I help SaaS and B2B teams design, train, and operate AI support agents that handle real customer conversations without creating risk, noise, or extra human work. This work is grounded in running AI-first support systems in production, not demos or theory.
Lets work togetherMost AI support failures are not model problems. They are training, scope, and ownership problems.
I work with teams to train AI support agents that; follow policy consistently, know when to escalate, handle edge cases without guessing, improve over time instead of drifting.
This is about turning an LLM into a reliable support operator, not a chatbot.
I help define and implement; core system prompts and behavioral constraints, role clarity (what the AI can and cannot do), tone control without personality drift, refusal rules and escalation logic, consistency across conversations.
An AI agent is only as good as its inputs. I work with teams to; audit and restructure help content for AI consumption, identify gaps that cause hallucinations, define source-of-truth hierarchies, prevent contradictory or outdated guidance from leaking into responses.
AI support does not eliminate humans. It changes their role. I help design; clean handoffs from AI to human agents, escalation triggers that make sense, internal notes and summaries generated by the AI, workflows where humans review outcomes, not rework answers.
Support is where AI risk shows up first. I advise on; billing and refund boundaries, account changes and permissions, security, compliance, and privacy constraints, preventing AI from making commitments it cannot keep.
generic prompt templates, fine-tuning as a solution, AI optimism without accountability
repeatable behaviour, measurable outcomes, support load reduction, customer trust
For teams already using AI in support. Review current AI behaviour and failure modes, identify training gaps and risk areas, clear recommendations on what to fix first.
For teams building or restarting their AI support agent. System prompt and instruction design, knowledge base alignment, escalation and refusal rules, initial performance benchmarks.
For teams running AI support at scale. Drift detection and prompt refinement, expansion into new support scenarios, policy updates as the business evolves, regular reviews tied to real conversations.