Document extraction and classification
Assess OCR and extraction approaches using representative documents. Define the fields, document types and validation rules required, including how incomplete, unreadable or unusual inputs should be handled.
AI integration with a defined business purpose.
AI is useful when it addresses a specific task and its output can be assessed. Corvanta Systems integrates document extraction, classification and knowledge assistance into South African business workflows, with attention to data access, review requirements and the consequences of an incorrect result.
UNDERSTAND THE PROBLEM
Turn document handling into reviewable work.
A delivery note, invoice or supporting document may contain useful information, but staff still need to read it, capture it and check it against other records. An AI-assisted workflow can prepare structured information for review. The design must keep the source available and make uncertainty visible.
What your system can include.
Review before consequential actions
Connect proposed outputs to supporting source material and a review workflow. Use agreed checks and exception rules before information is accepted or used to advance a business process.
An appropriate deployment approach
Evaluate approved hosted services or local inference where practical. Consider document sensitivity, available hardware, provider terms, operating cost and the quality required for the particular task.
FROM REQUIREMENT TO WORKFLOW
How we build and verifyMake the work traceable.
Define the task and consequences of errors.
Evaluate representative inputs and required outputs.
Build source checks and human review into the workflow.
Measure results and agree ongoing review.
The workflow, permissions and acceptance checks are agreed around your operation before the build.
SCOPE THE RIGHT SYSTEM
What we need to understand.
A useful first conversation starts with the work you need to control. These details help define the scope, dependencies and rollout.
Project scoping and engagements- Representative documents and examples of difficult or failed cases.
- Required output fields and the checks that establish correctness.
- Data restrictions, review owners and deployment constraints.
Practical questions.
Can AI outputs be treated as automatically correct?
No. Extraction and generated answers can be wrong. The workflow should apply relevant validation, preserve supporting evidence and require human review where the consequences or uncertainty justify it.
Can the AI run locally?
Local processing can be assessed where data or connectivity requirements favour it. Feasibility depends on hardware, the model, the document volume and acceptable response time. A representative evaluation should guide that decision.
Build the wider operation.
Serving businesses in South Africa. Integration options depend on the systems, permissions and provider access available for your project.
BUILT AROUND YOUR BUSINESS
Start with the operation.
Tell us what your team needs to do, where the current process breaks down and what a better system should change.