Customer Service AI That Knows When to Escalate
Deflect the routine volume without trapping anyone in a loop.
Most customer service AI fails the same way: it is measured on deflection rate, so it is tuned to avoid escalating, and it ends up holding onto conversations it cannot resolve. That produces a good dashboard and a worse experience than the queue it replaced. Senteras builds the opposite: systems grounded strictly in your own help content and ticket history, that answer confidently where they can and hand over immediately where they cannot.
Book a free consultationWhy this is usually broken
- Generic bots answer from a vendor corpus rather than your actual policies, so they are confidently wrong about your business
- Deflection-rate targets create systems that refuse to escalate, which is worse than no automation
- Ticket history contains the answer to most incoming questions and is almost never used as a knowledge source
- Customer data sent to a third-party model is a disclosure your privacy policy may not cover
How we build it
Grounded in your content only
Answers are generated from your help center, policies and resolved tickets, with a citation to the source. If the source does not cover it, the system says so instead of guessing.
Escalation as a first-class outcome
Confidence thresholds route to a human automatically. We measure resolution quality and escalation accuracy, not raw deflection, because deflection alone is trivially gamed.
Trained on resolved tickets
Your ticket history is the highest-quality training and retrieval corpus you own. Most teams leave it sitting in the helpdesk unused.
Deployed where your data lives
Runs on your infrastructure or your own cloud tenancy, so customer conversations never become a third-party disclosure.
What changes
Figures are drawn from Senteras engagements and are illustrative of typical results. Outcomes vary by data quality, infrastructure and scope.
Common questions
How is this different from the AI chat our helpdesk vendor already includes?
Bundled helpdesk AI is generally retrieval over your public help center with a fixed prompt. That works for questions your help center already answers well. It does not use your resolved ticket history, cannot enforce your escalation rules, and sends conversations to the vendor's model. The gap shows up on anything policy-specific.
What happens when the model does not know?
It escalates. The confidence threshold is a tuned parameter and it is deliberately conservative at launch, which means a higher escalation rate in the first weeks while the retrieval corpus fills the gaps it exposes.
Can it take actions, like issuing a refund?
Yes, through the agent layer, with approval gates on anything with a financial or irreversible effect. Most clients start read-only and add actions once the answer quality is proven.
Where we deploy this
Retail & Consumer Goods
Predict what customers want before they ask, and stock exactly what you need.
Technology & Software
Ship faster, support better, and build AI-native products your competitors can't match.
Insurance
Submission intake, claims triage and policy analysis on your own infrastructure.
Healthcare & Life Sciences
Administrative relief and clinical intelligence, plus helpdesk, security and backup, from one provider that treats a BAA as the starting point.
Education
Institutional AI that satisfies FERPA before it touches a student record.
The services behind it
Custom AI Agents & Automation
AI that doesn't just answer questions. It gets things done.
Local & On-Prem LLM Deployment
The most powerful AI models, running entirely on your hardware.
Model Fine-Tuning & Integration
Models that speak your industry's language, trained on your data.
Start with a conversation, not a proposal
Thirty minutes. We will tell you what we would change first, and whether you need us at all.
Book a callThe firm behind the firm