LLM Challenge

Don’t let LLM hallucinations corrupt your data

A dual-LLM consensus engine ensures only accurate data makes it into your API, ETL and HITL workflows.

Trusted by forward-thinking engineers

Add a trust layer to AI document data extraction

Verify outputs with two LLMs

Two LLMs run your prompts in parallel: an extractor and a challenger. Get an output only if both agree—NULL if not. Configure any combination: OpenAI + Claude, Azure GPT + Vertex + more!

Track, debug & optimize with extraction metadata

Get drilled-down insights by running the metadata for every extraction. Track token costs, view challenger confidence scores, or debug outputs after deployment.

Results you can count on

Production-Grade Data Integrity

Improved Automation Scaling

Reduced Manual Oversight

Supports the models and
adapters of your choice

Loved by users

Unstract lets us turn a wide range of document formats into clean, structured data with low integration effort and without compromising on enterprise-grade controls. Its high extraction accuracy paired with clear highlight-based validation speeds up our process a lot!

Lenny Hartman, CTO, Tokenstreet

Results you can count on

Insurance Claims Processing

No more time-consuming manual data retrieval. Improve insurance resolution time by automating claims document processing.

Insurance Underwriting

Unstract enables critical data retrieval at high accuracy. This enables insurers to make faster underwriting decisions at scale.

Insurance Fraud Detection

Unstract’s intelligent document processing helps insurers build models to detect anomalies and flag fraudulent claims.

Faq

Frequently asked questions

01Which LLM combinations work best?

We recommend pairing LLMs from different providers. Popular combinations: OpenAI GPT-4 + Google Gemini Pro, Anthropic Claude + Cohere, OpenAI + Anthropic.

02What happens when LLMs disagree?

The field returns NULL.

03How long does consensus take?

Typically adds 2-5 seconds to extraction time. For financial and legal documents, accuracy trumps speed.

04Can I use the same LLM model for both extraction and challenging?

Technically yes, but you shouldn’t! Using the same model (or even models from the same provider) defeats the purpose because they tend to make similar mistakes. Provider diversity is key to catching different error patterns.

05What’s the difference between a NULL and an empty field?

Important distinction: NULL means “we couldn’t reach consensus.” Empty means “we agreed this field has no value.”

06Can I see the challenge metadata for debugging?

Yes. The full conversation log is available via API for every extraction. You can see why the LLMs disagreed and the confidence score given by the challenger LLM.

Secure and Compliant, Always

Unstract adheres to the strict rules and regulations of various compliance authorities. Rest assured, we have policies, systems, and processes to ensure that your data is always safe, secure, and private.