Customer Support Automation
Resolve the routine, escalate the rest — support agents grounded in your knowledge and systems that actually close tickets, not just chat.
The problem
Support volume outpaces headcount, and a bolted-on bot that can’t see an order or a policy just frustrates customers. Deflection only helps if the AI actually resolves — and hands off cleanly when it shouldn’t.
What it includes
The capabilities we build into this solution — engineered for production, not a demo.
Grounded answers (RAG)
Responses sourced from your docs and policies, not guesswork.
Tool & system access
Look up orders, process changes, open tickets — real actions.
Voice & chat
One assistant across chat, email, and voice channels.
Graceful escalation
Hands off to an agent with full context when it should.
Guardrails
On-brand, in-scope, and honest about limits.
Analytics
See what customers ask and where to improve.
How we'd build it
The same method behind everything we ship: de-risked, measured, and in production in one quarter.
Feasibility
We validate the use case on your real data, define the accuracy bar and ROI, and give you an honest go/no-go — usually in weeks.
Build & evaluate
We engineer the system and score it against a labelled set until it clears the bar — quality is a measured number, not a promise.
Deploy
We ship to production, integrated with your systems and behind the right human-approval gates, with monitoring from day one.
Operate & improve
We watch it in production, handle drift, and expand scope as trust builds — feasibility in 6 weeks, production in ~90 days.
What you get
- Real ticket resolution, not just deflection theater
- Grounded, on-brand answers across channels
- Clean human handoff with full context
- Insight into demand and gaps to keep improving
Production AI we've shipped
We'd build your solution with the same discipline behind these real, in-production engagements — described by sector under NDA.
A production document classifier — 5,000+ documents/month, built to a ≥95% accuracy target and live in 8 weeks.
Read the case studyBank-statement automation across 21 banks with a measured 0.24% transaction-direction error rate.
Read the case studyAI-at-POS recommendations running against live in-store transactions across every location.
Read the case studyWant to scope Customer Support Automation?
Tell us the use case. We'll come back with a feasibility view and an honest go/no-go — a working prototype in about six weeks.
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Location
Barcelona, Spain • Lisbon, Portugal • Sheridan, WY, USA (incorporation)
Why Choose CONE RED?
- AI R&D lab & venture builder — production systems, not slideware
- Feasibility in 6 weeks, production in ~90 days
- Accuracy measured and evaluated — never invented or guaranteed
- Real engagements across financial services, healthcare & retail
- End-to-end support from strategy to deployment
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