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DaVinci AI
What we do

A focused practice, end to end.

From the first decision worth informing to the production system that informs it daily, we cover the full arc.

A practice, not a menu

Four disciplines, one practice.

Most engagements draw on more than one of our disciplines. Analytics modernization needs data engineering. A useful ML deployment needs analytics instrumentation around it. Applied AI without document and data foundations is theater. We keep the four disciplines inside one practice for exactly that reason.

The pages below describe each capability in detail, but the typical engagement is shaped around an outcome, not a service line.

Data & Analytics
01

Data & Analytics

Dashboards that change behavior, built around the decisions they need to inform, instrumented for the metrics that matter.

  • Executive & operational dashboards
  • KPI design and instrumentation
  • Self-service analytics enablement
Explore Data & Analytics
Machine Learning
02

Machine Learning

Forecasting, segmentation and decision-support models grounded in your operational reality, not toy notebooks.

  • Forecasting & demand modeling
  • Risk, propensity & segmentation
  • Model monitoring and retraining
Explore Machine Learning
Applied AI
03

Applied AI

LLM-driven workflows and document intelligence applied where they remove real friction, not as headline ornaments.

  • LLM workflows & retrieval pipelines
  • Document & language understanding
  • Human-in-the-loop design
Explore Applied AI
Data Engineering
04

Data Engineering

Modern, governed data platforms that turn one-off reports into durable, auditable assets for the whole organization.

  • Lakehouse and warehouse design
  • Pipeline orchestration & quality
  • Governance, lineage and access control
Explore Data Engineering
How we partner

Three formats. All senior-led.

Most engagements start with a Discovery sprint, then graduate to a Build sprint or Embedded team. We’re happy to start anywhere that fits the work.

012–4 weeks

Discovery sprint

A focused engagement to define the decision worth informing and prove the data exists to inform it. Ends in a working prototype, an honest feasibility read, and a costed roadmap.

Typical deliverables

  • Decision and KPI map
  • Data feasibility assessment
  • Working prototype on your data
  • Costed roadmap to production
028–12 weeks

Build sprint

A senior pod takes a defined initiative from prototype to production-grade system, designed for your stack, instrumented for adoption, hardened for the real world.

Typical deliverables

  • Production-grade build
  • CI/CD, monitoring and runbooks
  • Stakeholder training and enablement
  • Ninety-day adoption review
03Quarterly

Embedded team

For organizations standing up an internal capability, we embed alongside your team, shipping production work while transferring practice, patterns and ownership.

Typical deliverables

  • Quarterly outcomes plan
  • Pair-building and code review
  • Standards, templates and playbooks
  • Capability transfer and handoff
What we ship

The artifacts that come out of an engagement.

Every engagement is different, but the deliverables tend to cluster into the same families. Below, what to expect.

Decision artifacts

  • Decision and KPI maps
  • Metric definitions in version control
  • Stakeholder briefing dashboards
  • Ninety-day adoption reviews

Systems

  • Production analytics and ML services
  • Data pipelines with quality tests
  • CI/CD with monitoring and alerting
  • Documented runbooks for your team

Governance

  • Lineage and data catalogs
  • Role-based access and policies
  • PII tagging and handling
  • Audit trails and decision provenance

Enablement

  • Onboarding for stakeholders
  • Training for analyst teams
  • Pattern libraries and templates
  • Documentation and architecture decisions

Model artifacts

  • Feature stores and training pipelines
  • Evaluation harnesses and back-tests
  • Drift and performance monitoring
  • Model documentation and challenger models

AI workflows

  • Retrieval-augmented generation pipelines
  • Structured generation with schemas
  • Human-in-the-loop tooling
  • Evaluation suites and regression tests
Frequently asked

Questions we hear, answered honestly.

How small an engagement will you take?
Our smallest engagements are two-week Discovery sprints. Below that, the ramp-up cost typically outweighs the value. We’d rather pass than under-deliver.
Do you take fixed-bid engagements?
For Discovery sprints, yes, they’re scoped tightly enough to price with confidence. For Build sprints, we prefer milestone-based pricing that rewards both sides for finishing the actual work on time.
Will you sign our paper?
Usually, yes. We’re comfortable with mutual NDAs, MSAs, DPAs and BAA-equivalent agreements where applicable. We do flag clauses we can’t accept rather than silently renegotiating later.
Can you work alongside our existing vendors?
Often. We’ve worked productively in environments with established SI partners, platform vendors and in-house teams. We’ll be explicit upfront about what we own and what we don’t.
Do you build IP we keep?
Yes. Code, models and documentation we produce in your engagement are yours, with a limited reuse license for us to evolve our patterns. We spell this out in the MSA.
What if the prototype doesn’t pan out?
That’s the point of a Discovery sprint. If the feasibility read is negative, we say so, and you walk away with an honest assessment and the prototype to inform your next decision. We’d rather give bad news early than collect fees for a year of bad news.

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.

Start a conversationadmin@davinciai.dev