Case Studies
Gave a Field Service Dispatcher 400 Hours Back
Plain English: A scheduler was spending 4 days per month making a manual Excel-based calendar for 100+ client visits. We built a simple, web-based tool the scheduler runs instead.
Tech Specs: We built a deterministic constraint solver first, covering drive radius, cadence, and PTO. Then, we layered in an LLM. The result is a browser tool with a drag-to-reschedule calendar.
Built a Teams Bot for Accurate Sales Quoting
Plain English: An insurance brokerage relied on a PDF library, OneNote files, and unwritten rules to quote new business. We built a Teams bot that answers team member questions accurately, while citing its sources.
Tech Specs: We built a deterministic extraction pipeline that created over 7,000 rules surfaced from natural language interactions with a database.
Who We Serve
- We partner best with firms with 30+ employees and more than $5 million in revenue.
- We work with businesses who see opportunities to deploy AI for good in their organization, no matter what stage of AI deployment they are in.
- If you are a non-profit looking for low bono or pro bono work, please contact us.
What We Believe
- AI should support people, not replace them. We build tools that help people do more work that matters. If a project's goal is cutting jobs, we're not the right firm.
- Effective AI needs shared buy-in. The most effective projects create shared excitement on your team.
- People in the loop. AI can save your team time, but important decisions remain theirs.
- Ownership stays with you. From training to data, we believe core competencies and operations should live with your organization, not ours.
- AI deployment must be thoughtful. Not all projects are a good fit for AI. We focus on the ones that are.
Where to Start
Most organizations move through four stages, and we meet you at whichever one you're in.
- Stage 1: Thought partner. Use AI to ask questions, not connected to any systems. Someone pastes data into ChatGPT and copies the answer back by hand.
- Stage 2: Assistant. Tools are wired into your internal systems and data, reading your source documents and learning your organization.
- Stage 3: Teammate. AI runs workflows on its own, reviewed by your team at checkpoints you define.
- Stage 4: System. High-impact, complex workflows run end to end and improve over time. Rare, and usually the last stage we reach with a client, not the first.
We don't charge for getting to know you. That would be ridiculous.
Who's Behind This
Colin Burns, public markets and capital allocation.
Justin Duke, software engineering and SaaS.
Myles Marino, early-stage operations.
Harrison Roday, business operations, finance, and deal execution.