Most AI strategies fail for one reason: they skip the foundation. DataOps builds AI strategy and implementation the way it actually works, governed data products first, then the LLMs, agents, and control towers that act on truth instead of assumptions.
Where AI creates value in your business, what your data can support today, and what has to be built first. An honest sequence, not a wish list.
Which models, for which use cases, in what order, and a way to score vendor pitches against your requirements instead of their demos.
Retrieval-augmented generation grounded in your governed data, including knowledge graphing and ontology, so answers trace back to sources you trust.
Specialized agents with defined roles and defined data access, designed as a system. In active design today for a $40B enterprise.
The governed data architecture that makes control towers, demand forecasting, and digital twin simulation possible, built across Demand, Supply, Risk Management, and Advanced AI pillars.
AI wired into the tools your teams already use, automating the workflows between systems instead of adding another silo.
Build the foundation once and each of these becomes an extension of the same architecture, not a new project from zero.
Forecasts built on certified data products, so planning decisions rest on numbers the enterprise actually trusts.
One governed view across Demand, Supply, Risk Management, and Advanced AI pillars.
Agents that surface supply and market risk from governed data before it reaches the P&L.
Model the operation and test decisions in simulation before committing capital in the real world.
Governed data products packaged into services your customers pay for, turning the foundation into a revenue line.
For a $40B aerospace and defense supplier, the strategy phase delivered the AI layer on top of a governed foundation: a supply chain control tower spanning Demand, Supply, Risk Management, and Advanced AI pillars, a multi-agent architecture, and a Copilot Studio agent strategy with five scoped use cases and a 90-day deployment roadmap.
It starts with readiness and strategic alignment: where AI creates value in your business, what your data foundation can support today, and what has to be built first. From there we produce a concrete roadmap. For a $40B aerospace and defense supplier, that meant a supply chain control tower design, a multi-agent architecture, and a Copilot Studio agent strategy with five use cases and a 90-day deployment roadmap, delivered as a 3-month strategy phase inside a 12-month transformation.
No. Order matters. Every AI system we design consumes only governed, certified data products. Build the foundation first and agents act on truth. Skip it and they act on assumptions, which is why pilots that demo well die in production. The right sequence is a governed data foundation first, then the AI layer on top.
A multi-agent architecture is a system design where multiple specialized AI agents, each with a defined role and defined data access, work together on a business process instead of one general-purpose model doing everything. We have a multi-agent architecture in active design for a $40B enterprise, sitting on structured tables and views built specifically for LLM and agent consumption.
With a governed foundation in place, quickly. For a $40B aerospace and defense supplier, we scoped a Copilot Studio agent strategy with five use cases and a 90-day deployment roadmap. The broader pattern is a 12-month full transformation: a 3-month strategy phase followed by 9 months of implementation.
Tell us where your AI ambition stands today. We'll show you the sequence that gets it to production, and how fast the first agents can ship.
