For an executive team, the cost rarely shows up as a single number. It shows up as headcount that grows with volume, customers who wait days for an answer, and skilled people doing data entry. It shows up as errors that surface late and cost more to fix. It also carries risk: sensitive data moving through inboxes and spreadsheets, and outcomes that are hard to trace.
How much of our document work is still handled by hand, and what does one document cost us?
For any outcome, can we show where the data came from and what decided it?
If volumes doubled next year, would our costs double too?
If the answers are unclear, or the answer to the third is yes, the process is a candidate for change.
Many document AI pilots prove that data can be extracted from a clean set of samples, then stop. Real documents vary more than samples do. Security and legal teams ask where data is processed and who can see it. Audit asks how a result can be explained. A demo tells you little about any of this. What matters is whether the output can be trusted, governed and run at scale.
The Cegeka Document AI Accelerator is a production-grade foundation for document processing, from intake to delivery of data into your own systems.
It reads scans, photos, emails and handwritten forms, and splits files that contain several documents. It classifies each document, extracts the data you need and validates it before delivery. Cases the system is unsure about go to a person for review, and their corrections are captured so accuracy improves over time. Every step is logged, and every result links back to its source document and the model version that produced it.
It runs in your cloud, on your premises or with Cegeka. Model components can be swapped, so you are not tied to one vendor. Access can be controlled down to field level where the data requires it.
A European healthcare insurer processes more than 200,000 sensitive documents every day. Strict confidentiality rules ruled out public cloud services and external AI models, so the full pipeline runs on confidential computing infrastructure and uses self-hosted AI models. Document classification reaches 92.9% accuracy, and the correct type is among the system's top three suggestions in over 98% of cases. Low-confidence results go to human review.
A regional bank focused first on transforming a manual document process involving large volumes of scanned files, each containing several document types. The solution now splits them, identifies each type, extracts the relevant data and passes it into the existing process. The bank has lowered its cost per page and shortened processing times, and it is extending the approach step by step.
Our approach has been shaped by work in document-intensive environments, where success depends on more than proving a single use case. We start with one contained process, establish the controls needed for reliable operation and create a foundation that can support further document workflows.
The central question is not simply whether AI can extract information from a document. The real test is whether an organization can use that output reliably, with appropriate governance and at the required scale.
At Cegeka, we start with a process painful enough to matter and contained enough to improve—then design for the controls and scale needed beyond the first process.
Is a document process causing delays, manual work or compliance concerns? Visit our Document Processing AI page to get in touch with our team.