How we craft AI that ships.
From discovery to deployment. A proven process that delivers working solutions—not abandoned prototypes.
Four phases. 4-8 weeks. Working systems.
- 1
Intake & Reality Check
Week 1What Happens
- • Clarify the real problem (not the stated problem)
- • Map constraints: budget, team capacity, data quality, existing workflows
- • Identify edge cases and failure modes
- • Define success metrics (what changes when this works?)
Deliverables
- Problem definition document
- Constraint map
- Success criteria
- Estimated timeline
Why This Matters
Most AI projects fail because they solve the wrong problem. We start by understanding what actually breaks, who it affects, and what "fixed" looks like.
- 2
System Design
Week 1-2What Happens
- • Design what the AI needs to know, do, and integrate with
- • Map RACI (who approves, who's informed, who executes)
- • Build guardrails and approval flows
- • Design data structures and agent architecture
- • Plan for human-in-the-loop scenarios
Deliverables
- System architecture diagram
- Data schema
- Workflow map
- Integration requirements
- Risk mitigation plan
Why This Matters
AI doesn't work in isolation. We design the full system—including the people, processes, and governance around it.
- 3
Build & Ship MVP
Week 2-6What Happens
- • Weekly demos (you see progress, not surprises)
- • Small, safe releases into production
- • Iterate based on real use (not assumptions)
- • Test edge cases as we discover them
- • Refine based on actual user behavior
Deliverables
- Working MVP deployed to production
- User feedback log
- Iteration backlog
- Performance metrics dashboard
Why This Matters
We ship usable work every week. If something doesn't work in production, we know in week 2, not week 12.
- 4
Scale & Handoff
Week 6-8What Happens
- • Expand to full workflows (beyond MVP scope)
- • Train your team on operation and maintenance
- • Document everything (no tribal knowledge)
- • Set up monitoring and alerting
- • Plan for ongoing improvements
Deliverables
- Production system at full scale
- Training materials and SOPs
- Technical documentation
- Monitoring dashboard
- Improvement backlog
Why This Matters
You own what we build. We don't disappear after launch—we make sure your team can run, maintain, and improve the system.
How we're different.
Weekly demos mean you see progress and can course-correct early. No surprises at the end. If something's not working in week 2, we fix it in week 3—not after launch.
Before/after snapshots and clear metrics. If we can't measure it, we don't claim it. Success means real outcomes (hours saved, users served, errors prevented)—not "AI is live."
Documentation, training, and support included. You own what we build together. We don't create dependency—we create capability.
We design for messy data, incomplete information, and real constraints. If it only works in perfect conditions, it doesn't work.
Every engagement includes:
What You Get
- Weekly demos
See progress every week, not just at the end
- Working software
Production-ready systems, not prototypes
- Documentation
Technical docs, SOPs, and runbooks
- Training
Your team learns to operate and maintain the system
- Source code access
You own everything we build
- Ongoing support
2 weeks post-launch support included
- Improvement backlog
Prioritized list of future enhancements
- Performance metrics
Dashboard showing real impact
What You Don't Get
- PowerPoint decks with recommendations
- Prototypes that collapse under real use
- Systems that require us to maintain forever
- Vendor lock-in
How we work together.
Built for organizations that can't afford to fail.
Need to serve more people without adding headcount. Limited budgets, high impact requirements, mission-critical reliability.
Managing cohorts, matching mentors, evaluating applications. Need structured workflows that don't break as programs scale.
Running on spreadsheets and manual processes. Need AI that integrates with existing tools, not replaces everything.
Ready to get started?
Book a free 30-minute discovery call. We'll map your use case, identify constraints, and outline a realistic path forward—whether that's working with us or not.