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DaVinci AI
Blueprints

The blueprints we build engagements on.

Five opinionated reference architectures, woven through with AI assurance and governance. They’re frameworks and patterns, not licensed products, deployed inside your tenant.

Why blueprints

Patterns worth not re-inventing.

Every engagement is bespoke, but the scaffolding underneath doesn’t have to be. Five foundations come up again and again across the kind of work we’re built for, a decision layer, an agentic layer, a knowledge layer, a forecasting and ML layer, and the data platform underneath. We’ve documented our approach to each as a reference architecture so engagements don’t start from a blank page.

These are reference architectures, not SaaS products. There’s nothing to license. In an engagement they’re deployed inside your tenant, the code and documentation are yours, and we’d operate them with you for as long as the engagement runs. We keep a limited reuse license so we can keep the patterns evolving.

Cross-cutting

AI Assurance & Governance.

Built into every blueprint, not bolted on after. The same evidentiary spine runs through all five, so a recommendation, a model output or an agent action is something an oversight function, an auditor or an Inspector General can actually interrogate.

NIST AI RMF alignment

Govern, Map, Measure, Manage, mapped to the artifacts an oversight function actually asks for.

SR 11-7 model risk

Model documentation, validation evidence and challenger frameworks shaped for regulated review.

Evals & red-team

Task-grounded eval suites, regression tests and adversarial probes wired into CI before models ship.

Decision provenance

Every recommendation, every agent action, every metric, traceable to the data and the rules behind it.

PII / CUI handling

Boundary-level detection, redaction and access control. Data stays inside the tenant it started in.

Decision Intelligence
Blueprint · 01

Decision Intelligence

A governed semantic layer the operating cadence runs on.

Most leadership teams don’t have a data problem, they have a decisions problem. Decision Intelligence is the blueprint we use to put a governed semantic layer, an opinionated dashboard pattern library and adoption instrumentation in front of the actual operating cadence. Shaped to drop into Tableau, Power BI, Looker or custom front-ends, and to be handed off operating.

What’s in it

  • Governed semantic layer (dbt / Cube / LookML)
  • Executive, operational and program dashboard patterns
  • Decision provenance and usage instrumentation
  • Ninety-day adoption review and handover

Where it fits

  • Retiring the weekly briefing deck for a live cockpit
  • Reconciling competing metric definitions across units
  • Standing up an operations huddle screen for daily review
  • Embedding analytics inside a customer-facing product
Explore the Data & Analytics practiceReading: Dashboards as decision systems
Agentic AI
Blueprint · 02

Agentic AI

Multi-step agents with tools, guardrails and human checkpoints.

The frontier of useful AI is no longer the chat box. Agentic AI is the blueprint we use to put goal-directed agents, equipped with the right tools, the right context and the right checkpoints, inside the systems where work actually happens. Built around enterprise model endpoints that don’t train on your data, with evals, traces and human-in-the-loop wired in from day one.

What’s in it

  • Planner / executor architectures with tool registries
  • Retrieval, memory and structured-output layers
  • Policy engine and human-in-the-loop checkpoints
  • Evaluation harnesses, traces and red-team suites
  • Enterprise model routing across Azure OpenAI, Bedrock and Vertex

Where it fits

  • Procurement and acquisition workflow agents
  • Tier-1 case triage and routing inside ServiceNow or Salesforce
  • Document-heavy intake, FOIA, benefits, claims, with HITL
  • Research and analyst copilots wired to internal data
Explore the Applied AI practiceReading: Where AI actually fits
Knowledge Intelligence
Blueprint · 03

Knowledge Intelligence

Unstructured content, made operational.

Every program is sitting on a knowledge base it can’t fully use. Knowledge Intelligence is the blueprint for the layer that turns PDFs, forms, contracts and tribal knowledge into structured signal a downstream system, or an agent, can act on. Layout-aware extraction, retrieval with citations, schema-enforced generation, PII handling at the boundary, and a human review queue where it matters.

What’s in it

  • Layout-aware extraction for PDFs, forms and contracts
  • Retrieval-augmented generation with citations
  • Structured generation with schema enforcement
  • PII / CUI detection and redaction at the boundary
  • Knowledge graphs over policy and entity catalogs
  • Human-in-the-loop review queues

Where it fits

  • Procurement and contract intelligence at scale
  • Prior-authorization and form-processing pipelines
  • Knowledge assistants grounded in internal policy
  • Inbox triage with summarization and routing
Explore the Applied AI practiceReading: Where AI actually fits
Forecasting & ML Systems
Blueprint · 04

Forecasting & ML Systems

Models that survive contact with production.

There is a long, expensive distance between a model that works in a notebook and a model an organization can rely on. Forecasting & ML Systems is the blueprint that closes it, a shared feature store, walk-forward back-testing, model documentation aligned to regulatory standards, deployment scaffolding with shadow and canary patterns, and drift and fairness monitoring on day one.

What’s in it

  • Shared feature store with versioned definitions
  • Walk-forward back-testing and calibration harness
  • Model documentation aligned to SR 11-7-style standards
  • CI/CD with shadow and canary deployment patterns
  • Drift, fairness and performance monitoring

Where it fits

  • Productionizing analyst-built investment signals
  • Demand and revenue forecasting with calibrated intervals
  • Customer churn and propensity scoring
  • Capacity and workforce forecasting
Explore the Machine Learning practiceReading: Where AI actually fits
Data Foundations
Blueprint · 05

Data Foundations

The platform the other blueprints sit on.

Nothing else holds without this. Data Foundations is the blueprint for the layer underneath, ingestion, layered storage, tested transformations, orchestration, lineage and observability. Designed for your tenant on Snowflake, Databricks, BigQuery or Synapse, and shaped so the other four blueprints slot in without re-plumbing.

What’s in it

  • Managed and CDC ingestion patterns
  • Bronze / silver / gold layered storage
  • dbt-modeled transformations with tests and snapshots
  • Airflow / Dagster orchestration with SLAs
  • Lineage, catalogs and PII tagging
  • Cost observability and right-sizing

Where it fits

  • Replatforming a legacy on-prem BI estate
  • Standing up a governed lakehouse in a regulated tenant
  • Wiring data contracts between producer and consumer teams
  • Migrating reporting onto a modern warehouse without going dark
Explore the Data Engineering practiceReading: The shape of modern data platforms
Where they fit

Designed for regulated environments.

The blueprints above are shaped for the kinds of environments that don't tolerate hand-waving, where regulators, auditors and compliance teams are part of the work.

  • Financial Services
  • Insurance
  • Public Sector
  • Healthcare
  • Pharmaceuticals
  • Higher Education
  • Energy & Utilities
  • Manufacturing
  • Transportation
  • Retail & CPG
  • Telecom & Media
  • Professional Services
Frequently asked

Questions we hear, answered honestly.

Are these SaaS products?
No. There’s nothing to license. They’re reference architectures and engagement blueprints, deployed inside your cloud tenant on infrastructure you own. Your data doesn’t go anywhere it isn’t already going.
Do you own the code we end up with?
You do. Engagement IP is yours, with a limited reuse license for us to keep evolving the underlying patterns. We spell it out in the MSA.
Can we use just one of them?
Yes. Most engagements draw on one or two. An analytics modernization usually combines Decision Intelligence (for the cockpit) with Data Foundations (for the platform underneath). An applied-AI rollout typically pairs Agentic AI or Knowledge Intelligence with Data Foundations. We compose around the actual problem.
How do you handle AI assurance and model risk?
Every blueprint ships with the assurance spine on the band above , NIST AI RMF mapping, SR 11-7-style model documentation, task-grounded eval suites, red-team probes, decision provenance and PII / CUI handling at the boundary. It is part of the work, not a separate engagement.
Do these work on our existing stack?
In nearly every case, yes. The blueprints are stack-aware patterns, not stack-prescriptive products. They’re designed to run on Snowflake, Databricks, BigQuery, Synapse, on-prem Hadoop or the major cloud-native AI services, we pick the target with you.
What does long-term support look like?
During an engagement we’d operate the blueprint with you and hand over runbooks for ongoing operation. Some teams keep us on an embedded quarterly cadence afterward; others take it fully in-house. Both work.
Continue exploring

Continue with the practice.

What we do
Services

What we do

A focused practice across data, analytics, machine learning and applied AI.

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Representative use cases
Work

Representative use cases

The kinds of engagements the blueprints are built for, grouped by sector and discipline.

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How we think
Insights

How we think

Practice notes and reference architectures on how the practice gets done.

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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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