A custom AI customer service agent built on Beast Productions' agent framework — resolving 78% of support tickets autonomously from day one, saving 600+ hours per month and cutting average response time from 4 hours to 38 seconds.
The client ran a B2B SaaS platform with 8,000 active customers and a support team of six. Average response time was 4 hours. Customer satisfaction scores were declining. The team was spending 70% of their time on repetitive, answerable queries — password resets, billing questions, API documentation lookups, feature availability checks — leaving complex, high-value issues to pile up.
They'd tried Intercom's basic bot and a couple of off-the-shelf AI tools. Neither had the accuracy or contextual awareness to handle real customer queries confidently. Customers saw through them immediately. They needed something that actually worked — not a glorified FAQ widget.
Orbit is a custom AI support agent built on Beast Productions' agent framework, deeply integrated with the client's Intercom workspace, internal knowledge base, account database, and billing system. It doesn't just search FAQs — it can look up individual customer account data in real time, check subscription status, query the documentation, cross-reference known issues, and take actions like resending emails or applying credits.
The knowledge layer is built on a RAG (Retrieval Augmented Generation) system with a vector database containing the full support documentation, historical ticket resolutions, product changelog, and a custom escalation decision tree. Every response is grounded in real data — Orbit never hallucinates a policy or feature that doesn't exist.
Critically, Orbit knows its own limits. It's tuned with a conservative escalation threshold — if it detects ambiguity, frustration signals, billing disputes above a threshold, or anything outside its training, it hands off to a human immediately with a full conversation summary pre-written for the agent picking it up.
Orbit resolved 78% of tickets autonomously from day one — without any warmup period. Average response time dropped from 4 hours to 38 seconds. Customer satisfaction scores actually improved, rising from 3.8 to 4.6 stars, because customers were getting instant, accurate, personalised answers rather than slow generic ones. The support team was freed to focus entirely on complex, relationship-level issues — and headcount growth was paused for 12 months as a direct result of the efficiency gain.