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TürkçeNot every task needs AI. The right ones do.
We start with the work itself: who provides information, who checks it, and where do questions arise? Only then do we decide whether rules, interfaces or an AI model would actually help.
Endurion Automate · Endurion Relay
Process documents sensibly
AI can propose values from quotes, delivery notes or emails that lack a fixed data structure. Business rules and designated people review those suggestions before further processing.
Control data and permissions
Which data may be processed, who approves it and where will it be stored? We define these boundaries before granting system access, explicitly addressing personal and confidential content.
Prove outcomes, not promises
During the pilot, we compare results with reviewed examples and record misclassifications, queries, rework and costs. These findings inform whether the workflow should enter production and at what scope.
AI terminology in plain language.
API
A documented way for systems to exchange data. Example: update a ticket after approval.
OCR
Text recognition in images or scanned PDFs. Example: read an invoice number and flag uncertainty.
RAG
AI answers grounded in retrieved documents. Example: cite the current maintenance manual.
Human in the Loop
A human reviews a consequential step. Example: approve an invoice mismatch before posting.
Halluzination
A plausible but incorrect AI claim. Example: inventing a policy that does not exist.
Workflow
A defined sequence of actions and decisions. Example: intake → review → approval → ERP.
Webhook
Event message between systems. Example: new ticket triggers a notification.
Audit-Log
Traceable record of actions. Example: who approved which data and when?
DSGVO / GDPR
Rules for personal-data processing. Example: minimize data and define deletion.
SLA
Agreed service targets. Example: respond to P1 incidents in a defined period.
Pilot / Proof of Value
Limited test with measurable criteria. Example: process 100 anonymized cases.
ROI
Return on investment, considering setup, licences and operations, not just time saved.
Decide before the first model call.
An AI integration also affects data access, provider terms and operating costs. The pilot must review these alongside task accuracy.
| Decision point | What to clarify | Delivery outcome |
|---|---|---|
| Data | Which content may be passed to which service? | Permitted data categories, locations and retention. |
| Quality | Which errors are acceptable and which require a hold? | Test cases with expected outcomes and a review stage. |
| Operations | What happens on timeout, provider failure or model changes? | Failure path, owner and measurable usage budget. |
Start with one real process.
Bring a sample input that fixed rules cannot handle. We review data quality, validation steps and integration with your existing system.