Beyond Extraction: AdaExtract's Confidence Layer

The World Changed. So Did We.

In 2024, we introduced AdaExtract as a foundation model for information extraction. Our goal was to build a highly accurate, schema-driven model that could outperform general-purpose LLMs. But the ground has shifted. Today, with the rise of incredibly powerful models like GPT-5 and beyond, competing on raw extraction capability is a race to commoditization.

Simply building a better extractor is no longer the winning strategy.

That’s why our focus has sharpened. We believe the next frontier isn’t about what information can be extracted, but how much that information can be trusted.

The Problem: When “Mostly Right” is Completely Wrong

For professionals in high-stakes fields—a lawyer reviewing a contract, a doctor parsing a patient’s medical history, a forensic economist analyzing financial statements—an AI that is “mostly right” is useless. A single hallucinated fact or a misidentified relationship can lead to catastrophic errors, legal liability, and a complete breakdown of trust.

This is the critical gap that general-purpose LLMs, for all their power, do not solve. They can extract information, but they cannot reliably tell you how confident you should be in that extraction. Their confidence scores are notoriously uncalibrated, making them unsafe for mission-critical automation.

Our Solution: A Two-Stage Architecture for Trust

AdaExtract is evolving. We are building a modular, two-stage system designed to augment, not replace, the most powerful models on the market. Our intellectual property and defensible moat lie not in the extraction itself, but in a specialized validation layer that delivers calibrated confidence.

graph TD A[Input Document] --> B subgraph Stage1[Stage 1: SOTA Extraction] B[Any SOTA Model e.g. GPT-6, GLiNER] end B --> C[Raw Extractions - Entities and Relations] subgraph Stage2[Stage 2: AdaExtract Validator - Our IP] D[Specialized Calibration Model] end C --> D D --> E[Validated Knowledge Graph with Calibrated Confidence Scores] style Stage1 fill:#e3f2fd,stroke:#333,stroke-width:2px style Stage2 fill:#d1c4e9,stroke:#333,stroke-width:4px

Figure 1: The AdaExtract Two-Stage Architecture

Stage 1: Pluggable SOTA Extraction: We leverage the best available model for the job—whether it’s a massive commercial API or a fine-tuned open-source model—to perform the initial, raw extraction. This ensures we always ride the wave of progress and never bet against the market’s best performers.
Stage 2: The AdaExtract Validator: This is our core innovation. A lightweight, highly specialized model analyzes the raw extractions from Stage 1 and assigns a calibrated confidence score to every entity and relationship. If our validator says an extraction has 95% confidence, it means you can expect it to be correct 19 out of 20 times.

This decoupling is our strategic advantage. As new, more powerful LLMs emerge, our system only gets better. We are future-proof by design.

What Calibrated Confidence Looks Like

The difference is stark. An uncalibrated model might be confident and wrong, or unconfident and right. A calibrated model’s confidence directly translates to its accuracy.

A conceptual plot showing an uncalibrated model's confidence vs accuracy as a scattered line, and a calibrated model's confidence vs accuracy as a clean diagonal line.

Figure 2: Conceptual Calibration Plot. Our goal is to turn the unpredictable output of raw LLMs (left) into the reliable, trustworthy predictions of a calibrated system (right).

Why This Matters: Our Defensible Moat

Our two-stage architecture creates a durable competitive advantage:

Future-Proof: We don’t compete with GPT-7; we are the essential plugin that will make it enterprise-ready.
Solves a Higher-Order Problem: We are not selling information extraction, which is becoming a commodity. We are selling trust, reliability, and risk mitigation—premium values in any high-stakes industry.
Lightweight & Specialized: Our core IP is in the validator model, which is smaller, faster, and cheaper to train and run than a monolithic foundation model, allowing for rapid iteration and domain-specific adaptation.

The Road Ahead

Our research team has already validated this two-stage approach, demonstrating a significant improvement in confidence calibration over baseline models. Our next steps are focused on refining this capability and integrating it deeply into the AceTeam platform, turning this powerful research into a product that unlocks the next wave of enterprise automation.

For more information or to request a demo of our confidence calibration in action, contact our team at [email protected].