Accuracy is in the eye of the beholder
Accuracy needs context
It’s 2025, and every structured data vendor is still shouting the same thing: “we’re the most accurate,” “best zero-shot performance,” “production-ready out of the box.” You’ll see it on every landing page and pitch deck, like it’s just a matter of which vendor wins the leaderboard.
At bem, we think that’s a fundamentally broken framing. It treats accuracy like it’s a single, objective number — when in reality, accuracy is always contextual. It depends on your inputs, your schema, your edge cases, your users, and the specific decisions you're making downstream. Accuracy isn't just a model score. It's a reflection of how well a system fits your world.
This obsession with zero-shot performance — how well a model performs on the first try — is seductive. It makes it easy to believe that the hard part of automation has been solved, that models will just know what to do without being taught. But in practice, zero-shot performance is a starting point. It is not the finish line. And treating it like the finish line leads to brittle systems and broken promises.
We’ve seen this same thinking play out in conversations about AGI. The assumption is that, eventually, a superhuman agent will run your business logic for you — navigating edge cases, making judgment calls, handling complexity without ever needing to be taught. But even the most capable agent would need access to your context: your documents, your rules, your systems, your constraints. Otherwise, it's just another hallucination.
This is why we’ve made a different bet at bem. We’re not building a product that claims to be “the most accurate.” We’re building infrastructure that helps you get accurate — quickly, iteratively, and on your terms.
Accuracy at bem doesn’t come from one-shot perfection. It comes from closing the loop.
With bem, every correction you make becomes a training signal. You can turn corrected outputs into golden examples with one click. No external labeling tools, no export-import workflow. Just use the product as intended, and your system gets better in the background.
We let you build golden datasets as part of the normal review process — not as a separate, burdensome task. That means you can track performance, run automated evaluations, and pinpoint where accuracy is degrading or improving over time. We give you versioned workflows, auditable changes, and a full history of how each decision was made.
Even more importantly, bem lets you define what “correct” means. You can use LLMs not just to extract information, but to judge whether an output is valid. Want to check if a document matches a golden reference? Or whether a remittance file includes all the required fields before it hits your ERP? That’s what LLM-as-a-judge enables — and it’s built in.
We call this self-correcting infrastructure. Because every time someone fixes a workflow, the system learns. Every time someone flags a failure, the feedback gets recorded. And every time you run the same flow again, it runs better.
This isn’t a flashy promise. It’s infrastructure that quietly compounds.
So no, we won’t tell you bem is the most accurate platform. That would be meaningless.
What we will tell you is that with a small amount of effort — and the right feedback loops in place — you can get to production-grade, auditable, operator-approved accuracy faster than you thought possible. And your workflows will keep improving over time, without adding headcount or rewriting brittle logic.
Accuracy isn’t a static metric. It’s a system that evolves.
One last thought
There’s something deeper we believe, too: positioning your product around “accuracy” is not just misleading — it’s defeatist.
It’s the kind of posture you take when you’ve given up on system design. You wave around a few benchmark scores and call it “state of the art,” but behind the scenes, it’s all one-off prompt tuning, hand-picked demos, and error-prone edge cases waiting to happen.
The obsession with being “most accurate” traps teams in a Sisyphean loop. Every time the model falls short, you tweak a prompt, update a rule, add a few more examples. Push the boulder a little further up the hill. But the hill never goes away, because the core assumption is wrong: that there is a single, universal definition of accuracy you can optimize for once and be done.
That’s not how real operations work.
In high-stakes industries — freight, fintech, claims, compliance — accuracy is not an aesthetic ideal. It’s a constraint. And it varies based on context, tolerance, and risk. What looks like a rounding error to a model is a regulatory violation to your legal team. What passes in an eval set might break a payment, or misroute a load.
So instead of chasing some mythical peak accuracy, we’ve built bem to focus on something better: adaptation. Accuracy isn’t a fixed point. It’s a process. It’s what happens when the system learns from your users, your data, and your constraints — and gets better every time.