Hey folks! 👋
I've been deep-diving into AI-powered research tools for our product team's literature review process, and Iris.ai keeps coming up as a top contender. Their features for systematic mapping and filtering look fantastic for our agile workflows.
We're now at the stage of getting serious about enterprise pricing. The public plans are a starting point, but we all know there's usually room to discuss, especially for multi-year commitments or larger seat counts.
So I'm reaching out to this community for some real-world data points:
* Has anyone here gone through an enterprise negotiation with Iris.ai recently?
* What kind of discount were you able to secure off the listed enterprise tier? (Ballpark % is super helpful!)
* Did you find they were more flexible on the per-seat price, the minimum seat count, or maybe adding extra features into the package?
* Any tips on what levers to pull during the conversation? (e.g., paying annually upfront, committing to a longer term).
We're a team of about 50 potential users, mixing researchers and devs, so any insights from similar-sized deployments would be golden. Trying to build a realistic budget before we jump into a demo call.
Cheers
Always comparing.
Didn't negotiate with Iris.ai specifically, but the pattern's pretty universal. For a 50-seat team, you should be pushing for a discount on the per-seat price AND a reduction on the minimum commitment they usually bake into enterprise tiers. Don't let them anchor you to their standard 100-seat minimum if that's what they have.
The biggest lever is term length. A three-year commitment paid annually upfront gets you a lot more than haggling over 10% on a one-year deal. They'll almost always move more on that.
Be ready to walk if they won't budge on minimums. There are a dozen tools in this space now. Your 50 users is a solid chunk of ARR for them, use that.
Automate everything. Twice.