A readiness score
The assessment scores the work, data, tools, risk and people behind each candidate workflow, and shows why each one was ranked, delayed or rejected.
AI work evidence
Evidence from the LOKAL AI practice.
An AI pitch is easy to write. The useful question is what your team can inspect afterwards. This page shows the record behind the practice and what each engagement leaves behind.
The shape of a useful decision
01A readiness score
02A workflow map
03An architecture decision
What can you check before you hand us AI work?
LOKAL has operated since 2017 and has worked with 250+ organisations.
The practice holds the Claude Certified Architect – Foundations credential, and its founder is a member of the OpenAI Champions Network. Every AI engagement leaves a record your team can inspect: a readiness score, a workflow map, an architecture decision and a training record.
What an engagement leaves behind
For buyers who want to see what an AI engagement produces before they commit to one.
A workflow map
The first workflows, drawn from the starting input to the action they support, with the owner, the human check and the exceptions named.
An architecture decision
The tool and model choice for each workflow, explained against your data, security needs and existing licences, so your team can review it before the build.
A training record
Who was trained, on which tasks, and what they agreed to try next at work. The record is what the 30, 60 and 90 day reviews measure against.
How an engagement runs
Each step leaves something the next owner can inspect.
- 01
Assess
Find the work with a real owner, usable inputs, manageable risk and a result your business can recognise.
- 02
Prioritise
Choose a small first wave. Record what is ready, what needs a prerequisite and what should not become an AI project.
- 03
Build and enable
Implement the workflow, the human check, the guidance and the training together.
- 04
Review
Measure use and output quality, handle exceptions and expand only after the operating pattern holds.
The record behind the practice
What you can verify, with the edges still attached.
Since 2017
An established operating company
LOKAL has run client work from Sydney and Manila since 2017, before AI services were a category.
250+
Organisations since 2017
The count covers all LOKAL services, not AI work alone.
CCA-F
Claude Certified Architect – Foundations
Held in the practice. The founder is also a member of the OpenAI Champions Network. Neither credential is an endorsement of a client engagement.
Before the scope
What this evidence does not claim
- A credential proves a credential. It does not prove a client outcome.
- Every figure appears with its period and scope, or not at all.
- A client logo, photo or private reference is used only within its permission.
- An assessment can conclude that no workflow is ready to build yet.
Questions about our evidence
Clear expectations make a better brief.
Can LOKAL provide a private AI reference?
Possibly, where the client has permitted it and the proposed work makes the request relevant. A private reference is arranged deliberately and does not imply permission to publish the relationship.
What evidence comes with an AI readiness assessment?
You receive the inspected inputs, the scoring logic, the ranked workflow map, the open questions, the gap register and the proposed sequence. The evidence shows why a workflow was ranked, delayed or rejected.
Do credentials guarantee implementation quality?
No. Credentials show defined training or assessment capability. Scope quality, architecture decisions, QA, user adoption and operating outcomes still need evidence from the engagement itself.
Has LOKAL published AI search research?
Yes. In April 2026 LOKAL published a channel comparison across roughly 2.5 million hotel sessions: visitors from AI assistants had an 85.2% engagement rate, against 77.3% for organic search. AI referrals were under 0.1% of sessions, and the comparison does not show causation.
A useful first brief
Bring one workflow that is slow, expensive or hard to control
We’ll look at the work, systems, data and risk around it.
If an assessment is premature—or AI is the wrong answer—we’ll say so before proposing a programme.
The brief