38,000
tickets migrated into a structured knowledge base
Migration scope is documented: 38,000 tickets / 85,000 messages / 123,000 data points. Automation is a Target KPI — 80% of routine queries — not a realized result: the engagement is active (Phase 2), deploying against the 38,000-ticket backlog, so a final deflection rate is not published yet.
The problem
How it worked before
What we built
What changed
System shape
Inputs
38,000 historical tickets / 85,000 messages
Processing
Semantic memory + classification over a vector store
Outputs
Pre-submit ticket deflection + suggested knowledge-base answers
Method
- 1Map: AI-led audit of the support operation to surface the CEO bottleneck and shadow systems
- 2Structure: a 4-level documentation framework; 38,000 tickets moved into a structured knowledge base
- 3Implement: distributed-agent architecture over a PGLite / pgvector semantic memory layer
- 4Adapt: an exception-handling rule protocol so the system retrains on edge cases instead of escalating them
Constraints
- Persian / RTL language throughout
- 123,000 distinct data points migrated securely
- Local data-security compliance (AFTA certification)
Our role
Why this is anonymized
Client identity is withheld under engagement confidentiality. The metrics are real and verifiable in a private reference call.
Have an operational document you want analyzed?
We do not run standard sales calls. Send your most complex operational document and an engineer will return a clear model of what automating it is worth.
Request an operational diagnostic