Applied Reason

Applied ReasonIndependent engineering practice

Engineering experience.
Amplified by AI.

We combine decades of engineering, systems and commercial experience with modern AI to identify the real problem, design the right architecture and deliver systems that work in practice.

Start a conversation →Explore our approach ↓
Inputs + Applied judgement → Outcomes

01 / What we do

From complex problems to working systems.

We design and implement AI systems, custom software and operational workflows around the problem—not the technology.

That means understanding the constraints, choosing a workable architecture, integrating it with existing operations and proving that it performs as intended.

01

AI Systems Engineering

⌁

Agents, intelligent systems, knowledge retrieval and AI integrations engineered for dependable use within real technical and operational environments.

02

Custom Applications

›_

Purpose-built applications, internal business tools, platforms and API integrations designed around the way your organisation actually works.

03

Intelligent Workflows

□—□

Automation for complex operational processes, combining AI agents, software and appropriate human oversight at the points that matter.

04

Talent Intelligence

♙♙♙

Talent pools, candidate discovery, assessment tools, recruitment systems and hiring workflows built for technical roles and informed decisions.

05

Technical Advisory

◯◯

Independent guidance on AI architecture, technical feasibility, systems engineering and implementation planning before significant time or capital is committed.

03 / Built, not just proposed

Real systems.
Real engineering.

AI-Powered Talent Intelligence Infrastructure

Applied Reason’s work includes integrated recruitment systems that connect candidate and job data, structured processing, AI-assisted evaluation, human decisions and downstream recruitment actions.

The architecture below shows the functional pattern without exposing candidate data, client configuration or proprietary implementation details.

System scope

  • ATS and candidate databases
  • Candidate discovery and enrichment
  • Resume parsing and structured evaluation
  • AI-assisted candidate assessment
  • Agent orchestration
  • Human review and approvals
  • Recruitment workflow automation
  • External systems and API integration
Illustrative talent intelligence architecture Candidate and job data flows through processing and enrichment, AI evaluation, human review and recruitment actions. ATS, candidate databases and external APIs connect at the input and action layers. CANDIDATE AND JOB DATAATS · DATABASES · JOB REQUIREMENTSPROCESSING AND ENRICHMENTDISCOVERY · PARSING · STRUCTURINGAI EVALUATIONASSESSMENT · ORCHESTRATIONHUMAN REVIEWDECISIONS · APPROVALSRECRUITMENT ACTIONSWORKFLOWS · EXTERNAL SYSTEMS · APIS INPUTSOUTPUTS
Illustrative architecture — functional relationships only. Sensitive data and proprietary implementation details are omitted.

04 / Who we help

Built for builders.

For organisations building products, running technical operations or managing complex work—and needing systems shaped around their constraints rather than another generic software subscription.

A / 01

Engineering and product companies.

Teams developing physical or digital products that need AI and software grounded in real engineering constraints, product lifecycles and technical decision-making.

A / 02

Technology businesses.

Product, platform and software teams that need to integrate AI, develop a specialised application or turn a promising prototype into a dependable system.

A / 03

Operations-led organisations.

Businesses with complex workflows, specialist knowledge or disconnected tools that need a custom system instead of another off-the-shelf subscription.

05 / Founder

Experience
×
AI

Founded by an engineer.
Built on experience.

Lance Harvie

Founder & Principal AI Systems Architect

Lance holds a degree in Microelectronics and Information Processing. His engineering background covers embedded systems, software development, complex systems architecture and the disciplined debugging required when hardware, software and operations interact.

He also brings more than two decades in technical recruitment as the founder of RunTime Recruitment, alongside experience in business operations and commercial problem-solving. He now applies that combined perspective to AI-powered operational and recruitment systems. Engineering-led problem solving starts with the real constraint, designs the whole system and validates the outcome. Generic AI implementation starts with a tool.

06 / How we work

Reason first.
Then build.

  1. 01

    Understand the problem

    Define the real need, the operating context and the technical and commercial constraints.

  2. 02

    Design the system

    Choose a workable architecture, establish responsibilities and identify where AI adds practical value.

  3. 03

    Build and integrate

    Develop the software, connect the required tools and data, and keep human oversight where it belongs.

  4. 04

    Validate and improve

    Test the system against real use, measure what matters and refine it with evidence.

07 / Start here

Bring us an engineering or
business challenge worth solving.

Start a conversation →