How we work
From objective to outcome, one sprint at a time.
We start from business objectives, not technology. We prioritize use cases, stage the work, and validate every milestone with clear KPIs so each step funds the next.
The framework
Clarity. Structure. Execution.
Business first, technology strong. Most AI projects fail because they start with the technology. Ours start with the P&L.
Clarity
Find what actually matters
A focused diagnostic workshop to identify the AI opportunities that move the P&L. You leave with a draft 30/60/90 plan; the final version with KPIs and backlog follows in 3 to 5 business days.
- High-impact use cases tied to ROI
- Data and technical feasibility
- A prioritized 30/60/90 plan
Structure
Evidence before investment
A staged path where every step produces something you can see. Proofs of concept and MVPs built on your real data, with clear checkpoints and go/no-go gates.
- Sprint-based roadmap
- POCs and MVPs on your data
- Measurable checkpoints
Execution
Deploy what works
Validated solutions go into daily operations with a commercial-grade rollout, integration with your systems, training, and ongoing support.
- Production deployment
- Integration with your stack
- Training and adoption support
Execution model
The AI engineering pod
Execution runs through a dedicated or fractional pod: product, engineering, QA, and DevOps, combining onshore, nearshore, and offshore talent to optimize total cost. It starts with a fixed-price first win in about 30 days. After that the pod works one sprint at a time on a monthly engagement, with a performance review every sprint.
Spec before code
Every feature starts as a written specification your stakeholders approve before any build begins.
AI-accelerated, human-owned
AI speeds up requirements, design, code, and tests. Architecture decisions and code review stay with senior engineers.
No change-order surprises
If your priorities change, we re-plan in the next sprint. No surcharge for changing direction.
You own it
Running on your infrastructure, documented, handed to your team with a named owner.
Questions
What operators ask us first.
Do I need perfect data?
No. We identify what is sufficient for a proof of concept or MVP and what needs to mature before production.
How long until I have a plan?
You leave the workshop with a solid draft. The final 30/60/90 plan with KPIs and backlog arrives in 3 to 5 business days.
How does the engineering team work with mine?
We assign a dedicated or fractional pod that works in sprints alongside your people, on a monthly engagement with a retrospective every sprint. The first phase is a fixed-price project, so you see results before committing to more.
What if our priorities change?
We reprioritize at the next sprint planning. There is no surcharge for changing direction.
Do we have to buy a platform?
No. We are vendor-neutral. We build on the tools you already run and only recommend new software when a workflow proves it needs it.
What size companies do you work with?
Mid-market operators, typically $50M to $500M in revenue, too big for off-the-shelf tools and too small to get the big consultancies' attention.
Next step
Find the one workflow worth automating first.
Thirty minutes with José. You bring the operation, we bring an honest read on where AI pays and where it doesn't.