The Three Phase Playbook We've Run With 30+ Companies
Start With The Number That Matters
Before we touch a tool or write a line of code, we identify the business or financial metric that unlocks your next milestone (funding, exit, valuation, cash flow). For most companies, revenue, growth rate, or profit matter most.
Then we find the constraint to remove or growth lever to pull to move that metric. We map levers to AI use cases and initiatives.
The SLED Framework for Finding the Best AI Use Cases
Once we know the biggest constraint or growth lever, we use SLED to find the highest-leverage AI use cases to execute it.
Most companies fixate on the Do bucket, automating what people already do. The highest-impact opportunities are almost always in the Scale or Launch buckets.
What's already working that you'd do ten times more of with infinite headcount?
We get AI to expand the volume of what's already proven to get results without adding headcount. Common for delivery capacity, and customer acquisition plays.
What channel, motion, or campaign would unlock your next milestone if you could stand it up without hiring a team?
We build it AI-Native from day one. This is the path for new offers or new customer acquisition plays.
What would improve your performance but you couldn't justify spend at human cost?
AI handles work that was never viable before, at a fraction of the cost. Common for acquisition, conversion, and retention plays where quality or coverage was a bottleneck.
What does your team do manually every day?
We get AI handling the repetitive work so your people move up to higher-leverage activity. The primary path for cost reduction and profitability intiatives are automating people or replacing tools.
Build for Outcomes, Not Features
We embed with your team to discover opportunities, map workflows, and iterate on solutions.
Every build starts with a prototype to get higher-fidelity feedback sooner.
Getting AI to do excellent work and drive outcomes requires functional expertise, not just technical skill. We encode domain knowledge into the system so it drives outcomes, not just technically working solutions.
Getting It Used Is Half the Work
AI no one uses is the same as AI that never shipped. We treat deployment as its own phase because that is where the business value gets realized.
Clear internal docs covering how the system works and why it makes the decisions it makes.
Getting every person who touches the system up to speed and confident using it.
Rollout structure, adoption tracking, and the top-down mandate that actually gets teams to change how they work.
ROI calculation benchmarked against before/after for Scale, Do, Enable use cases. New cost vs. new results for Launch.
Ongoing monitoring, incident response, and model improvements so reliability and output quality do not drift as usage grows.
New capability and scope as your business evolves. A system that never gains features falls behind the work you need it to do.