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AI consultants or an in-house AI team: which should you start with?
Hire an outside AI team when you need working systems this year and lack the people who have shipped them before; build an in-house team when AI is core to your product or you already run a strong engineering function. The strongest pattern combines them: outside engineers build the first production systems alongside your staff, then hand them over to the people who will run them.
Featuring insights fromShaun Devan · Founder & CEOUpdated · 3 min read
The short version
| Outside AI team | In-house AI team | |
|---|---|---|
| Time to first system | Weeks | Months, after hiring and onboarding |
| Experience | Has shipped similar systems elsewhere | Learns on your first project |
| Business knowledge | Learns your processes during the engagement | Already knows them, or will over time |
| Continuity | Depends on the handover | Stays with the company |
| Hiring risk | None; the engagement ends when the work does | Senior AI engineers are scarce and slow to hire |
| Best for | First production systems, a fast start, new capability | AI as part of the product, long-running platforms |
When an in-house team is the right choice
If AI is part of what you sell, the people who build it should be yours. The same goes for a company with a mature engineering team that has already shipped one AI system and needs depth rather than a start. In those cases an outside firm adds a layer you would eventually have to remove.
An in-house team is also right once the systems exist and the work becomes steady improvement: new rules, new document types, new agents on a proven pattern.
When outside engineers are the right choice
Most companies outside the software industry have no one who has taken an agent from prototype to production, written its evals, and kept it working after the model changed. Hiring that person takes months, and a first hire working alone tends to rebuild mistakes others already made.
Outside engineers also bring a view across companies. They have seen which approaches broke in production and which held, and they can put a first workflow live while your hiring plan catches up.
What a good handover looks like
- Code, data and credentials sit in your accounts from the first day, not transferred at the end.
- Every agent has written documentation, an eval set and a named owner on your side.
- Your engineers have changed at least one agent themselves before the outside team leaves.
- Operators know how to review exceptions and when to escalate.
- There is a plain list of what the outside team still supports and for how long.
How Native works
Native sends forward-deployed engineers into the business to build alongside your team, then trains that team to run and extend the agents. Native helped TIE, a transportation insurance MGA that had already tried automating its endorsement work in ChatGPT, put agents into production that now decide 74% of endorsements with no underwriter. See how delivery works.
Case study · TIE
TIE decides transportation insurance endorsements in under a minute.
Native helped TIE, a US transportation insurance MGA, put agents on every submission and endorsement pack. They check each one and either decide it or hand the underwriter a pre-filled decision, and 74% of decisions now need no underwriter.
Frequently asked questions
Should I hire an AI engineer or an AI consultant first?
If you have no one who has shipped an AI system to production, an outside team usually gets the first one live sooner and gives you a working reference for what to hire. If AI is core to your product, hire first.
How do I avoid depending on an AI consultancy forever?
Make ownership and training part of the contract: your accounts from day one, eval sets and documentation for every agent, and your own engineers making changes before the engagement ends.
What roles does an in-house AI team need?
Typically an AI engineer who can build and evaluate agents, an engineer who knows your systems of record, and an owner in the business who decides what the agents may do.
Can a small company build an in-house AI team?
It can, but one person rarely covers model work, integration and evals. Smaller companies often get further by training existing staff to run systems an outside team built.
