A live AI system needs someone watching whether it is still as accurate as on day one
For organizations using AI-OCR or computer vision in daily operations, the team assesses the system, data, and support model before proposing service scope and response targets.
There is no outage alarm — you usually find out after the team has stopped trusting it
Documents and forms change without anyone telling you
A supplier changes their invoice layout, or purchasing adds a field — and a system that used to read cleanly starts missing specific fields.
Shop-floor conditions shift by season and shift
Changed lighting, a camera that got nudged, or a new part variant will all skew visual inspection results.
Nobody looks at the accuracy number regularly
With no recurring report, the first signal is a complaint or a customer-visible error.
Whoever set it up moved team or left
The knowledge of how to tune and maintain it sat with one or two people; once they are gone, it stays frozen at its original settings.
What happens each month
Track accuracy and volume every month
Compare system output with human corrections to track trends. Alert thresholds are defined after a baseline exists and are confirmed in service scope.
Retrain when documents or conditions change
We gather real-world misreads, adapt the model to the new patterns, and test before anything goes live.
A monthly report a manager can read
Volume, accuracy, the fields humans corrected most, and what we recommend changing next month.
Define incident levels and response handling together
Response targets, service hours, contact channels, and fees are stated in the engagement-specific proposal before work begins.
| Severity | Example situation | How service is confirmed |
|---|---|---|
| Critical | The system is down, or output is wrong enough to be unusable | Channels and response targets are confirmed in the engagement proposal |
| High | Output quality changes from baseline, or a document group stops processing | Thresholds and response targets are defined from the actual system in the proposal |
| Normal | Configuration change, a new document layout, or a usage question | Queue and delivery timing are confirmed per request |
If your setup matches these
- An AI-OCR or computer vision system already in production
- Its output is used in daily operations, not just trials
- No internal team dedicated to maintaining the model
- You need a report you can show management or an auditor
We will say plainly if you do not need this yet
- The system is still in development and not yet live
- You already have an in-house data science team maintaining it
- Usage is occasional and low-volume enough that drift does not matter
What gets asked before signing a monthly contract
Does it have to be a system PM INNOSOFT built?
No. The team can first assess systems built by others, then state what can be supported, what access or data is required, and any constraints.
Does retraining require real customer data?
Data sources, processing location, transfers, and confidentiality controls must be defined in the project scope and data agreement before work begins.
When can we cancel?
Cancellation, notice, and exit deliverables must be confirmed in the engagement-specific proposal or agreement.
What if accuracy misses the agreed target?
Thresholds, measurement, remediation approach, timing, and fees must be based on the system's measured baseline and confirmed in writing for that engagement.
Find out whether what you run today is still as accurate as it was
Start with a review of the system's data and recent output. The team then confirms scope, method, timing, and fees so you can decide before work begins.
