Data analytics & AI consulting
for founders who need clarity
Decide what data and AI work is actually useful, build the infrastructure that matters, and turn analytics into decisions you can act on. You keep full ownership.
Data and AI consulting is hands-on help choosing what to measure, standing up the stack your team can run, and wiring analytics or models into decisions. The goal is clarity and usable systems, not endless pilots.
Who data and AI consulting is for
Founders who need a clear data path without drowning in tools: reliable reporting, a few AI use cases worth shipping, or architecture their team can operate. Teams in Orlando and Germany use this when they already run the company and need a scoped project.
Related work includes software development consulting, process automation consulting, and fractional CTO. If you want to build or partner on a company with us, start with the venture studio. For how the models differ, see venture studio vs consulting. For when data and AI help actually pays off, read data and AI consulting for startups.
What data and AI consulting covers
Built for teams who want clarity more than jargon. We help you make sense of the data you already have, decide where AI is worth the effort, and build just enough infrastructure to move without stalling in endless pilots.
Data strategy & architecture
A data plan and architecture that match how your business actually decides.
We design the architecture, pick tools your team can run, and write a roadmap that covers what to fix now versus what can wait. The goal is reliable data for decisions, not a shiny warehouse nobody uses.
Advanced analytics
Predictive models and analysis that answer real business questions.
Predictive modeling, stats, and lightweight ML where they earn their keep. We turn raw data into answers your team can use in planning and operations, not dashboards that look clever and sit unused.
AI implementation
Practical AI where it removes busywork or improves decisions.
We find the few AI use cases worth shipping, build a rollout path, and implement with your team so the result fits how you already work. No pilot theater.
Business intelligence
Dashboards and reports people actually open.
We build reporting that matches how leaders and teams make decisions: clear metrics, simple views, and a rhythm for reviewing them.
Data governance
Quality, security, and ownership rules your team can keep.
Policies and processes for quality, access, privacy, and compliance. Enough structure that people trust the numbers without creating a bureaucracy.
Machine learning solutions
Custom models for a specific problem, not AI for its own sake.
We identify where ML helps, build and deploy models with your team, and measure whether they change outcomes. If it does not move a metric, we do not ship it.
How data and AI projects usually run
Typical timeline
Most data and AI projects land between 12-20 weeks, depending on where you start. Some founders need a sharp strategy and roadmap; others want us in the weeds on implementation.
Phase 1: Data assessment (weeks 1-3)
Read on your data landscape, stack, and AI readiness. We check quality, gaps, and the few places analytics or AI would actually help.
Phase 2: Strategy & roadmap (weeks 4-8)
Data and AI plan with priorities, milestones, and written success criteria matched to budget and team capacity.
Phase 3: Implementation (weeks 9-16)
Hands-on build with your team: analytics, models, and wiring into the workflows people already use.
Phase 4: Follow-through (ongoing)
Tuning and expansion if you want it. Many teams keep a lighter advisory cadence as they grow the next use case.
Why founders hire us for data and AI consulting
We come at data and AI from real venture building. That keeps recommendations ambitious enough to matter, and practical enough that your team can ship them without burning out. If you are still deciding between scoped help and co-building, start with venture studio vs consulting.
What you can expect
- Data strategy and architecture your team can operate day to day
- Analytics and predictive work tied to decisions, not vanity dashboards
- AI and ML only where it removes busywork or improves outcomes
- Business intelligence people actually open and use
- Governance for quality, security, and compliance without bureaucracy
Common questions
What does your data and AI work cover?
Help deciding what is worth measuring or automating, building the systems behind it, and turning analytics into decisions your team will actually use.
Do you build models or only advise?
Both, depending on the brief. We start with the decision you need to make, then implement pipelines, dashboards, or applied ML only where it beats a simpler rule.
How is this different from the venture studio?
Consulting is work inside your company. The studio is for building or partnering on companies with us. If you want to co-build, start with the studio.