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What We Do
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About Us
Move beyond isolated AI experiments to build scalable, domain-driven systems with AI-ready architectures.
Enhance valuation and accelerate ROI through due diligence, tech migrations, and strategic restructuring.
Control cloud costs, streamline data, and focus investments on high-value, scalable solutions.
Streamline operations and improve decision-making by aligning your ERP systems with how your business actually works.
Reduce risk and increase resilience by closing the security gaps hiding in your architecture and workflows.
Optimize operations, restructure teams, and modernize technology to maximize the value of digital investments.
Deliver mission-critical software in production-ready increments that create measurable value early.
Reduce risk and complexity with every cycle, turning legacy systems into platforms that can evolve.
Management Consulting
Software Engineering
Product & Strategy
AI
Security
Cloud
Data
Talent
The belief that shaped us — and how it evolved into LiminalArc, the natural next step in our journey.
Our team of over 100 experts spans the nation, from consultants and technical architects to product specialists.
We are looking to hire mature, pragmatic consultants who are deeply passionate about meaningful change.
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What AI Looks Like at Scale in a Multi‑Unit Enterprise
Beta This model is still under development.
Most large companies already have AI. A copilot in one division, a pricing model in another. The pilots work. The scale doesn't, because every unit runs on its own systems and its own version of the data, so nothing built in one place can run in the next.
This is our picture of what has to change for AI to scale. Business units at the top, placing a short list of bets close to the work. A shared run layer in the middle that takes over what gets proven and runs it for every unit. And underneath, the systems and data you already have, organized around what the business does, with data owned at the source. Build once. Deploy everywhere it applies.
Each element shows two things: how it usually works today, and how it works in this model.
The arrows show how work moves. Hypotheses go down to be tested, proven solutions move into the run layer, and reusable services come back up to every unit.
Hover any element for the full version. Then switch to CEO, CIO, CFO or COO to see why that seat should care: the enterprise story, the control story, the cost story, the operations story.
FROM: Every unit inventing its own AITO: Units running experiments on a shared system
FROM: Every solution its own islandTO: One layer that owns what's proven
FROM: Capabilities buried under productsTO: Harmonized, built once, shared
None of this is a rebuild. Almost every box on the diagram is something a large company already has. The change is where it sits, who owns it, and whether the next unit has to build it again. That is the whole difference between AI that works in one place and AI that works everywhere it applies.
The eBook walks the same model end to end, one layer at a time. It does that from the CEO, CIO, CFO and COO seats, so you can see what changes for yours.
Get the eBook