A strategy that links decisions, data, and measurable business outcomes - and scales without runaway cost or risk.
Most organizations don’t struggle with tools - they struggle with clarity. A true Data & AI Strategy connects decisions to data, maps where value will appear on the P&L, and defines how to deliver it safely and at scale.
At Fornax, we design strategies that combine decision roadmaps, platform blueprints, and governance frameworks into one operating model leaders can trust. Our approach blends business and technical realities: investment cases that stand up in the boardroom, operating models that teams can execute, and controls that balance speed with compliance. The result: a data and AI foundation that accelerates decisions, reduces waste, and creates repeatable value at scale.
Where will AI impact our business first?
What capabilities do we need now, and which can wait?
How do we govern risk without slowing delivery?
How much will this cost - and how do we prove the ROI?
What’s the right operating model to keep us fast and in control?
Clarity on which decisions matter most, the KPIs to track, and where AI creates lift.
Modern architecture patterns (data products, warehouse/lakehouse, real-time vs batch, observability) that fit your reality.
A framework for domains to publish trusted, reusable data and features with contracts and SLAs.
Roles, responsibilities, funding, and portfolio cadence to avoid scattered pilots and ensure scale.
Built-in privacy, safety, and compliance guardrails that keep delivery fast and auditable.
A clear line of sight from investment to impact, including cost-per-decision, reuse savings, and ROI.
How we build your strategy
Define Value & Decisions
Anchor on critical business choices, map KPI trees, and identify signals needed.
Assess Readiness & Risks
review data quality, platforms, skills, and compliance posture; highlight blockers early.
Design Architecture & Products
Shape the blueprint for pipelines, data products, and AI models that scale.
Set the Operating Model
Align ownership, workflows, and governance so delivery is fast but accountable.
Plan Economics & Roadmap
Align investment with outcomes, optimize for reuse, and chart the path to scale.
True cost view
We model the full lifecycle of AI and data investments - from data preparation to platform usage and ongoing operations - so leaders avoid hidden expenses.
Decision-level economics
Instead of abstract ROI, we measure cost and value per decision supported by AI. This makes business cases defensible at every level.
Reuse as leverage
Shared components, features, and governance reduce incremental cost with each new use case. Your strategy compounds value, instead of multiplying costs.
Policy by Design
Safety, compliance, and security are engineered into workflows, not bolted on at the end.
Adaptive controls
High-risk areas get stronger oversight; low-risk flows stay automated and fast.
Audit readiness
Every data source, model version, and outcome is logged for transparency.
Evolving with standards
Your strategy stays aligned with global frameworks and regulatory changes.
Explore All Capabilities
Strategy and Transformation
We help leaders build strategies that don’t sit in decks, but those that scale, adapt, and deliver measurable value.
Data Foundation
A modern data foundation gives you one source of truth for analytics, AI, and decision-making - engineered for reliability, speed, and scale.
Advanced Analytics & Insights
We build analytics platforms and production models so leaders make faster, confident decisions at scale.
AI / ML Innovation
From robust AI engineering to production-grade LLM solutions and ML platforms, Fornax turns experimentation into scalable impact.
Start by anchoring each model to one decision and its KPI. Prove uplift with experiments or causal methods, then scale via shared components (features, evaluation, observability). This converts scattered pilots into a managed portfolio with compounding reuse and lower unit costs. (Leaders who report material gains typically rewire processes and scale on common platforms.)
Choose the pattern that matches decision latency, data shape, and governance. Many enterprises run a pragmatic mix: curated warehouse layers for finance/BI and lake-style zones for semi-structured/real-time and LLM grounding. The strategy defines which decisions live where, how data is productized, and how evaluation/observability span both. (The winning move is consistency of standards, not one logo.)
Treat cost as an engineering constraint: tier models by task complexity, cache aggressively, batch/stream where appropriate, and monitor unit metrics (cost per decision/request). Bring FinOps into AI planning so budgets reflect reality (training vs. inference, GPU vs. API, data/storage). This is how leaders scale adoption without budget shocks.
Most settle on a hybrid: a central platform & governance core with empowered domain teams that own decisions and data products. The key is clarity on who owns outcomes and how teams request/extend shared services. This preserves speed locally while avoiding fragmentation.
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