Question 1

We already shipped an AI feature. Why would we need this?

Accepted Answer

Shipping one AI feature and running AI as a governed, shared capability across the product are different problems. Most B2B SaaS companies in this segment have isolated AI features per team with no shared platform or governance underneath — that's usually where the gap shows up.

Question 2

Do you help us add AI to our product, or manage AI that's already there?

Accepted Answer

Both, depending on where you are. Some engagements start with the platform underneath (model gateway, prompt registry, eval gates) so new AI features don't each reinvent the plumbing. Others start with governance for AI capability that's already shipped and growing without oversight.

Question 3

What size company is this segment aimed at?

Accepted Answer

50-500 employees, roughly $5M-100M ARR, with a 20-200 person engineering team. Below that, the AI-native startup profile usually fits better; above it, enterprise engineering.

Question 4

Who typically signs off on this kind of work?

Accepted Answer

The CTO in most cases, with the CIO involved at the larger end of this range. Platform, DevOps, and security teams usually influence the decision before it reaches that point.

Question 5

Where do these engagements usually start?

Accepted Answer

Internal AI platform work or agent adoption most often, with AI operations and governance work following once there's a shared platform to operate and govern.

B2B SaaS

The profile.

What they need.

Who's in the room.

Where we usually start.

Agent Engineering

AI Platform Engineering

AI Operations & Optimization

AI Governance & Control

Common questions.

Bring us the AI feature nobody's governing yet.