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AI-Native Executive Intelligence Platform

A digital twin of your business, built for AI.

A live, working model of how your operation runs — assets, processes, costs, rules — that AI can genuinely reason over. Analysis, reporting, planning and simulation all run on that one model.

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Modelled as
FIG.01 — one model of the business. Every workflow runs on it.
Lens 01 · TechnologyCIO · CDO A simpler stack, not another layer Business logic, semantics and reporting are built into the twin — not more products to buy, integrate and govern on top of the warehouse. AI is part of the architecture from day one. How the platform is built → Lens 02 · PerformanceCEO · CFO · COO Finance and operations, one model Cost and performance joined across the value chain. Board packs, budgets, forecasts and scenarios — built on the twin, delivered in PowerPoint and Excel. Reporting, scenarios & planning → Lens 03 · OperationsGM · Planner Simulate the day before you run it Discrete-event simulation on the live twin — daily planning runs, no specialist package, no re-keyed data. Shift reports to board packs from the same model. Simulation & daily planning →
Proven in mining · in build across asset-intensive industries
Mining Energy Utilities Infrastructure Heavy Manufacturing
The problem

Dashboards say what happened. Executives need to know why.

  • Variance reviewWhy did margin drop in this asset class last quarter?
  • Monthly reviewWhat constraint is binding the value chain?
  • BoardIf we approve this capital programme, where does the value show up, and when?
  • TransformationWhich initiatives are actually moving the drivers we said we would move?

A dashboard can show every number in these questions and still not answer one of them. The why lives in connections a dashboard does not hold: costs to processes, constraints to throughput, initiatives to drivers.

Why did margin drop in this asset class last quarter?

Budget $18.4M → actual $12.1M. The twin computes the answer from the drivers themselves, and every number traces to the source data behind it.

Margin variance bridge, budget to actual Budget margin of 18.4 million dollars falls to an actual 12.1 million. Volume costs 2.8 million, grade 1.9 million, logistics 1.6 million and energy 1.2 million; labour recovers 0.7 million and foreign exchange 0.5 million. 18 12 6 0 USD M 18.4 −2.8 −1.9 −1.6 +0.7 −1.2 +0.5 12.1 BUDGET VOLUME GRADE LOGISTICS LABOUR ENERGY FX ACTUAL
  • Budget margin18.4
  • Volume−2.8
  • Grade−1.9
  • Logistics−1.6
  • Labour+0.7
  • Energy−1.2
  • FX+0.5
  • Actual margin12.1
FIG.02 — One ledger question, answered in drivers rather than described.
The shift

Two architectures for AI on enterprise data. Only one is built for reasoning.

Two routes from data to an explained answer From the same source data, two routes. Route A, a semantic layer plus a language model, translates the question and guesses: the path stops short and never reaches the answer. Route B, the digital twin, computes: a single traced path arrives at the explained margin variance. Your data 48,112 rows Semantic layer + LLM ? Guesses Digital twin Computes Margin, explained
Route A

Semantic layer + LLM. Translates your question against a written description, then guesses. The path stops short of an answer you can check.

Route B

The digital twin. Computes. One traced path from the same data to the explained variance.

FIG.03 — Same data, two routes. One stops at a guess. One arrives.
A · WAREHOUSE + SEMANTIC LAYER

Document the business, then hope the AI translates correctly.

Snowflake, Databricks, Microsoft Fabric and dbt attach a semantic layer to the warehouse: KPIs, formulas, relationships, ontologies. A written description of how the business works, for an LLM to translate your question against.

Everything rests on that description being complete and current, and on the translation being right, every query, every time.

A leap of faith: hoped complete, hoped current, hoped right.
B · MANAGEDANALYTICS DIGITAL TWIN

Model the business itself, then let AI reason over the model.

The twin is not documentation written for the AI. It is the working model your reporting, planning and analytics already run on, so updating a report, a driver or an assumption updates the model the AI reads. There is no second description to keep in sync.

Answers are computed, not guessed. You can see the logic behind a number, drill from the board figure to the transaction underneath it, and get the same answer twice.

Your warehouse stays where it is. The twin reads from the systems you already run, and takes the place of the semantic layer and ontology you would otherwise buy, integrate and govern on top of them.

Defensible by construction: causal, deterministic, current.
What the twin connects
  • Assets Facilities, equipment, fleet, IT systems, people.
  • Processes Workflows, policies, operating rules and procedures.
  • Value chains End-to-end flows that deliver product to customers.
  • Constraints Limits on capacity, budget, compliance and risk.
  • Information links How the model carries state and context between them.

Business logic lives in the twin — not rewritten in every report, script and app.

The business twin layer Integrated model of how the business works Data & systems foundation Enterprise data, tables, dashboards AI reasons over this. Stored. Not reasoned over.
FIG.04 — The business twin · the foundation under everything else.

For a CFO, a CIO or a risk committee, this is the whole question: can you show exactly how a number was produced, and get the same number twice? Read: why twin-first AI beats warehouse-first AI

The platform

Understand. Decide. Execute. One twin underneath.

The executive intelligence platform is three pillars built on a living model of your business.

PILLAR 01

Understand

Live performance, explained by the model.

Variance, value drivers and causal explanations, computed from the model of how your business actually works.

  • QUERY-BASED ANALYTICS
  • CAUSAL VARIANCE EXPLANATIONS
  • BASELINE DIAGNOSTICS
Understand PILLAR 02

Decide

Decisions tested against a live model.

Capital and operating scenarios run against real constraints: see where value shows up, and when, before you sign.

  • SCENARIOS & SIMULATIONS
  • FORECASTS & BUDGETS
  • HYPOTHESES ON LIVE DATA
Decide PILLAR 03

Execute

Strategy turned into measurable work.

Initiatives and transformation tracked against the actual drivers in finance and operations, auditable end to end.

  • INITIATIVE & PORTFOLIO MGMT
  • BENEFITS & VALUE ATTRIBUTION
  • REPORTING AUTOMATION
Execute
Executive use cases

What executives actually run on Helm.

USE CASE 01CEO · CFO · COO

Explain margin & production variance

AI variance analysis that traces margin and production movements back to the operational drivers behind them.

View use case
USE CASE 02CEO · CFO · COO

Run board & monthly packs from live data

Board and monthly business review packs generated from the live twin, with narratives grounded in source systems.

View use case
USE CASE 03CEO · CFO · COO

Test capital & operating scenarios

Model capital and operating scenarios against real operational constraints before you commit.

View use case
USE CASE 04CEO · CFO · COO

Track transformation value

Initiative and transformation value tracked against the actual drivers in finance and operations, not a slideware baseline.

View use case
USE CASE 05GM · Planner

Simulate complex processes

Discrete-event simulation of complex processes (bottlenecks, throughput and capacity) run on the same model.

View use case
USE CASE 06Platform modules

Add capability without adding systems

Purpose-built modules on the same twin: decarbonisation reporting that stands up to audit, logistics optimisation for route to market, meeting management.

View modules
Industries

Proven in mining. In build across asset-intensive industries.

Deep capability is built vertical by vertical, for businesses where physical operations, capital, constraints and value chains determine performance.

01 Mining & Metals Deepest build. Pit to port modelled as one twin. Read more → 02 Oil, Gas & Energy Upstream, midstream, integrated. In build · Read more → 03 Utilities & Renewables Generation, transmission, distribution, retail. In build · Read more → 04 Infrastructure Ports, rail, water, airports. In build · Read more → 05 Heavy Manufacturing Steel, chemicals, cement, pulp & paper. In build · Read more →

Not seeing your industry here? Helm can be adapted wherever better business reasoning is needed. If you want to explore a solution for your industry, we'd like to work with you.

Talk to us about your industry
NEW · 3D MINE SIMULATION · UNDERGROUND & OPEN CAST

Mining is the vertical we've gone deepest in: pit to port as one model, replayable shift by shift, level by level. The interactive twin itself — levels, legend, scrub — is rendered live on the mining page.

See the full mining build
6–8wks
Time to deploy

From kickoff to first live use case, measured in weeks, not quarters.

Legacy twin programmes take quarters and often stop at single assets because enterprise-scale modelling was too expensive to build by hand. We use AI to help construct the twin itself, so your first live executive use case runs against real data, real processes and real decisions in 6–8 weeks.

Outcomes

What changes when the twin is live.

Composite outcomes drawn from real deployments — and the words of a CFO who ran a budget cycle in the twin. Customer-specific stories are shared under NDA in a briefing.

In our first budget cycle we ran multiple driver-based scenarios in the twin. The quality of our budget packs and analysis was transformed, and we had full traceability to source data whenever we needed to drill into a number.

CHIEF FINANCIAL OFFICER · TIER-1 MINER
STRUCTURAL VALUE IDENTIFIED IN 8 WEEKSTwin diagnostic across a multi-asset mining group, pit to port — new operating configuration designed.
$60M
TO BUILD AN OPEN-PIT DIAGNOSTICFor a diamond miner — four fleet operating scenarios evaluated.
9DAYS
FASTER BUDGET PREPARATIONFor a mid-tier miner — every number traceable to source data.
3×
FASTER TWIN CONSTRUCTION VS LEGACYAI helps build the twin itself — configure the model, don't re-architect the stack.
5×
See it on your data

What does your business look like as a model?

If your business needs more than descriptive dashboards, we will show you what twin-grounded AI looks like on your own operation.

Request a briefing Read the positioning paper

Under NDAYour data, your environmentAn industry lead, not a rep