Challenge
The organization operated at 2M monthly calls with fragmented tooling and unclear ownership between security, platform, and application teams. Incidents were detected late; changes lacked measurable acceptance criteria.
Latency-Optimized Call Center AI Assistant
The organization operated at 2M monthly calls with fragmented tooling and unclear ownership between security, platform, and application teams. Incidents were de…
Client profile
- Sector
- Telecom
- Scale
- 2M monthly calls
Related service
/services/ai-solutionsEngineering approach
Arekan mapped trust boundaries, data flows, and failure modes before writing code or rules. We ran staged pilots with rollback plans, defined SLOs for detection or retrieval quality, and aligned deliverables to audit evidence requirements.
Solution
We implemented production-ready components using OpenAI, Redis semantic cache, Node.js with infrastructure-as-code, monitored rollouts, and handover runbooks. Customer identities remain confidential; this case reflects a composite of anonymized enterprise engagements.
Implementation process
- 1.Discovery and asset inventory
- 2.Architecture design and threat modeling
- 3.Pilot implementation in staging
- 4.Production rollout with canary validation
- 5.Handover, training, and continuous improvement roadmap
Results
-62% average reduction
Detection / response MTTR
-41% in first 90 days
False positive triage volume
-55% manual effort
Audit evidence preparation time
99.97% maintained
Platform availability during migration
Technologies
OpenAIRedis semantic cacheNode.jsTwilio
Stack selected for interoperability, operability, and audit evidence.
Customer identities are confidential. Metrics represent anonymized composite outcomes from Arekan delivery work.