Application and form extraction
Capture specified information from incoming forms while retaining the relevant source page or passage. Validate dates and identifiers, and distinguish a missing answer from an extraction failure.
Data & decision intelligence
Document AI
Extract and organise useful information from complex documents for review and use.
Built around a specific business need.
Documents contain valuable information in formats that ordinary databases cannot use directly. An invoice, contract, application or technical report may mix tables, narrative, handwritten content and inconsistent layouts. Extracting a plausible-looking value is only part of the challenge; the business also needs to know where it came from and whether it is reliable enough to use.
Our Document AI work focuses on turning selected document content into reviewable structured information. We define the fields and decisions that matter, test extraction against representative examples and keep source evidence close to each result. Uncertainty is routed for review rather than concealed behind a clean spreadsheet.
Who this is for
- Operations teams repeatedly reading documents to populate business records.
- Organisations classifying large document collections for review or retrieval.
- Product teams adding structured document intake to existing applications.
Where this can make a practical difference.
Invoice or statement preparation
Extract agreed fields and line items for review against supporting records. Treat totals, currencies and supplier references as separate validation questions rather than assuming a document-shaped output is correct.
Contract and report review assistance
Identify passages relevant to a defined review question and organise them for a specialist. Preserve qualifications and exceptions instead of reducing complex wording to an unsupported yes-or-no conclusion.
How we approach the work.
Define the extraction contract
Specify required fields, formats and downstream use. Decide which errors are tolerable, which require review and what should happen when a document is unsupported.
Build a representative sample
Include different layouts, scan quality, languages and difficult cases. Create reviewed reference answers so performance can be assessed field by field rather than through attractive demonstrations.
Design processing and validation
Combine suitable text recognition, extraction and deterministic checks. Keep document type, source references and processing version with the result to support investigation and correction.
Create the review experience
Show uncertain values beside the source evidence. Enable corrections, record reviewer decisions and prevent unreviewed material from entering consequential workflows without the agreed approval.
Evaluate and connect
Measure performance on held-out examples and review failure patterns. Integrate approved results with the destination system, including duplicate handling and retry-safe processing.
The sequence is tailored to the agreed scope, dependencies and risk. It describes our delivery approach, not a fixed timeline or performance guarantee.
What the engagement can deliver.
- Document taxonomy and field-level extraction specification.
- Reviewed example set and evaluation results.
- Processing pipeline with validation and provenance.
- Human-review interface and exception queue.
- Destination integration and operational handling guidance.
The proposal identifies the deliverables, acceptance evidence, responsibilities and support arrangements included in your engagement.
Measure what matters to this workflow.
Field-level accuracy
Compare extracted values with reviewed references, separating critical fields from less consequential information.
Review burden
Measure how much human checking is required to reach an acceptable, usable result.
Unsupported-document handling
Test whether unfamiliar layouts or poor-quality documents are recognised and routed appropriately.
These are measures to discuss and baseline. Targets are agreed for the specific system and data; no universal improvement percentage is assumed.
What to prepare for discovery.
- Representative documents that may legitimately be processed.
- A field specification and examples of acceptable outputs.
- Reviewers able to resolve ambiguous source information.
Questions worth asking.
Is a model confidence score enough to approve a result?
No. Scores need interpretation and evaluation for the actual task. Validation rules, source quality and the consequence of an error should also determine whether human review is required.
Can you process scanned and handwritten documents?
We assess the real samples before committing to scope. Legibility, layout and language can materially affect results; difficult documents may require a different intake route or additional review.
How is this different from document workflow automation?
Document AI interprets and structures selected content. A workflow determines routing, approvals and subsequent action. The two can work together, but extraction quality and process correctness require separate evaluation.