A small, senior practice. Built for the work.
DaVinci AI is a focused data, analytics and applied-AI practice working with business and public-sector teams across regulated industries.
Founded on a stubborn idea: that data should change decisions.
DaVinci AI was founded by practitioners who’d spent enough years inside large data programs to see the pattern: enormous investment in tooling and headcount, very little change in how decisions actually get made.
The firm is built around the opposite premise. Small teams of senior people. Engagements scoped around a specific decision worth changing. Prototype in weeks; production in a quarter. Honest about what the tools can and can’t do.
Our deepest pattern of work is in the federal civilian, defense and state-level programs where the data is sensitive, the regulators are real, and the documentation has to land before the model does. We work the same way across financial services, healthcare and enterprise, places where the same engineering discipline pays off.
How we choose to work.
Decision over decoration
We design analytics and AI around the decisions they need to inform, not the dashboards they need to populate.
Prototype, then harden
We move quickly and ship working software, then harden it. No sixty-page slide decks before a single model is trained.
Senior practitioners
Every engagement is led by the people doing the work. No pyramid staffing, no inheritance from a junior team.
Boring tools where they fit
We choose dependable, well-supported tools where they work, and leading-edge ones where they unlock genuine new capability.
A practice, not a pyramid.
Every engagement is led by the people doing the work. We staff small, senior teams, typically two to four practitioners across data engineering, analytics and machine learning, rather than rotating in juniors to scale headcount.
That keeps quality consistent, communication direct and the work accountable. For larger programs we partner with a small network of trusted specialists rather than expand the firm.
What we do, and what we deliberately don’t.
Two lists. Where we add real value, and where we’d send you elsewhere. Together, they’re how we keep the practice sharp.
What we do
- Decision-grade analytics and dashboards
- Forecasting, segmentation and decision models
- LLM workflows and document intelligence
- Modern, governed data platforms
How we work
- Small senior teams (2–4 practitioners)
- Discovery sprints in 2–4 weeks
- Build sprints in 8–12 weeks
- Quarterly embedded engagements
Where we work
- Federal civilian, defense and state programs
- Financial services and insurance
- Healthcare operations
- Mid-to-large enterprises
What we don’t do
- Pyramid staffing or junior-only teams
- Multi-year transformation programs
- Direct clinical decision-making
- Investment recommendations or trade execution
Tools we lean on
- dbt, Snowflake, Databricks, BigQuery
- Python, PyTorch, scikit-learn
- Tableau, Power BI, Looker
- Azure OpenAI, AWS Bedrock, Vertex
How we contract
- Mutual NDAs, MSAs, DPAs, BAAs
- Direct and subcontracted through primes on federal vehicles
- Milestone-based pricing for build sprints
- Client-owned IP with limited reuse license
Frameworks the work runs inside.
The frameworks our delivery is designed to align with. We bring the documentation; your security team doesn’t have to chase it.
- FedRAMP Moderate / High
- NIST 800-53 Rev. 5
- NIST 800-171 / CUI
- FISMA
- Section 508
- CJIS
- IRS Pub 1075
- HIPAA / HITECH
- SOC 2 Type II
- ATO · SSP · POA&M
- Azure Gov · AWS GovCloud
- IL4-equivalent environments
A short history of the firm.
The firm we set out to build, in five movements.
- 2024
Founded
The firm was founded around a stubborn premise: that small teams of senior practitioners, doing the work themselves, would outproduce larger firms running pyramid staffing models. - 2025
First engagements
Initial engagements across financial services and the public sector. Patterns we still use today, the Discovery / Build / Embedded engagement model, the decision-first analytics method, the data-product operating model. - 2025
Practice areas formalized
Four disciplines articulated as a single practice: data & analytics, machine learning, applied AI, and data engineering. Reusable patterns moved into a shared internal library. - 2026
Public-facing writing
The first wave of insights published, practice notes drawn from real engagements, stripped of identifying detail and written by the people who shipped the work. - Today
Selective growth
We hire slowly. We turn down engagements that aren’t set up to succeed. We’d rather stay small and do excellent work than grow on the back of work we wouldn’t stand behind.
Questions we hear, answered honestly.
How big is the firm?
Where are you based?
Do you do speaking and panels?
Do you take pro bono work?
Continue with our work.
What we do
A focused practice across data, analytics, machine learning and applied AI.
ExploreHow we think
Practice notes, perspectives and reference architectures from the work itself.
Explore
Join the practice
Senior roles for practitioners who want autonomy, ownership and consequential problems.
Explore
Have a problem worth solving?
Whether you’re scoping a new initiative, modernizing analytics, or evaluating where AI actually fits, we’d be glad to talk.