Many companies build AI one project at a time: one vendor for data labelling, another for the model, another for deployment, and maybe an agency for the website. Every new project starts with a new brief and a new team learning the business from scratch.
Our longest engagements work differently. The same people stay on the account, and when the same engineers stay, the knowledge stays too. Here is what we have learned from them.
1. The cost of re-hiring for every project
Hiring a new vendor for every project has costs that do not appear on the invoice:
- Re-briefing: every new team has to learn your product, data and rules again.
- Lost context: the lessons from the last project leave with the last team.
- Slower starts: weeks are spent onboarding before real work begins.
2. Handover boundaries are where things break
A typical AI product involves data labelling, model training, the core pipeline, deployment onto hardware, and the product or website on top. That is often four different suppliers. Every handover between them is a place where:
- Scope gets unclear (“that is not our part”)
- Accountability disappears
- Small misunderstandings become big problems
3. Sorted Tech: one team for the complete stack
Sorted Tech is our longest-running engagement, running since 2023. A dedicated team is responsible for their complete development stack:
- Data labelling
- Model training
- Core pipeline development
- Edge deployment
- Ongoing maintenance of their website
Because one team covers everything from labelling to edge deployment, there are no handover boundaries where scope and accountability usually get lost. And because the same team maintains the website, the product and the models behind it move together.
The result is one team accountable for the full stack, from the labelled frame to the deployed model to the live site.
4. MiniAi Live: moving with the technology
MiniAi Live, in biometrics and identity, needed AI engineering capacity that could grow with their roadmap rather than a fixed statement of work.
Over three years, the technology itself changed. The work started in computer vision and moved towards LLMs and agentic AI as the field shifted. A supplier who could only do one of those would have had to be replaced.
We embedded AI engineers into their team rather than delivering against a specification from outside, so priorities could shift without renegotiating scope. As the work moved from vision to LLMs and agents, the same engineers carried the product context with them instead of it being re-learned.
5. When a dedicated team makes sense, and when it does not
A dedicated team is a good fit when:
- AI is a core part of your product, not a one-off experiment
- The work spans several stages: data, models, deployment, product
- Your roadmap is likely to change
- You want knowledge to build up over time
A single project engagement may be better when:
- You have one clearly defined, short piece of work
- You are still testing whether AI is useful for your business
Many of our engagements start as a defined project and become a dedicated team once the value is proven.
6. How to set up a dedicated AI team well
- Give them ownership, not just tasks: a team responsible for the whole stack makes better decisions than one handed tickets.
- Keep the team stable: the value comes from people staying and building context.
- Embed them in your process: shared planning and communication beat monthly reports.
- Let scope follow the roadmap: the point is flexibility, so do not lock them into a fixed specification.
Looking for an AI engineering team that stays with you? Let’s talk.