An AI engineer in your team, without hiring one.
We scope the work with you. Then one of our engineers joins your team one to three days a week to build it.
For New Zealand businesses of 20 to 100 staff with no AI function yet.
- MonYour team, as usual
- TueYour engineerStandup, then building
- WedYour team, as usual
- ThuYour engineerShips the next item on the list
- FriYour team, as usual
Blake leads the engagement and reports to you monthly.
How it works
- About 2 weeks
Scope
Blake maps your systems and turns your ideas into a prioritised build list. If you don’t need an engineer, you’ll hear that here.
- 1-3 days a week, 3 month minimum
Embed
Your engineer works inside your team and tools, shipping off the list and reprioritising with you as things change.
- Final 2 weeks
Hand over
Everything documented. Then run it yourselves, move to managed support, or hire full-time.
The scope is paid, because it’s what lets your engineer turn up with a plan instead of spending a month learning your business.
What you get
- A named engineerIn your standups and chat, working in your systems.
- A build list from day oneWeek one is building, not discovery.
- Blake as engagement leadRegular check-ins and a monthly written report.
- Your team upskilledThe know-how stays in the business.
- A clean exitEverything lives in your systems, documented.
- Xero
- simPRO
- ServiceM8
- PayHero
- Shopify
- CartonCloud
- Buildxact
- Unleashed
- Site App Pro
- Outlook
Who you’ll work with
“We engaged Harkness AI to build an internal AI-driven quality assurance platform, and Alex has been excellent to work with throughout. He picked up a fairly niche operational problem quickly, turned it into a workable technical design, and delivered in steady increments rather than disappearing for weeks at a time. Communication was clear and honest, including when something turned out harder than first scoped. The platform is now in testing with our team and doing what we asked of it. Easy to recommend if you want practical AI implementation rather than theory.”
Straight answer: our engineers are early in their careers. They’re backed by a scoped build list, connectors we’ve already built and Blake overseeing the work, and you meet your engineer before they start. Meet the team.
Is it the right fit?
Yes, if
- You have 20 to 100 staff, and nobody’s job is AI
- There’s a list of things you want built
- The work spans several systems
Something else fits better if
- You have one job to do: a single build or Claude setup
- Your team needs to learn the tools: training
- You need a senior specialist for production machine learning: hire one
How it compares
Against hiring, an agency or a fractional Chief AI Officer.
| Embedded AI engineer | Full-time hire | AI agency | Fractional CAIO or advisor | |
|---|---|---|---|---|
| How you pay | A paid scope, then a fixed monthly fee | Salary, often $130,000+, plus recruitment | Project fee, often five or six figures | Monthly advisory retainer |
| Commitment | 3 month minimum, then monthly with a month's notice | Permanent employment | Fixed scope per project | Monthly retainer |
| Who builds | Your embedded engineer | Your hire, if you find the right person | The agency, outside your business | Usually nobody - strategy, not builds |
| Speed to start | About 2 weeks | Often months | Weeks of scoping first | Quick |
| Knowledge kept | High, documented at handover | Highest | Low, mostly leaves at handover | The strategy, little else |
| Best for | 20-100 staff with a real build list | A full week of AI work, every week | One large, well-specified build | Strategy and governance before building |
Embedded engineer vs hiring an AI engineer
A full-time hire is the strongest long-term option once AI work fills a week, every week. Until then you pay $130,000+ in salary, wait months to recruit, and risk hiring before the work is defined. A common path: start embedded, let the real workload show itself, then hire - and we can help with that hire.
Where a full-time hire wins
- Highest knowledge retention - context, code and capability live in-house permanently
- Full-week availability and ownership, including support for production systems
- Best long-term economics once there is genuinely a full week of AI work
Where a full-time hire falls short
- Salaries often exceed $130,000 in NZ, before recruitment costs and overheads
- Recruitment typically takes months in a market where AI engineers are scarce
- Expensive if the AI workload is not yet defined - you pay full-time rates during discovery
- You carry the risk of choosing the wrong person
Embedded engineer vs an AI agency
An agency gives you parallel capacity for one large, clearly specified build, often with formal procurement. The trade-offs are cost and continuity: you fund overheads, the people who pitch aren’t always the people who build, and most of what they learn leaves at handover. For a steady stream of smaller builds, an embedded engineer fits better.
Where an agency wins
- More hands - a team can deliver a large build faster than one engineer
- Structured delivery with project management, QA and documentation baked in
- Fits enterprise procurement: formal contracts, fixed scope and tender processes
Where an agency falls short
- Higher total cost - you fund account management and overheads, not just engineering
- The people who pitch are not always the people who build
- Knowledge mostly leaves at handover unless you pay for ongoing support
- Changing scope mid-project is slower and costlier than with an embedded engineer
Embedded engineer vs a fractional Chief AI Officer
A fractional Chief AI Officer or advisor helps leadership with strategy, governance and a roadmap, but most don’t build. An embedded engineer is the other half: the scope gives you the plan, someone in your team ships it, and Blake keeps an eye on direction the way an advisor would.
Where a fractional CAIO wins
- Strong on strategy, governance and board-level conversations
- Useful before you know what to build, or when AI risk is the main question
- Light time commitment from your leadership team
Where a fractional CAIO falls short
- Most do not build - the roadmap still needs someone to deliver it
- Advice without delivery can stall once the workshop energy fades
- You may end up paying for an advisor and a builder separately
Need a full-time hire instead?
We find and vet candidates through our network, including Young Kiwis in AI, and give you a shortlist of up to three. One flat finder’s fee, paid when your new engineer starts.
Questions
Start with a scoping call
Tell us what you want built and which systems it touches. If an embedded engineer isn’t the right fit, you’ll hear that on the call.
Last updated 19 September 2026 - simplified the page to the key points and added help hiring a full-time AI engineer