Updated May 2026. Written by Morgan, FellowHire Marketing. Reading time: 9 minutes.
A vendor-agnostic buyer's guide. The questions to ask, the trade-offs to weigh, and the gotchas worth watching for.
AI coworker products (assistants, coworkers, fellows, agents, whatever each vendor calls them) are easy to buy and harder to buy well. The pricing pages do not tell you what matters. The demos do not show what breaks. This guide is the framework we wish more buyers used. We will point at FellowHire when it fits, but the framework holds regardless of which product you pick.
Most teams shop AI by vendor first ("we are looking at Viktor and ChatGPT Team"). Wrong move.
Better: name the role you want covered. Sales? Support? Paralegal? Copywriting? The role determines whether you need a generalist coworker, a role-specific fellow, or just an assistant.
A specific test: write down the work you actually want this AI to do this week. If the list spans 5 different roles, you are looking for a generalist. If it is mostly one role, you are looking for a specialist.
What does the AI actually see in our team? (Slack scopes, M365 Graph permissions, file access. Get specific.)
Where does our data go and how long is it kept? (Vendor data retention policies vary widely. Get this in writing.)
Is the AI custom-trained on our material, or is it off-the-shelf with prompt context? (Material difference. Custom-trained = depth; prompt-only = generic.)
Who reviews outputs before they go to customers? (Default should be human-in-the-loop for customer-facing work.)
What happens when the AI is wrong? (Audit logs? Rollback? Error analysis?)
How does pricing scale? (Per seat? Per message? Per credit? Annual flat? Predictable bills matter.)
What is the implementation timeline? (1 week? 1 month? 3 months? Cost of delay is real.)
What is the contract length and exit cost? (Annual contracts have lock-in. Read carefully.)
What compliance certifications does the vendor hold today? (SOC 2? ISO? GDPR? HIPAA? Some industries require these; many vendors are pre-certification.)
Who exactly will use this AI in our team? (One person? The whole team? Multi-user changes the value proposition substantially.)
Per-message or per-credit pricing without a usage estimate. You will either over-pay or under-use.
Per-seat pricing for shared tools. If only 1 of 10 teammates uses it, why are you paying for 10?
Free tiers that do not demonstrate the actual product. Make sure the trial includes the integrations and the role you actually need.
"Contact us for pricing" as the only price. For SMB, this is usually a sign the deal is being structured around your budget rather than the product's value. Push for a price list.
Annual upfront with no escape clause. Some vendors offer mid-year exits if the product does not deliver. Ask for that.
Demos show happy-path workflows. Ask the vendor to demo a workflow that broke last week and how they handled it.
Demos show outputs you would publish. Ask to see the rough drafts before human review.
Demos use the vendor's data, not yours. Ask for a paid pilot on your data before signing annual.
Demos hide the management overhead. Ask: "Who maintains this? How often does it need re-tuning? Who is on the hook when it goes wrong?"
"Just build it ourselves with OpenAI's API" is a common alternative. Sometimes it is right. Mostly it is not.
When build wins: you have an in-house ML team, the workflow is highly proprietary, and the vendor offerings are off-fit.
When buy wins: you do not have an ML team, the workflow is common across teams (sales, support, paralegal have known patterns), and time-to-value matters.
Hidden cost of build: every product update from a vendor (new model, better tooling, better integrations) becomes your engineering team's job to keep up with. That cost compounds.
Set 2-3 specific outcomes you want the pilot to prove (e.g., "Pat's outbound drafts hit a 35%+ reply rate"). Write them down.
Time-box: 30 days is usually enough. 60 max. Anything longer is the vendor stalling.
Define rollback: if the pilot fails, what is the exit?
Get baselines: measure the work BEFORE the AI lands. Otherwise you are comparing AI-with vs vibes.
Pilot with the people who will actually use it, not with leadership only. The user reality is what matters.
We sell role-specific fellows custom-trained for one role at a time. Annual flat pricing. SOC 2 and ISO 27001 compliant. Slack and Teams native. Pilot programs available.
If you want a generalist coworker rather than role-specific fellows, we point you at Viktor or Lindy. The framework above holds regardless of who you pick.
Yes. They solve different problems. Evaluate generalists against each other (Viktor vs Lindy vs ChatGPT Team) and specialists against each other (FellowHire vs vertical tools). Then decide which category fits your team. The framework in this guide covers both.
30 days is usually enough. 60 max. Anything longer is the vendor stalling. Set 2-3 measurable outcomes, get baselines before the pilot starts, and evaluate honestly at the end.
Shopping by vendor before naming the role. The role determines the category (generalist vs specialist), and the category determines which vendors are even relevant.
Sometimes. If you have an in-house ML team, a highly proprietary workflow, and the vendor offerings are off-fit, build wins. For common role patterns (sales, support, paralegal) where time-to-value matters, buy wins. The hidden cost of build is maintaining parity with vendor improvements.
We will help you figure out which category fits your team, even if the answer is not us.