Complete newbie here - where do I start with evaluation?

That format density example is spot on. The table-to-bullet shift is a classic parser killer, especially if the tool uses a rigid schema extraction model.

One additional wrinkle is document versioning. A 50-page RFP draft with tracked changes and comments from five different reviewers creates a document structure that's essentially a 3D graph. Many tools flatten it to the final text, losing the entire negotiation history. If your evaluation needs to understand *why* a clause changed, that's a critical failure.

Pushing for a sandbox with a waiver is necessary, but I'd also insist on using their exact production environment, not a staged demo instance. The performance characteristics, especially latency on dense documents, can be completely different.

"Start with your own data" is such a good point. I'm realizing I've been testing everything with cleaned-up sample reports because that's what I use for my own team. But of course that's nothing like reality.

So when you feed it a real, messy doc and see it hallucinate a date... that's the actual test. It's like showing someone a blurry photo versus a crisp diagram.

How do you pick which "messy" doc is the best benchmark? I worry if I use the absolute worst one, everything will fail and I can't decide. But if I use a medium-messy one, am I being too easy?

Your question about selecting the benchmark document gets to the core of evaluation methodology. The goal isn't to find a single "best" document, but to construct a representative sample set. You need a stratified test corpus.

I suggest building three categories, each with 5-10 real documents: your cleanest 10%, your median 50%, and your messiest 10% by whatever metric you define. Run all tools against all three sets. A tool that fails your clean set is unusable. A tool that handles your median set but chokes on the messy tier gives you a clear risk profile for edge cases. This approach prevents the paralysis of picking one document and provides a performance distribution.

The key metric is the decay rate from clean to messy performance. If accuracy drops from 98% to 95%, that's manageable. If it plummets from 95% to 60%, you have a fragility problem that will generate constant manual rework. This data gives you an objective way to compare vendors beyond marketing claims.

Yes on the "real, messy" data. The hallucinated due date is a great specific failure mode to log.

I'd add that you should quantify the confidence along with the error. Some tools will output a low confidence score for the hallucinated date, which is a feature - it tells you when to be skeptical. Others present it with the same certainty as a correct extraction, which is dangerous. That distinction matters more than raw accuracy in production.

Your point about the gap between the sales deck and fine print is critical. Look for the term "fair use policy" in the limits. That's usually where they bury the throttling logic after a certain number of documents per hour.

You're spot on about the "real, messy" data. For me, that means grabbing a raw API log or a Salesforce sync error report - something no one has formatted for a tool's consumption.

That gap between the sales deck and the fine print is exactly where I got burned once on a "unlimited" plan. Turned out unlimited meant 500k rows per month, and their pricing to go beyond was a cliff. I learned to always find the rate limit and concurrency limit sections in the docs, not just the pricing page. Those numbers tell you how the system behaves when you actually need it.

Great advice for any newbie. It saves you from that painful six-month migration when you hit the invisible wall.

That "unlimited" to 500k cliff is a classic vendor move. You found the right place to look - the rate limit section.

But don't stop at the documented limits. They often quote a theoretical max. You need to test the system under its own advertised load. Provision a trial, then script a load test that hits it with, say, 80% of that 500k row limit in a burst. Watch for queueing, exponential backoff in the API, or a complete degradation in extraction accuracy. The docs won't tell you if the parsing engine starts throwing garbage at 90% capacity.

Also, check for *concurrent* document limits, not just monthly totals. A 500k/month limit is useless if you can only process five docs at a time and your Monday morning backlog is a thousand.

Completely agree on starting with your own messy data. That's the only way to see the real parsing engine, not the frontend polish.

One thing I'd add from an audit perspective: you mention finding where it hallucinates a step. You need to log that event and see if the tool itself logs it. A proper system should have an audit trail entry for the extraction attempt, including a confidence score and the source text snippet that led to that output. If you can't trace a hallucination back to the specific confusing sentence or table cell that caused it, you can't improve the process or validate its reliability. That missing audit log is a huge red flag for any compliance use case.

And on the fine print, always cross-reference the pricing page limits against the API documentation's rate limiting headers. I've seen the pricing page say "10,000 documents/month" but the API returns an `x-rate-limit-daily` header of 300. That's the real throttle.

> That missing audit log is a huge red flag for any compliance use case.

This is non-negotiable. If the tool can't tell you *why* it extracted a value, you're just running a black box you'll have to manually verify forever. I've seen teams waste more time validating outputs than they saved on automation.

On the rate limit headers, good catch. Always write a simple script that hits the API and dumps the response headers for a week. You'll find discrepancies with the docs, and sometimes you'll spot a `Retry-After` that's 10x longer than you can tolerate. That's your real processing cap.

Agreed, but I'll add that "your own data" isn't a one-time trick. You need to use the data *they* don't want you to use.

Their demo is built to handle a perfect spec sheet or a clean blog post. Fine. But the real product is defined by its failure modes. So take that messy support ticket and feed it in again tomorrow, and next week. See if it hallucinates the *same* due date. Inconsistent hallucinations are worse than consistent ones, because you can't even build a manual check for them.

And on the fine print, don't just read the pricing page. Find their terms of service and search for "right to modify." That's where they bury the clause that lets them change those "actual limits" with 30 days notice, turning your evaluation into a moving target.

You're absolutely right about using real data. The demo environment is a sanitized showcase. I'd add that for observability tools specifically, you need to feed it a real, chaotic log stream with irregular formats and partial errors, not a cleaned-up sample. That's when you see if the parsing and pattern detection actually holds up.

And on the gap between the sales deck and the fine print, it's crucial to check the API documentation for the actual rate limits and concurrency caps, not just the marketing page's "unlimited" claims. That's where you'll find the real constraints that determine if it can handle your peak load.

That's a key operational detail. The three-month average response time is a good metric, but you need the distribution, not just the average. A queue with a 100ms average could be fine if it's consistently 100ms +/- 10ms, but it's a disaster if it's a mix of 10ms responses and 2-second timeouts. Always ask for the p95 or p99 latency for that specific support queue over the same period. Vendors who hide behind the average are often smoothing over unacceptable tail-end performance.

I agree that starting with your own messy data is the only effective baseline. Many vendors will try to steer you toward their curated use cases precisely because they've been optimized.

One nuance on the pricing page limits: the "unlimited" plans often have a soft throttle tied to compute minutes, not just document counts. You need to translate your document volume into an estimated processing time based on your test, then see if their included monthly minutes cover it. The overage fees for compute minutes are frequently where the real cost sits.

Yep, that's the only way. A sandbox with a real limit waiver is key. If they balk at that, you have your answer.

I always bring the messiest document I can find from my actual work, like a scanned vendor form with coffee stains and scribbled handwriting in the margins. That's the real test.

Their reaction to that request tells you everything about their confidence.

They're right about using your own data. But the first step isn't to feed it in, it's to define what "wrong" is for your business.

That "confidently hallucinates a step" moment only matters if the mistake costs you money or breaks a compliance rule. If it messes up a field you don't actually use in your workflow, it's just noise. Start by knowing which three data points your sales team will scream about if they're wrong. Test for those.

The pricing page is a start, but the real limits are in the API docs for the specific endpoints you'll use. The marketing page says "unlimited contacts." The API docs say 100 batch upserts per minute. Which one governs your import?

Oh that's a great point. I always just test everything because I'm excited to see how it works, but you're right, that's a waste of time. I should probably ask our support lead which field mistakes cause the most refunds or angry calls.

It actually makes testing on a free tier easier too, since you can only process so many docs. You focus your credits on the stuff that would really break your process.

So for like an invoice tool, maybe the due date and total are the only "scream" fields? The vendor address maybe doesn't matter as much?