AI and documents
A model can prepare material, but the system still needs to control sources, output format and cases requiring approval. We embed these capabilities in the current document or request flow.
Discuss AI automationScope
The signals help identify the problem. Scope and result show what RUNSY can own technically.
Signals
Scope
Result
Related situations
See how this area connects with specific problems in processes, data and systems.
Knowledge
Problem
Procedures and decisions are scattered across documents, email and business systems. The answer depends on who happens to be available.
What we change
We organize the sources, connect them to search and show which document supports each answer. Questions without a reliable answer go to the right person.
After the change
The team finds answers faster and can verify the source document.
Documents
Problem
Documents, requests or case descriptions must be read, classified and turned into a proposed next step.
What we change
The model extracts the required data and prepares a proposal. The system checks agreed rules, and a person approves the result where an error would matter.
After the change
Each case takes less time to prepare, and the system records who approved the result and why.
Migration
Problem
In a large migration, some mappings are prepared by hand and data errors may surface only when the new system goes live.
What we change
We keep migration rules in the repository, run trial migrations and compare results. Exceptions and rejected records require explicit approval.
After the change
Before the switch, the team knows what moved, what was rejected and who approved each exception.
Let's talk
Tell us where the team loses time, which systems are involved and what needs to work differently. That is enough for a first conversation.
Contact
Send a few sentences about the process, systems and expected result. That is enough to see whether the first step is integration, AI automation, an audit or an operational application.
