AI Applications & Knowledge

Document Intelligence

In claims, trade, finance and legal operations the work is not decided in a system of record. It is decided in a PDF someone emailed. We build the extraction, classification and routing that turns that flow into structured data your systems can act on.

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The business problem

Templates break the moment a supplier changes their layout

Traditional document capture works by template, so it handles the documents you anticipated and fails on the rest. Every new supplier, format change or unusual case becomes a configuration task, and the exception queue grows faster than the team clearing it. The result is a process that is nominally automated and actually staffed by people fixing what the automation could not read.

What we do

Extract by meaning, verify by rule

We extract fields by what they mean rather than where they sit, so a layout change does not break the pipeline. Every extracted value is then checked against rules you already have: totals reconcile, dates are plausible, the party exists in your master data. Anything that fails validation, or that the system is not confident about, goes to a person with the source document and the extracted value side by side. Confidence thresholds are yours to set, and we tune them against your own tolerance for review effort.

Document Intelligence

Capabilities

Intelligent Document Processing
OCR
Document Classification
Data Extraction
Contract Analysis
Invoice Processing
Form Processing

Common use cases

Process supplier invoices against purchase orders and route only genuine mismatches for review.
Read first notification of loss documents and open the claim with fields already populated.
Extract obligations, dates and parties from executed contracts into a register.
Classify and route inbound correspondence to the right team without a person reading it first.

How we deliver

Shape

Turn the request into a specification: who uses it, what a correct answer is, who decides.
Ground

Connect to the content and systems that hold the answers, respecting existing permissions.
Evaluate

Score against a labelled set built from your own cases, before anyone outside sees it.
Ship & operate

Staged release with monitoring, cost control and a regression suite that guards quality.

Technology

OpenAI
Anthropic
Azure OpenAI
pgvector
Elasticsearch
Microsoft 365
SharePoint
Confluence
Salesforce

Security & governance

Answers are grounded in your own content and carry citations, so a reader can check them. Retrieval respects the permissions already set on the source, which means a user never sees through the application what they could not see directly. Prompts, retrieved context and responses are logged for audit, and evaluation runs continuously rather than once at launch.

Engagement models

AI Project

We take responsibility for designing and delivering a defined AI solution.

Dedicated AI Team

Long-term dedicated engineering capacity built around your stack and delivery model.

Managed AI

We operate, monitor and continuously improve production AI systems.

Why TeamExtension.ai

We design the exception path first

Document automation is judged on what it does with the awkward ten percent, not the easy ninety. We build the review queue, the confidence thresholds and the audit trail as part of the system rather than as an afterthought, because that is what determines whether your team ends up trusting it or working around it.

Selected clients

Frequently asked questions

What accuracy can we expect?

It depends on document quality and field type, so we measure on your documents before quoting a number. More usefully, accuracy is a dial: raising the confidence threshold sends more to review and fewer errors through. We tune that with you against the cost of each.

Do we need to label training data?

Usually not for extraction, which works from the document itself. We do need a modest labelled set for evaluation, typically a few hundred documents, because that is how accuracy gets measured rather than assumed.

What about handwriting and poor scans?

Both reduce accuracy, and both are measurable. We test on your worst documents rather than your best, and set the confidence threshold accordingly so poor inputs route to a person instead of producing a confident wrong value.

Can it handle a new document type without redevelopment?

New layouts of a known type, generally yes, because extraction is not template-bound. A genuinely new document type with different fields needs configuration, but that is a definition task rather than a development one.

Where does the data go?

Into whatever system owns the process: your ERP, claims platform or case management tool, through its API. The extraction layer is not a destination, and we do not build one.

Related capabilities

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Automate the cross-system processes that rule engines could never handle reliably.

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Agentic AI & Automation

AI Agents

Software that completes multi-step work inside your systems, under your access controls and your approval rules.

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AI Data & Models

AI Data Engineering

The pipelines, quality controls and vector stores that AI systems depend on to be correct.

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Discuss Your AI Initiative

Extract, classify and route the documents that currently move through people's inboxes.

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