2026 AI operating engagements · mid-market

AI that
works for you

Most companies have ChatGPT and Copilot licences and call that an AI strategy. Echelon One installs the operating layer beneath them, so AI lives inside how decisions are actually made, not next to it.

Westpac
ClarkMorgan (China)
Shanghai World Expo
DMG Entertainment
Times Square VR
Faculty Group
Kasu
Wayex
Westpac
ClarkMorgan (China)
Shanghai World Expo
DMG Entertainment
Times Square VR
Faculty Group
Kasu
Wayex

You are using Claude and ChatGPT already. Drafting documents, summarising meetings, getting first-pass answers through the day.

Useful. But the outputs are not stress-tested and they are not integrated into how your business actually decides.

There is a deeper layer of integration sitting one step away. One that rebuilds the cognition layer of the business so AI lives inside how decisions get made, not next to them.

That layer is what we install.

The position this firm operates from
The 2025 market gap
88%
of companies now use AI in at least one business function. Mainstream adoption.
94%
of them report no significant value from the investment.
6%
are capturing the upside. They rebuilt the work around AI. Everyone else bolted it on.
Source · McKinsey, The State of AI in 2025: Agents, Innovation, and Transformation. November 2025.

Built. Shipped.
In production.

The methodology isn't theoretical. It's been deployed inside operating businesses with measurable headcount and cost consequences.

Faculty Group2024
Designed and shipped a custom multi-LLM interface for customer-archetype generation. Built eighteen months before this was a category. Used in production until the underlying LLMs caught up. The build proved the thesis.
Kasu2025
Replaced a four-person development team with one AI-augmented CTO. Replaced a four-person marketing team with an AI workflow. Same output. Fraction of the cost. Currently running in production.
Portfolio2026
Twenty-plus active projects executed as a sole operator. AI is the team.

Indicative ranges, measured against
a pre-engagement baseline.

Featured outcome
3050%
Reduction in internal reporting and briefing cycle time
The single largest unit of cognitive overhead in most mid-market companies. AI does not eliminate it. It compresses it to the residual judgment layer that actually requires a human. In my own businesses the headcount layer has compressed materially further than this. See [02] Track record.
Range consistent with published findings from McKinsey (The State of AI in 2025: Agents, Innovation, and Transformation, Nov 2025), Microsoft (2025 Work Trend Index: AI at Work Is Here. Now Comes the Hard Part, Apr 2025), Anthropic (Economic Index Report, Jan & Mar 2026), and Stanford HAI (AI Index Report 2025).
Decision velocity
Faster decision turnaround on recurring decisions
Investment committee, hiring, vendor selection, customer escalation. The recurring lanes get codified.
Coordination
Less alignment overhead at the exec layer
Fewer meetings to align. More to decide.
Key-person risk
Institutional memory captured
Quality
Decision-quality measurable
Audited against your own pre-engagement decision baseline.
Recurring cognitive work
Document drafting, meeting summarisation, follow-up tracking, research synthesis
The work knowledge teams spend most of their week doing. Re-architected so AI carries the load and humans carry the judgment.

The decision engine
behind the method.

Proprietary to Echelon One. The mechanism that lifts the floor on every decision the operating layer touches.

Anthropic OpenAI Google xAI Alibaba

One frontier model drafts a position. Models from five different labs attack it. It iterates until the argument is exhausted, then names the assumptions that did not survive. Five labs, one model each: different training lineages catch what each other miss. And it is built to halt rather than manufacture a confident answer on a weak foundation. Most AI hands you one confident opinion. The Loop hands you the version that survived an argument between five.

See how the Loop works

Four phases. Fixed deliverables.
Roughly 11–14 weeks end to end.

01
Discovery2 weeks
Interviews with the senior team and key operators. Workflow audits. Decision-pattern mapping. Failure-portfolio extraction. We end with a written diagnostic of where the operating model is leaking and where AI can produce measurable advantage.
Deliverable20–30 page diagnostic. Standalone-usable.
02
Architecture3–4 weeks
Design of the AI operating layer. Voice profile, decision framework, failure portfolio, 90-day rubric, AI autonomy framework, standing-context backbone. The foundational operating documents the company will work from. This is the heaviest IP-creation phase.
DeliverableFull operating-document set.
03
Implementation4–5 weeks
Build of the multi-surface infrastructure. Tool selection, integration, AI system deployment, security and governance. The architecture becomes a live operating system the company can use.
DeliverableWorking operating system.
04
Calibration2–3 weeks + optional retainer
Failure portfolio captured against early use, 90-day rubric set, iteration cadence established with the senior team. Then hand-off. The company runs it from here.
DeliverableCalibrated system + handover.

Better outcomes.
Faster. Cheaper.

Foundational operating documents that compress recurring cognitive work and lift the floor on every decision. Not deck slides. Not theory.

01
An executive voice profile and brand voice that AI tools across the organisation actually follow.
02
A documented decision framework that names which decisions go to humans, which to AI, which run hybrid.
03
A failure portfolio of the company's past bets mapped against the patterns that explain them.
04
A 90-day rolling rubric that replaces wishful OKR cycles with a real check on whether the quarter worked.
05
A multi-surface AI operating layer integrated into the tools the company already uses.
06
An AI autonomy framework defining when AI initiates work, when it asks first, and which actions need human approval.
07
A standing-context backbone so AI is productive in every conversation, not just the ones where someone primes it.

By day 14 you have a real artefact in your hand.
No lock-in.

D 01–03
We talk.
Two structured conversations of roughly two hours each. Senior team, executive sponsor, operating context, current pain. I leave with a clear read on whether the engagement will work, or whether to walk.
D 04–10
Discovery interviews.
Six to ten people in your senior layer plus the operators who actually know how work gets done. Each interview is roughly an hour. I read transcripts overnight. Patterns surface fast.
D 11–14
First-cut diagnostic.
20–30 pages, written document. We meet to walk through it. You push back where you disagree. I revise. If you stop here, you still have a Phase 1 deliverable that is useful on its own.

Three tiers. Fixed-fee per phase.
Quoted against scope.

Tier 01
Diagnostic
2 weeks
Phase 1 standalone. For companies that want the assessment before committing to the architecture.
  • Discovery interviews
  • Workflow + decision-pattern audit
  • Written diagnostic, 20–30 pages
  • Phased proposal for what comes next
Tier 03
Full operating engagement
11–14 weeks
All four phases. Exit with a working operating system, calibrated against real use, with documentation and ongoing-retainer optionality.
  • Everything in Architecture
  • Multi-surface infrastructure build
  • Integration + security + governance
  • Calibration against early use
  • Retainer option

Writing on AI.
At The Lombe Report.

26 May 2026 · Featured
I replaced an eight-person team with AI.
At Kasu, a four-person engineering team became one AI-augmented CTO. A four-person marketing team became an AI workflow. Same business, same output, a fraction of the cost. The methodology, the math, and the 2025 research showing only six percent of companies are getting AI right.
04 May 2026
How to start an AI argument.
Subjecting AI outputs to iterative critique from competing models produces better results than accepting the first answer. Productive disagreement embedded into the AI development process itself.
22 Apr 2026
Get your own AI team.
Inside a multi-agent AI system. A primary chief-of-staff coordinator manages eight specialist agents, each with their own role, voice, and area of authority.
26 Feb 2026
The AI crisis.
South Australia is building elite advanced-manufacturing capability while ignoring the mass displacement of mid-tier knowledge workers already underway through AI automation.

Built and operates the working
operating system this methodology comes from.

Luke
Lombe.
Principal · Echelon One

Founder and operator across Westpac, ClarkMorgan (China), the Australian Pavilion at Shanghai World Expo, DMG Entertainment, Times Square VR, Faculty Group, Kasu and Wayex.

The methodology Echelon One installs is the same architecture behind the eight-person team math at Kasu, the early multi-LLM build at Faculty Group, and the twenty-plus-project sole-operator portfolio Luke runs today. Two years of daily use across operating businesses, not theory. Real artefacts: voice profiles, decision frameworks, 90-day rolling rubrics, failure portfolios, AI autonomy frameworks. Refined through actual operating pressure, not designed in a deck.

The differentiator is not the slides. It is that the working version exists. You can see it. Then you walk through what the corporate version looks like in your business.