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DaVinci AI
Services · Machine Learning

Models that earn their keep.

Forecasting, segmentation and decision-support models grounded in statistical rigor and built to live in production, not in a notebook.

Forecasting

Honest forecasts you can plan against.

A forecast is only useful if it’s honest about its uncertainty. We build forecasting systems that pair point estimates with calibrated intervals, explainable drivers, and the workflow to retrain as the world changes.
  • Demand, revenue and risk forecasting
  • Hierarchical and time-series reconciliation
  • Calibration, back-testing and drift monitoring
Segmentation & propensity

Find the structure already in your data.

Behavioral, customer and risk segmentation that survives contact with the operations team. We focus on interpretable features and stable cohorts, not silver-bullet black boxes you can’t explain to a regulator.
  • Customer & cohort segmentation
  • Propensity, churn and lifetime-value models
  • Interpretable feature engineering
MLOps

A model is a system, not a notebook.

We treat models as software: versioned, monitored, tested, and rollback-safe. That’s what turns a one-off experiment into an asset that compounds value year over year.
  • Feature stores and training pipelines
  • CI/CD for models, with shadow and canary deploys
  • Drift, fairness and performance monitoring
Modeling capabilities

The model families we build and ship.

The right model is the one that fits the data, the constraints, and the people who have to live with it.

Forecasting

  • Time-series and hierarchical forecasts
  • Probabilistic / interval forecasting
  • Demand and revenue planning
  • Operational capacity forecasting

Classification & propensity

  • Churn, conversion and uptake models
  • Fraud and anomaly detection
  • Credit and risk scoring
  • Document classification

Segmentation

  • Customer and cohort clustering
  • Behavioral and journey segmentation
  • Geo-spatial and account groupings
  • Lookalike modeling

Regression & uplift

  • Pricing and elasticity models
  • Uplift / treatment-effect modeling
  • Causal inference (DID, IV, synthetic controls)
  • A/B and switchback analysis

NLP & extraction

  • Entity extraction and linking
  • Topic and theme modeling
  • Sentiment and intent classification
  • Free-text taxonomy mapping

Recommendation & ranking

  • Content and product recommenders
  • Next-best-action systems
  • Learning-to-rank for retrieval
  • Cold-start strategies
Case snapshot

How it plays out, in practice.

A representative engagement, described in the structure of challenge, approach and outcome. Specifics changed to preserve client confidentiality.

Predictive Market Signals
Financial Services

Predictive Market Signals

Challenge

An investment-research team had hundreds of features and dozens of analyst-built models, but no shared infrastructure for evaluation, monitoring or productionization. Decisions varied by analyst, not by signal.

Approach

  • Stood up a feature store with shared, versioned definitions
  • Built a back-testing harness with walk-forward evaluation and calibration checks
  • Productionized two pilot signals with daily refresh and drift monitoring
  • Trained analysts on the evaluation harness so it became their own

Outcome

The two pilot signals replaced three analyst-built models. Calibration improved measurably, and the team shipped four more signals into production over the next two quarters, without our involvement.

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
Frequently asked

Questions we hear, answered honestly.

Do you do generative AI, or just classical ML?
Both. We treat LLMs as one tool among many, and we pick them when they fit. For structured prediction problems with abundant data, classical ML is often the right answer, and the right answer is what we want to ship. See Applied AI for LLM-specific work.
How do you handle model risk and governance?
Every model we ship into a regulated context comes with documentation aligned to SR 11-7-style standards: assumptions, data lineage, training methodology, evaluation results, monitoring plan, and a designated owner. It’s part of the deliverable, not a follow-up project.
What does monitoring look like in practice?
Feature drift, prediction drift, performance drift, and freshness, all instrumented before launch with alert thresholds calibrated against historical noise. We pair monitoring with a written runbook so on-call isn’t guessing when an alert fires.
Will your models work with our existing platform?
In nearly every case, yes. We’ve shipped models that live on Databricks, Vertex, SageMaker, Azure ML, on-prem Kubernetes and plain Linux boxes. We prefer the platform you already operate to a greenfield one we have to teach you.
Can you retrain our models for us?
We can, but we typically design and document the retraining pipeline so your team can own it after the engagement. Long-term dependence on a consulting firm for retraining is a failure mode worth designing against.
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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.

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