R
Link preview
RelayOps - Production-shaped telecom support agent (54% auto-resolve, 0 unsafe actions, full audit + decision console) [P]
I just open-sourced RelayOps - a small, honest, production-shaped AI support agent built specifically for telecom and subscription billing queues.Key results (v1.5.1): 54% of a 50-ticket sample queue auto-resolved 0 unsafe auto-actions 0 billing escapes (tested on 12 adversarial billing/account abuse cases) Safe-route rate 1.000 on 100 hand-written adversarial cases Deterministic access gate + server-side scoped tools + layered guardrail + durable SQLite audit store + Decision Console + Handoff Queue Tech stack: Fine-tuned Qwen2.5-1.5B LoRA (published on HF) as Tier-1 intent classifier Hybrid BM25+TF-IDF/RRF RAG with citations Independent guardrail that blocks hallucinated pricing/offers Full per-turn decision traces (what was known + what was unavailable) Action policy table (blast radius × reversibility) Everything is reproducible, heavily evaluated, and the README is brutally honest about synthetic-data caveats and pending reruns.Live demo (Streamlit): https://relayops-production.up.railway.app GitHub: https://github.com/patibandlavenkatamanideep/relayops I'm actively looking for design partners who run real support queues. Drop a small redacted sample of your tickets and I’ll run the exact same batch evaluation on your data and send back the full report (auto-resolve %, safety metrics, audit export, time-saved estimate). Zero cost, zero production access required. Would love feedback from the community especially on the calibration/safety routing layer, the audit ledger format, or the guardrail design. Let me know what you think! submitted by /u/Fit_Fortune953 [link] [Kommentare] reddit.com · reddit.com ↗
I just open-sourced RelayOps - a small, honest, production-shaped AI support agent built specifically for telecom and subscription billing queues.Key results (v1.5.1): 54% of a 50-ticket sample queue auto-resolved 0 unsafe auto-actions 0 billing escapes (tested on 12 adversarial billing/account abuse cases) Safe-route rate 1.000 on 100 hand-written adversarial cases Deterministic access gate + server-side scoped tools + layered guardrail + durable SQLite audit store + Decision Console + Handoff Queue Tech stack: Fine-tuned Qwen2.5-1.5B LoRA (published on HF) as Tier-1 intent classifier Hybrid BM25+TF-IDF/RRF RAG with citations Independent guardrail that blocks hallucinated pricing/offers Full per-turn decision traces (what was known + what was unavailable) Action policy table (blast radius × reversibility) Everything is reproducible, heavily evaluated, and the README is brutally honest about synthetic-data caveats and pending reruns.Live demo (Streamlit): https://relayops-production.up.railway.app GitHub: https://github.com/patibandlavenkatamanideep/relayops I'm actively looking for design partners who run real support queues. Drop a small redacted sample of your tickets and I’ll run the exact same batch evaluation on your data and send back the full report (auto-resolve %, safety metrics, audit export, time-saved estimate). Zero cost, zero production access required. Would love feedback from the community especially on the calibration/safety routing layer, the audit ledger format, or the guardrail design. Let me know what you think! submitted by /u/Fit_Fortune953 [link] [Kommentare]
Comments