The existing Workflow Build Ship-to-Spec Guarantee covers observable acceptance tests agreed before the build. These guarantees apply to their named engagements; they do not extend to the Sprint. These commitments require timely access, accurate inputs, and the agreed scope. They do not promise revenue, savings, layoffs, adoption, third-party uptime, or perfect model accuracy.
Yes. The model is designed to work beside the people who already own your systems. Roles, credentials, handoffs, IP, and ongoing maintenance are documented so the client is not trapped behind a consultant.
The scorecard determines the next move: operate it as-is, revise it, stop, build the next ranked workflow, or begin an Ongoing Partnership. Expansion is earned by evidence, not assumed in the original sale.
Tell us about your team, a workflow you want to improve, or a system you are ready to build. We will help you choose a useful first step—even if you are not sure where to start.
