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Data, Analytics & AI
Enterprise AI
The operating system for AI at enterprise scale.
An AI control plane — model registry, governance, FinOps, and shared infrastructure that lets dozens of teams ship AI safely on one spine.
The case
At a certain scale,
the platform is the strategy.
Single AI workloads are deliverable by any reasonable team. Twenty AI workloads across seven business units, four countries, and three regulators — that's a different problem.
Enterprise AI is what happens when you stop scaling teams and start scaling the spine. A federated control plane, shared registries, common evaluation, observability, and FinOps — so the next workload ships ten times faster than the first.
What we build
The control plane,
not the workloads.
AI control plane
Centralized model registry, deployment policy, and governance across teams, regions, and regulators.
Governance & risk
Model risk management, bias and fairness eval, audit logging, and regulator-ready evidence pipelines engineered in.
FinOps for AI
Per-model cost observability, inference budgets, capacity planning, and unit economics tied to product KPIs.
Shared platforms
Reusable feature stores, vector platforms, prompt registries, and eval harnesses that compound across teams.
Federated operating model
The org design — central platform, embedded AI engineers, federated stewardship — that makes the spine work.
Evaluation & assurance
Continuous evaluation, challenger models, and an evidence pipeline that proves — to risk, audit, and the board — that workloads still behave.
The control plane
One spine,
many workloads.
We design the control plane to be opinionated where it pays back and pluggable everywhere else.
Workload layer
Layer 01Independent AI workloads owned by product teams.
Platform layer
Layer 02Shared infrastructure, opinions, and SDKs.
Governance plane
Layer 03Policy, risk, and audit — federated.
Operations plane
Layer 04FinOps, observability, and capacity at platform scale.
Stacks we work with
Components, not megasuites —
each replaceable on its own clock.
At enterprise scale, the cost of a megasuite that doesn't fit one of your business units exceeds the integration tax of a thoughtful component stack. We design for replaceability across every layer.
Foundations
Where the workloads run. We design for portability across the cloud-AI platforms — never bet the operating model on a single vendor's roadmap.
Registries
One source of truth for what's in production. Often the first hard architectural line we draw on the way to scale.
Governance
Audit trails, model risk management workflow, and bias eval as platform infrastructure — not a per-team rebuild that quietly diverges across business units.
FinOps
Per-workload cost telemetry. Without it, the second-order effect of GenAI ambition is a surprise invoice — usually 90 days after the executive demo.
Outcomes we engineer for
What a working
spine pays back.
10×
Workload velocity
Increase in AI workloads shipped per quarter once the control plane is operational.
−40%
Per-workload cost
Typical reduction in unit cost per AI workload when shared platforms replace per-team rebuilds.
Audit-ready
From day one
Every workload inherits the governance posture of the spine — not built ad-hoc per team.
1 model
Of truth
One registry, one eval harness, one prompt source-of-truth across the enterprise.
Where this applies
When you have many AI
workloads, not one.
Enterprise AI as a discipline becomes essential past 8–10 production workloads — the math on shared infrastructure tips heavily by then.
- Banking & Capital Markets
- Insurance & Reinsurance
- Healthcare Payers & Providers
- Pharma & Life Sciences
- Telecom & Media
- Manufacturing (multi-plant)
- Energy & Utilities
- Public Sector & Sovereign
- Consumer Goods (multi-brand)
- Hospitality (multi-property)
- Retail (multi-banner)
- Higher Education
Common questions
At enterprise scale,
what teams ask.
When do we actually need a control plane?
Past 8–10 production workloads, the marginal cost of shared infrastructure starts paying for itself within a quarter. Below that, you're better off with disciplined templates.
Can we start federated, or must it be central?
We almost always start federated — a thin central platform with embedded teams. The platform earns its right to centralize specific concerns over time, not by mandate.
How do you handle multi-vendor model strategy?
Routing and fallback are first-class. The model layer is replaceable; teams shouldn't have to refactor when a new model lands.
Related in Data, Analytics & AI
Adjacent capabilities
in this practice.
Data Strategy & Consulting
Capability audits, target-state architectures, and a sequenced investment thesis tied to operating KPIs.
Data Lakehouse & Warehouse
Lakehouse on Databricks, Snowflake, or BigQuery — engineered for governed scale and downstream AI.
Advanced AI & Analytics
Forecasting, segmentation, and anomaly detection wired into operational workflows.
Start the conversation
From scattered workloads
to one operating spine.
We'll start with the workloads you already have, find the highest-leverage shared platform, and build out from there.