Why Do Finance and Accounting Teams Trust Bem?

We process millions of documents a month. Invoices from procurement systems nobody wants to touch. Capital call notices buried in fund admin inboxes. SEC filings with no consistent schema across issuers. Bank statements that look different from every institution. Purchase orders that arrive as PDFs, scanned images, emails, and sometimes just photos taken on someone's phone.

Our customers range from Fortune 100 financial institutions to mid-market SaaS companies and late-stage startups. The pattern is always the same: somewhere in their operation, there is a team, sometimes dozens of people, manually reading documents, typing values into systems, and praying they don't make a mistake that cascades downstream.

This is the problem we built Bem to solve. Not with agents. Not with chatbots. With production infrastructure for unstructured data that finance teams can actually trust.

The Trust Problem Nobody Talks About

Here's what most AI companies won't tell you: accuracy without proof is just a marketing claim.

When we onboard a new customer, we don't promise them a number. We don't say "95% accurate" and hope they believe us. Instead, we show them, in real time, exactly how confident the system is about every single field it extracts. Not a single aggregate score across the document. Per-field confidence, with reasoning about why the system believes what it believes.

This matters enormously in finance. When you're processing invoices for a company with a hundred venues and thousands of vendors, a single misclassified line item can throw off an entire reconciliation. When you're extracting data from SEC schedules of investments, a wrong number doesn't just create a support ticket. It creates a compliance exposure.

So we built the system around a simple principle: you should never have to take our word for it.

Every extraction comes with confidence scores derived from the model's own internal probability . Not a post-hoc guess, but the model itself telling you where it feels uncertain. We layer an independent evaluation model on top, a larger model supervising the extraction model, so you get a second opinion on every output. And then we surface all of this through an API and a UI that lets your team set their own thresholds.

Want to automate everything above 98% confidence and route the rest to a human reviewer? Done. Want to pipe the confidence distribution into your own data science team's monitoring dashboard? Also done. The point is that accuracy isn't a feature we ship. It's a loop we run together.

Self-Training Loops: Accuracy That Actually Improves

Most of our customers start at around 93-94% off-the-shelf accuracy on complex documents. That's good, but it's not good enough for production in finance.

The question we always ask is: are you happy with that number, or do you want to get closer to 100%? Almost everyone wants closer to 100%. And so the real product isn't just the extraction. It's the path from 93% to 99%+ with the least amount of effort.

Here's how it works. When the system is less confident about a field, it flags it. A human reviews and corrects it. That correction feeds directly back into the training . Not just as labeled data for fine-tuning, but as signal for the confidence judge itself. The system learns not only what the right answer is, but also learns to better predict when it's going to be wrong.

We've seen customers go from 93% to 99%+ accuracy with as few as 30 to 50 corrections across individual fields. Not documents. Fields. The models learn patterns remarkably fast when the feedback is targeted. And because we track the accuracy trajectory over time, you can see exactly where you are, how much labeling effort remains to hit your target, and what the trade-off looks like between human review budget and automation rate.

This is the opposite of a black box. It's a system that gets better in proportion to how much your team engages with it, and it tells you exactly where it stands at every step.

What "Production-Grade" Actually Means in Finance

Finance teams don't just need accuracy. They need auditability, governance, and infrastructure they can explain to their compliance and security teams.

We're SOC 2 Type II and HIPAA compliant. That's table stakes. What separates us is how we think about data architecture at the deployment level.

Most of our finance customers use one of two deployment models. The first is our managed cloud, encrypted end-to-end, multi-tenant isolation, zero data retention if that's what you need. The second, and increasingly popular for larger institutions, is private link.

Private link is how Snowflake and Databricks work with their enterprise customers, and it's how we work with ours. We deploy an instance in whatever cloud you're already on (AWS, Azure, GCP) and you connect to it through the private backbone of your cloud provider. Your data never touches the public internet. It's not just encrypted in transit. It never leaves your subnet. We don't get access to it. Your security team can verify that independently.

For institutions where even the concept of data leaving a private network is a non-starter, this is the deployment model that unlocks adoption. We've had conversations with data architecture teams at some of the largest financial institutions in the world where 70-90% of their data is unstructured, and the single biggest blocker to doing anything with it was the egress question. Private link answers it.

We're also fully cloud-portable and multi-region. We run production workloads in the US, Europe, and Asia. If your compliance requirements dictate that data from European operations stays in the EU, we handle that. If you need a dedicated instance in a specific availability zone, we handle that too.

The Document Types That Actually Matter

We built Bem to be document-agnostic by design, but in practice, the finance vertical has produced the most demanding and diverse set of inputs we've seen. Here's what's running through our system today:

Invoices. The single most common document type in our system. Equipment rental invoices, utility bills, labor invoices, service agreements. Every vendor formats them differently. Every AP team processes them the same painful way. We've worked with companies processing 20,000 to 60,000 invoices per month, and the schema variation across vendors is staggering. Bem handles this natively because every extraction is driven by a schema you define, not a fixed template that breaks when a vendor changes their layout.

Capital call notices and fund documents. Private market fund administration generates enormous volumes of unstructured documents with no standardized schema across issuers. We work with investment firms that need to discover, compare, and evolve schemas over time as new fund structures emerge. The challenge isn't just extraction. It's schema inference and evolution without losing data.

SEC filings. 10-Ks, schedules of investments, proxy statements. These are dense, long, and structurally inconsistent across issuers. We extract structured data from filings that can run 50 to 100+ pages, with confidence scoring on every field.

Bank statements. Every institution, every format, every currency. Scanned, digital, sometimes handwritten annotations. We process these at scale for reconciliation workflows.

Purchase orders, remittance advices, contracts, insurance documents, claims. The long tail of operational finance documents that nobody talks about at conferences but everyone processes manually.

The common thread is that none of these documents were designed for automated processing. They were designed for humans. And the companies we work with have tried the alternatives: Textract, legacy OCR, internal builds with off-the-shelf LLMs. They come to us because those approaches either couldn't handle the variation, couldn't provide the accuracy guarantees, or couldn't operate within their governance requirements.

Why We're Not an Agent (But You Should Build Agents on Top of Us)

There's a lot of noise in the market right now about AI agents. Autonomous systems that make decisions, take actions, and operate independently.

We're not that. Deliberately.

The finance teams we work with don't want autonomous agents making decisions about their data. They want deterministic, auditable infrastructure that transforms unstructured inputs into structured outputs they can verify, trust, and pipe into their production systems. They want to know exactly what the system did, why it did it, and how confident it is. They want a human in the loop, not because the AI isn't good enough, but because compliance demands it and good judgment requires it.

Bem is infrastructure. It's the layer that sits between the messy real world of documents and the clean structured world of databases, ERPs, and downstream applications. We don't make decisions for you. We give you the structured, verified, confidence-scored data you need to make better decisions faster.

But here's the thing: agents need reliable, structured data to be useful. An agent that reads a PDF and hallucinates a number is worse than no agent at all. The teams building the most impressive agentic workflows in finance are the ones that separate the perception layer (turning documents into trusted structured data) from the reasoning layer (deciding what to do with it). Bem is that perception layer. We give your agents clean, confidence-scored, schema-validated inputs so they can actually reason over real data instead of guessing.

We give you the primitives to build whatever workflow you need on top. Route documents by type. Split packets semantically. Enrich extracted data against your own systems. Flag inconsistencies within documents. Gate automation on confidence thresholds. All of this through an API, composable, embeddable in your product, your internal tools, or the agent framework of your choice.

What We've Learned

After working with finance teams at every , from three-person fintech teams to data architecture groups at institutions managing trillions in assets, a few things are consistently true:

Accuracy is the price of admission, but the path to accuracy is the product. Nobody cares about your model benchmarks. They care about how fast they can get to the accuracy they need for their specific documents, and whether they can measure it independently.

Governance isn't a feature. It's an architecture decision. You can't bolt on compliance after the fact. The deployment model, the data retention policy, the encryption strategy, the audit trail. These have to be designed in from day one.

The documents are the easy part. The hard part is everything that happens after. Extraction is the wedge, but the real value is in the workflow: routing, enrichment, validation, human review, and the feedback loop that makes it all better over time.

Trust is earned in production, not in demos. We've never closed a customer without them testing the system on their own data first. That's by design. We show you the scores. You decide if they're good enough. If they're not, we show you exactly what it takes to get there.

Bem is production infrastructure for unstructured data. We help finance teams extract, structure, and automate the document workflows that run their , with the accuracy, governance, and auditability they need to operate with confidence.

If your team is still manually processing documents that should be automated, or if you've tried other solutions and hit the wall on accuracy or compliance, we'd love to show you how Bem works on your data. Get started at bem.ai