Production AI needs a glass box: How bem gets better as you correct it

The promise of a magic black box is not enterprise-friendly. A vendor's guarantee of accuracy means nothing without proof.

There's a myth circulating in the world of enterprise AI. It's the promise of a magic black box, a single API call that delivers perfect, off-the-shelf accuracy for any document, every time. I’ve tried those systems. They don’t deliver what they promise.

If you're a technical leader, you know this is a fantasy. In the real world of high-stakes, mission-critical operations, "trust me" is not a viable strategy. Enterprise buyers don't want more magic; they want fundamental control. They want a glass box, not a black box.

The Problem with the Black Box

Most AI platforms are designed as a one-way street. You send in your unstructured data, a model does something mysterious, and you get a structured result. But what happens when it's wrong? How do you correct it? How do you ensure the system learns from its mistakes and doesn't repeat them?

For most platforms, the answer is you can't. You're stuck with a static system, forcing your team to build brittle, post-processing logic or, even worse, fall back on manual review. This isn't automation; it's just a more sophisticated way to create more work.

Closing the Loop: The bem Lifecycle

This is the core philosophy behind how we've built bem. We believe that true, production-grade accuracy isn't a static feature you buy; it's a dynamic, continuous process you control. Our platform is designed to provide the full, end-to-end lifecycle for AI.

1. Create a Function: It starts by defining your blueprint. You create a Function that specifies your desired output schema; your definition of "good."

# Create a reusable function for processing invoices
curl -X POST "https://api.bem.ai/v2/functions" \
  -H "x-api-key: <YOUR_BEM_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "functionName": "enterprise-invoice-processor",
    "type": "transform",
    "outputSchema": {
      "type": "object",
      "properties": {
        "vendor_name": { "type": "string" },
        "total_amount": { "type": "number" }
      }
    }
  }'

2. Call the Function: You call your Function with your unstructured data and get an initial structured result.

# Call the function with a new invoice PDF
curl -X POST "https://api.bem.ai/v2/functions/enterprise-invoice-processor/call" \
  -H "x-api-key: <YOUR_BEM_API_KEY>" \
  -F "referenceID=inv_abc_123" \
  -F "file=@/path/to/invoice.pdf"

3. Subscribe & Observe: We provide the "glass box." You create a Subscription to receive results via webhook, and use tools like Function Review for complete transparency into the performance of your functions. You can see accuracy, precision, and recall for every field, giving you a clear, statistical picture of how the system is performing.

# Create a subscription to receive results at your endpoint
curl -X POST "https://api.bem.ai/v1-alpha/subscriptions" \
  -H "x-api-key: <YOUR_BEM_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Invoice Results Subscription",
    "type": "transform",
    "functionName": "enterprise-invoice-processor",
    "webhookURL": "https://api.your-company.com/webhooks/bem/results"
  }'

4. Correct & Fine-Tune: This is the most critical step. We make it easy to give feedback on mistakes. Our API allows you to embed a correction mechanism directly into your own product. When your operators or end-users correct a field, that correction isn't lost; it's a valuable signal that is sent back to our platform via a simple PUT request.

# Send a correction back to bem via the API
curl -X PUT "https://api.bem.ai/v1-beta/transformations" \
  -H "x-api-key: <YOUR_BEM_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "transformations": [
      {
        "transformationID": "tr_2bxoJPNdSD4LgRT4YVC4gt72hlI",
        "correctedJSON": {
          "vendor_name": "Corrected Vendor Name Inc.",
          "total_amount": 150.75
        }
      }
    ]
  }'

This signal automatically triggers fine-tuning jobs. Your users' corrections in production aren't a sign of failure; they are the fuel that drives your unique model to 99%+ accuracy. It's a continuous, self-healing loop.

This is what it means to be an enterprise-ready AI platform. It's not about promising magic. It's about providing the tools, the transparency, and the control to build an AI system you can actually trust with your most critical operations.