Enterprise-grade AI services do not train on your business data by default — and they’ll put that in writing. Docupath follow a model-garden approach, leveraging multiple foundational models selected for each task.
We ensure, through both contractual and technical safeguards, that no customer data is ever used to train external models.

By combining this with Docupath’s own fine-tuned, de-identified learning layer, we achieve higher accuracy and reliability while maintaining the strongest data privacy standards.

The myth is true, LLMs can produce fluent but incorrect outputs - a behavior known as “hallucination”. It’s not a defect; it’s a statistical byproduct. That’s why the key is designing for prevention, detection, and containment, not just hoping it won’t happen. NIST’s AI Risk Management guidance explicitly treats factuality and uncertainty as risks to be governed, not wished away. (ft.com)

Our layered safeguards include:

1.      Combining ML and LLM - By combining our AI model garden with proprietary machine learning techniques and fine-tuning on de-identified documents, we enhance the precision, consistency, and reliability of our outputs. This approach allows us to achieve higher accuracy in data extraction and document structuring than an off-the-shelf foundational model, all while maintaining the highest standards of data privacy and security.

2.     Reducing variability - We configure models with lower temperature and controlled decoding settings to ensure consistent, reliable outputs.

3.    Human-in-the-loop validation - For high-impact processes, responses are routed for review, following NIST’s AI Risk Management guidance.

4.    Grounded prompting - Every output is anchored to source documents or data through retrieval-augmented generation (RAG), improving traceability and auditability.

5.    Guardrails before and after generation - We use safety filters, prompt-attack shields, and programmable guardrails to block unsafe or non-compliant content.

At Docupath, we know that even the best AI models can sometimes produce convincing but incorrect answers, a common risk in generative AI. That’s why we’ve built a layered safety and accuracy framework instead of relying on a single safeguard.

We configure our models to favor consistency over creativity, add human review where accuracy really matters, and ensure that every answer is grounded in the original document or data source. On top of that, we use guardrails and automated checks to prevent off-topic, unsafe, or non-compliant responses. This balanced approach of combining automation with oversight aligns with NIST’s AI Risk Management Framework and gives our clients reliable, explainable, and auditable IDP outcomes.

Docupath is designed so that teams can benefit from advanced AI without needing to understand or manage it.

Under the hood, Docupath uses a model-garden architecture, a curated collection of foundational and specialized AI models. Each model is automatically selected and orchestrated for the specific task, from document classification to field extraction, so users never have to choose, train, or fine-tune models themselves.

All of this intelligence operates invisibly, using Docupath’s own fine-tuned machine learning layer built on de-identified data to continuously enhance accuracy, structure, and consistency, while ensuring no customer data is ever used to train external models.

Even transformation and rejection rules work the same way, users simply describe what they want in plain language, and Docupath automatically generates and applies the underlying logic. This allows teams to configure powerful data validation and processing flows without writing a single line of code.

In short, Docupath’s “right model for the right job” approach, combined with natural-language configurability, delivers enterprise-grade AI capability that’s accessible, safe, and effortless to adopt, no in-house AI expertise required.

AI accountability is real, measurable, and governed by well-established frameworks such as:

These define clear expectations for governance, oversight, and transparency - all directly applicable to intelligent document processing (IDP).

At Docupath, we believe that AI accountability isn’t optional, it’s measurable, documentable, and essential to earning trust. This is why we are building our platform and operations to align with the recognized governance frameworks.

In practice, this commitment shows up in several concrete ways:

AI document processing is no longer the future - it’s today’s competitive advantage. The challenge isn’t whether AI can be trusted, but how it’s designed, deployed, and governed.With the right safeguards, architecture, and ethics, AI can make teams faster, smarter, and more confident - exactly what Docupath was built to do.