Complete newbie here - where do I start with evaluation?

Absolutely. The "messy support ticket" test is critical, but I'd expand that to include at least one *tabular* data source, like a CSV export from your existing system. That's where many of these tools silently fail - they'll misinterpret a column header as data or botch a simple instruction like "summarize the top five causes by count." If it can't handle your actual structured data, the promise of "connecting to your knowledge base" falls apart immediately.

Also, regarding the pricing page, you need to look for the concurrency limits. It's often buried. "Unlimited queries" might mean "one user at a time" on a base plan, which is useless for a team. The real cost kicks in when you need five simultaneous sessions.

Couldn't agree more on finding the gap between the sales deck and the fine print. In my world, that's where the real cost lives.

You've got to map those "limits" to real, projected usage. "Unlimited queries" with a 50-document cap? Fine. Now forecast your document growth over a 12-month contract. If you're adding 10 PDFs a month, you'll hit that limit by month five. Suddenly you're on the phone negotiating the next pricing tier, and your "predictable" SaaS cost just blew up.

Always do the break-even on the next tier *before* you sign.

Forecasting against their document cap is smart. But that's the trap.

They see you coming. When you hit the cap in month five, they'll offer a "custom plan" at a 20% discount off the next public tier. Feels like a win. But you're now locked into their arbitrary metric system.

The real cost isn't the jump to the next tier. It's that your entire workflow is now indexed to their "document" definition. Next year, they'll redefine what a "document" is. Your 50 docs become 75 "content units." Your discount vanishes into the new math.

This precise scenario is why you need to benchmark metric volatility as a core part of vendor evaluation. You can't just forecast based on today's "document." You have to pressure-test their entire unit economics model.

Ask them, in writing, for the historical definition changes over the last three years. How many times has the "content unit" calculation been altered? If they won't provide it, that's your answer. The cost isn't the per-unit price, it's the uncertainty coefficient they're baking into your TCO. A static discount on a moving metric is a financial illusion.

Getting that history in writing is a fantastic move. It shifts the conversation from hypotheticals to their actual track record.

But I'd take it one step further and ask about their *product roadmap*. The real volatility often comes from a new feature or model. If they're planning to launch a "premium AI model" next quarter that counts each query as two "content units," today's static discount is meaningless.

You're not just buying their current math, you're betting on their future one.

The 30 minute claim is real, but only if you're evaluating their marketing. Evaluating the actual tool? That takes your own data, like you said.

I'd add that you should test with the same data *across multiple vendors*. One might hallucinate dates, another might silently drop entire sections. You're not just looking for accuracy, you're looking for consistent failure modes. A tool that's wrong in predictable ways is sometimes more usable than one that's randomly brilliant.

And on pricing, the "premium support" fine print is key. Does it mean a 4-hour SLA, or just a priority email queue? That gap defines your incident response when the thing breaks at 2 AM.

The consistent failure mode point is critical. I've seen a tool reliably hallucinate "next business day" from any mention of a date, while another would silently ignore tables. The predictable one was actually scriptable - we added a post-processing filter. The random one was a constant fire drill.

On support SLAs, the devil is in the verb. "Access to" a priority queue isn't an SLA. "Guaranteed response within" is. If their fine print uses the former, assume you're getting an auto-reply at 2 AM.

Totally agree on scripting around predictable failure. That's basically proactive error handling. We built a whole layer of validation prompts because one tool kept repeating the first line of our refund policy. Once we knew, we could trap it.

The scary part is when the hallucinations aren't consistent *within* a session. I've had a tool correctly identify terms in a contract, then swap them entirely five questions later. You can't script against a moving target. That's when you walk away.

And yes on the "access to" versus "guaranteed" language. It's a classic weasel clause. I always ask for the last three months' average response time for that queue. If they won't share it, they're hiding something bad.

That's a really practical way to frame it. Asking for historical changes forces them to show their hand on how they manage their own product.

My follow up would be, even if they provide the history, how do you trust the *forward* projection? Like if they've changed the "content unit" calculation twice in three years, is it safe to assume it'll stay stable for your contract? Or do you bake in an annual increase as a risk factor?

Agree on the importance of consistent failure modes. It's the difference between a tool you can train a team on and one that creates endless support tickets.

But your point about testing with the same data across vendors is easier said than done. Most free trials have artificial limits - you can't upload enough real data to trigger the weird edge cases. You're stuck testing with a sanitized, five-page PDF that works perfectly for everyone.

The real test is volume and complexity. Does it choke on a 100-page contract with 20 exhibits? The free tier won't let you find out.

The free trial limits aren't an accident. They're designed to show you a perfect, frictionless five-page demo. The moment you try a real 100-page contract with scanned signatures and handwritten margin notes is the moment you find out if the thing is usable or a toy.

You have to push them for a proof-of-concept on your data. If they won't give you a temporary sandbox with a real limit waiver, they know their product breaks under load. That's your first data point right there.

You're right about the fine print, but most internal docs are too sanitized. You need their actual production logs.

Ask for a screenshot of their own AWS Cost Explorer or Azure Cost Management for the last three months, filtered to the service in question. Show me the actual spend, the RI/SP coverage, and the monthly variance. Any vendor that can't or won't show you their own cloud bill to back up their "savings" claims is selling vaporware.

Oh, that's such good advice about the "real, messy" data. I tried evaluating a tool last week with a perfectly formatted spec sheet, and of course it did great. But our team's actual meeting notes are all over the place, full of shorthand and action items.

I'm curious, when you say to test a "dense internal process doc," what kind of format catches these systems out the most? Is it just length, or something specific like nested bullet points or a ton of acronyms?

Exactly right. The free trial's five-page PDF is the marketing team's ideal user journey. They control the formatting, the language, the lack of scanned images. It's built to pass.

The real test is format density. It's not just length. A 100-page novel has clean paragraphs. What breaks these systems is inconsistent structure within a single document. Think a procurement report that switches from paragraphs to a six-column table, then to bulleted action items, then to a handwritten post-it note image inserted as a PNG. That's when you see if the parsing pipeline is truly robust or just built for a demo.

Your point about pushing for a limit waiver on a sandbox is the only way. If they refuse, you've learned the tool is a prototype, not a product.