Why ManagedAnalytics

Built differently. On purpose.

Most enterprise AI starts with a warehouse and bolts a semantic layer on top. We started with the model of the business. The architecture is different — and the kind of question we can answer is different as a result.

Four reasons

What makes ManagedAnalytics structurally different.

REASON 01

Twin-first architecture

The digital twin is the foundation, not an afterthought. AI reasons over the model of the business — assets, processes, value chains, costs, drivers, constraints, rules. The result is causal, defensible reasoning instead of pattern-matching over a warehouse.

REASON 02

AI-accelerated build

Twins were historically a multi-quarter expert exercise. We use AI to construct the twin itself, collapsing months of work into weeks — typically 20% of the time and effort of legacy approaches. The cost barrier that confined twins to single assets is gone.

REASON 03

Modular composability

One twin underneath, seven modules above. Customers start with Core Analytics plus a use-case module and add modules as new use cases come online. No re-procurement. No re-architecture. Every module compounds the value of the twin.

REASON 04

Executive lens, by design

The product is shaped by the questions executives actually ask: variance, value drivers, capital, scenarios, initiatives, board packs. Not a generic analytics platform retrofitted for the C-suite — built for it from the first commit.

How we compare

Where the alternatives stop, and where we start.

Most enterprise AI fits one of four shapes. Each is useful for what it does. None is the executive intelligence layer that asset-intensive businesses need.

What the executive asks
Helm
BI / dashboards
Power BI, Tableau
Warehouse + AI
Snowflake, Databricks, Fabric
OKR / EPM
Anaplan, Quantive
"Why did this number move?"
Yes — causal
Description only
Description only
Not in scope
"What would happen if we pulled this lever?"
Yes — twin-simulated
No
No
Spreadsheet-shaped
"Which initiatives are actually moving the drivers?"
Yes — attributed
No
No
Asserted, not computed
"Where is the value chain binding right now?"
Yes
No
No
No
"Is this output defensible to the board?"
Yes — full lineage
Manual lineage
Partial
Manual lineage
Time to first executive use case in production
6–8 weeks
Reports, not decisions
6–12 months
3–6 months
Reasoning substrate
A model of the business
A chart
A table
A goal hierarchy

Comparison reflects Helm's positioning. Individual vendors will differ on specific capabilities; we are happy to walk through head-to-head detail in a briefing.

Who we are for

Asset-intensive businesses where the model matters.

CFOs, CEOs, COOs, Chief Strategy Officers, and Chief Transformation Officers in mining, energy, utilities, infrastructure, and heavy manufacturing — typically $500M+ in revenue, with operations complex enough that no single dashboard, deck or spreadsheet captures the whole business.

Who we are not for

Light-asset businesses with simple value chains.

If a SaaS analytics tool, a BI dashboard, or a spreadsheet model captures your business well — keep using it. The leverage of a twin only shows up when there is something physical, capital-heavy, or structurally complex to model.

See where the structural difference shows up.

A briefing typically takes 60 minutes and walks through how ManagedAnalytics compares to whatever you have today.