Customer
Support
Reduce support costs and improve customer satisfaction with AI agents onReinforce Engine.
AI Agent Cost
Deploying customer-facing LLMs with leading accuracy and safety.
Quality customer support is critical to business success. Automating support with LLMs reduces costs while improving response time and customer satisfaction.
However, even frontier models struggle to provide accurate answers on company-specific topics.
Use task-specific AI agents for accurate, efficient, and personalized support.
Reinforcement fine-tuned adapters that share a model backbone deliver unparalleled specialization for support use cases, while minimizing GPU requirements.
Agents can be trained on synthetic data, ensuring safety while increasing speed-to-production.
CS Workflow
TRAINING INSTRUCTIONS
AI JUDGE
CSAT
FOR ESCALATION RATE
Improve support quality
When prompt engineering isn't enough, tune models to follow your unique customer support guidelines.
Speed to production
Shorten time to production with customizable AI judges that evaluate whether models adhere to your support policies.
Reduce support cost
Reduce cost for agentic workflows with adapters that deliver personalized support while minimizing GPU needs.
Put support on autopilot — safely.
See a tuned agent answer your real tickets, with the escalation rate to prove it.
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Use CasesQuestions teams ask about customer support
What does customer support actually involve?
We design the workflow, build the agent and the evaluation around it, run it in shadow mode against real traffic, then hand it over with a runbook. You end up owning a running system for customer support plus the evidence it works, not a proof of concept that needs rebuilding.
How long before customer support is live?
Four weeks is the standard implementation: discovery in week one, environments in week two, a shadow-mode pilot in week three, handover in week four. Most teams see their first agent running against real data by week three.
Do we need an ML team to run this?
No. Most clients start with strong software engineers and no ML specialists. The engagement is built so your existing team owns the system afterwards — we train them while we build rather than handing over documentation at the end.
How do you know it is working?
Every system ships with an evaluation suite: golden datasets built from your real cases, rubric-driven scoring, and regression gates in CI. Quality becomes a number you track per release rather than an opinion, and drift pages you the way a failing test would.
What happens when the agent gets it wrong?
Low-confidence and high-stakes cases route to a human queue by design. Every failure is captured with full trace context, and those traces become new evaluation cases, so the same mistake is caught automatically next time rather than recurring.
Does this run in our environment or yours?
Yours. Deployment happens inside your cloud perimeter, against your data stores and your identity provider. We integrate with the stack you already run rather than asking you to move anything into ours.
Who owns what we build?
You do. Fine-tuned weights, datasets, evaluation suites and runbooks are yours, handed over at the end of the engagement. There is no lock-in that requires us to keep the system running.
How is this priced?
A platform subscription plus a fixed-scope implementation fee, quoted in writing before work starts. Implementation is priced by engagement rather than by the hour, so a slower week costs you nothing extra.
We tried something like this before and it failed. Why would this be different?
Most failures are not model failures — they are missing evaluation, no human fallback, and no way to tell whether a change made things better. Those are the parts we build first. If we cannot define how success is measured for customer support, we say so before taking the work.
What do you need from our team?
One process owner who knows the workflow end to end, one engineer with access to the systems being integrated, and a weekly 45-minute review. That is genuinely it — no standing project committee.