How to Choose an AI Automation Agency

A practical checklist for evaluating AI automation agencies: questions to ask, what a good scope looks like, ownership, pricing models and red flags.

Nexora Editorial TeamPublished Aug 20, 20263 min read

FIG. 06 · EVALUATION CHECKLIST

Checklist comparing green flags such as written scope and code ownership with red flags such as vague deliverables and guaranteed results.

Choose an AI automation agency the way you'd choose any engineering partner: by how clearly they scope, how they handle failure, who owns the result, and whether they can explain trade-offs in plain language. Demos matter less than you'd think; what happens after launch matters more.

Yes, we're an agency writing this. The checklist below is the one we'd want you to use on us.

1. Do they start with your process or their tool?

A good agency asks about volumes, exceptions, systems and who does the work today before recommending a platform. If the first conversation is about a specific product they resell, they may be fitting your problem to their tool.

2. Will you get a written scope?

Before paying for the build, you should receive a document that states:

The processes and systems included
What's explicitly out of scope
How errors and exceptions are handled
Acceptance criteria — how you'll both know it works
Timeline, price and payment terms
What support is included after launch

3. How do they price?

Model

Pros

Watch for

Fixed price after scope

Budget certainty

Make sure scope is detailed

Hourly / time and materials

Flexible

No cap means no certainty

Monthly retainer

Ongoing improvement

Clear deliverables each month

Performance-based

Aligned incentives in theory

Hard to measure fairly; disputes

We use fixed pricing after scope because it forces both sides to be precise. See what AI automation typically costs for what drives the budget.

4. Who owns what?

Ask directly:

Are the workflows, code and prompts transferred to us?
Are the AI and platform accounts in our name?
Can another developer take over without the agency?

If the answer to any of these is no, understand why and what it costs to leave.

5. How do they handle mistakes?

AI outputs are sometimes wrong. Ask how they'll detect it: validation rules, confidence thresholds, human review queues, logs. Ask who receives alerts when an integration breaks at 2am. An agency that says their AI "doesn't make mistakes" is a red flag.

6. Can they show relevant work honestly?

Look for work similar to yours in complexity. It's fine if some is concept or internal work — many good agencies build demos — but it should be labelled as such. Be cautious of precise ROI figures without context or client attribution.

7. Do they understand data and privacy?

They should be able to tell you which providers will process your data, under what terms, and where it's stored. If you're in a regulated industry, they should raise compliance questions before you do.

8. What happens after launch?

Processes change and APIs update. Ask what's included for bug fixes, how changes are quoted, and whether monitoring or maintenance is available.

Questions to ask on the first call

What would you automate first in our case, and why?
What would you deliberately *not* use AI for?
What's the monthly running cost likely to be?
What does the handover include?
What happened on a project that didn't go to plan?

Red flags

Guaranteed percentage improvements before they've seen your data
No mention of testing or monitoring
Pressure to sign before a scope exists
Proprietary platform you can't leave

To prepare for these conversations, it helps to know your own numbers — how to calculate automation ROI is a good place to start.

Nexora Editorial TeamEngineering & StrategyGuides written and reviewed by the engineers who scope and build Nexora projects. We write about what we actually implement: automations, agents, integrations and production software.