Service Offering
AI Accelerator

Turn AI possibilities into evidence.

Larrison's AI Accelerator helps organisations move quickly from interest and uncertainty to tested ideas, informed decisions and a practical way forward.

TALK TO US

Beyond the AI workshop

Many organisations can identify dozens of possible applications for AI. The harder questions are which opportunities are worth pursuing, what will work in the organisation's real environment and what is required to deliver them responsibly.

Our accelerators bring the right people together to investigate those questions through practical work. We combine discovery, service design, technical experimentation and rapid engineering to test the most important assumptions early.

See it sooner. Shape it together.

Our prototypes give users a genuine voice in the direction of the solution from the outset. This reduces the risk of delivering something that meets documented requirements but does not work well for the people expected to use it.

terminal Prototype.v1
STATUS EVIDENCE ACQUIRED

What we do in the Accelerator

A structured, time-boxed approach to discovering, validating, and planning AI initiatives.

search

Strategic discovery

Work with leaders, subject-matter experts, users and technical teams to identify problems AI may be able to address. Opportunities are assessed against user value, organisational benefit, feasibility, information readiness, consequence and implementation effort.

sort

Use-case prioritisation

Turn a broad list of possibilities into a defensible set of priorities, focusing effort where AI can create meaningful value and avoiding investment in poorly defined applications.

build

Rapid, AI-assisted prototyping

We use AI to accelerate design and engineering, allowing us to move from an initial concept to something users can experience in days or weeks rather than months.

fact_check

Technical validation

Investigate architectural options, model and platform choices, integration requirements, data dependencies, security constraints and likely operating costs.

gavel

Responsible AI and assurance

Consider privacy, security, transparency, accessibility, human oversight, bias, information handling and the consequences of error in the context of the proposed service.

route

Production pathway planning

Determine what it would take to move a successful prototype into operational use, including architecture, data, integration, security, governance, support and delivery requirements.

Prototype to evidence

Make the idea tangible - early

The goal is not to make an unfinished concept appear complete. It is to create just enough of the solution to support meaningful participation and better decisions.

It is difficult to evaluate a new service from a presentation, requirements document or abstract discussion alone. People make better decisions when they can see an idea, interact with it and understand how it might work in practice.

Larrison uses AI-assisted design and engineering to rapidly turn an early concept into something tangible. Depending on the problem, this may be an interactive mock-up, a functional prototype, a simulated workflow or an early version using representative information.

Rather than waiting until every requirement has been documented, we put an initial experience in front of users and stakeholders as early as possible. They can explore it, challenge it and help shape it while the solution is still inexpensive and easy to change.

A fast, practical feedback loop

Idea to informed decision
01

Make it visible

Turn the initial idea into an experience people can see and use.

02

Put it in users’ hands

Observe how people interpret it and where it supports, or fails to support, their work.

03

Learn quickly

Identify unmet needs, incorrect assumptions, risks and technical constraints.

04

Refine together

Evolve the prototype with users, business owners and technical stakeholders.

05

Build the evidence

Determine whether the idea is valuable, feasible and ready for further investment.

Method

A typical engagement

  1. 01

    Frame

    Define the problem, desired outcomes and operating constraints.

  2. 02

    Explore

    Understand users, workflows, information and candidate opportunities.

  3. 03

    Prioritise

    Select the assumptions and use cases most valuable to test.

  4. 04

    Prototype

    Build enough of the solution to generate meaningful evidence.

  5. 05

    Evaluate

    Test usefulness, feasibility, performance, risk and user response.

  6. 06

    Plan

    Provide recommendations and a practical pathway for the next stage.