Artificial Intelligence · Build
We automate document handling end to end — capture, extraction, validation against your systems, and review of the cases that need a person. Extracted values are checked against master data, purchase orders and business rules before anything is posted, so the control boundary around financial approval is never bypassed. We measure the realistic straight-through rate on a sample of your own documents rather than quoting a vendor headline figure. Uncertain or failed documents are queued for review with the reason shown, and corrections feed back to improve the system.
In short
Intelligent document processing extracts and validates data from invoices, forms, contracts and scanned records. Extraction without validation produces data that looks precise and cannot be trusted.
Start a conversationWhat we do
We handle scans, photos and PDFs, and improve the capture step where image quality is the real constraint on accuracy. Poor capture cannot be fully recovered downstream, so it is worth fixing at source. Getting this right raises the ceiling on everything the rest of the pipeline can achieve.
We extract the fields that matter with a confidence score each, across the varying layouts and suppliers your documents come from. Confidence is what allows clean documents to pass straight through while doubtful ones are held for review. Handling layout variety is where generic tools tend to fall short.
We check extracted values against master data, purchase orders and business rules before anything is posted. This catches errors and duplicates that extraction alone would wave through. It also keeps automation inside the financial control boundary rather than around it.
We queue uncertain or failed documents for review with the reason for the exception shown, so a person can resolve it quickly. Corrections are captured and used to improve extraction and validation over time. The queue makes the system's uncertainty visible rather than hiding it behind a confident guess.
What you receive
Documented output your teams keep and can act on without us in the room.
When this work helps
How an engagement runs
Understand the workflow, the data available, the constraints, and what a good outcome would look like. We establish the problem precisely enough to judge whether AI is even the right instrument for it.
Review data protection, security, oversight, and feasibility for the specific use cases on the table. We report which cases are worth building, which are better solved another way, and what would have to be true to proceed safely.
Set out the approach, controls, governance, and evaluation measures before build begins. Deciding data boundaries, oversight, and how success will be measured up front keeps them from becoming retrofits.
Support implementation and evaluate performance, error modes, and control effectiveness in use. We measure the system against the criteria agreed at design, and report honestly where it falls short.
Where we work
Fidova advises on AI adoption and AI governance from Delhi, India, and works with organisations internationally, including teams that must satisfy overlapping regimes such as India's data protection law, the EU AI Act, and GDPR.
Engagements run in English, are scheduled around your time zone, and can be delivered remotely end to end, with on-site time planned where a phase genuinely benefits from being in the room.
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Questions
Straight answers to what buyers ask us before an engagement begins.
Enquiries
Your enquiry lands in the same inbox as every other on this site and is read by us. Tell us the requirement and we will reply with a practical next step.
Prefer email? contact@fidovaadvisory.com
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