Support agents that answer from your own records, and hand off before they guess.

Every support queue has the same shape: a long tail of hard cases and a mountain of the same questions. Where is my order. What does this policy cover. How do I change my plan. People answer those by hand all day, and the hard cases wait behind them.

A chatbot bolted onto a knowledge base answers fast and wrong. It invents a refund policy, promises a date nobody agreed to, and the first screenshot on social media costs more than the queue ever did.

The job is not to make an agent talk. It is to make one that reads your order system, your policy documents and your account records, answers only from them, and knows the exact line where it should stop.

The agent reads your systems. Code decides what it is allowed to say.

• Every answer is grounded in a record: the order, the policy clause, the account note. The agent cites it, and a reply with no source never ships.
• The actions it can take, a refund, a reschedule, an address change, are plain code with limits. The model asks for the action. Code checks the limit and runs it, or refuses.
• Hand-off is a rule, not a feeling. Low confidence, a dollar amount above the bar, an angry customer, a topic on the no-go list: any one of them routes to a person with the whole context attached.
• Tone, banned phrases and what it must never promise live in a spec your team edits, not in a prompt only an engineer can read.
• Every conversation is logged with the records it read and the rules it hit, so a supervisor can audit any reply in a minute.

Before it talks to a customer, it answers last quarter’s tickets, and your team grades it.

• We replay real tickets through the agent and compare its answers with what your best people actually said. The score you see is on cases it never trained on.
• We plant traps on purpose: a question the records cannot answer, a request past the refund limit, a policy that changed last month. Each one has to end in a hand-off, not a guess.
• The same walls hold in our shipped work: reviewer agents that fail closed for Lenovo, and a report engine for Acceleration Partners that rejects any sentence holding a number code did not place.
• The hand-off rate is a dial you set. Start strict, loosen one rule at a time as the graded replays earn it, and see what each setting would have done last month.

• Insurance

  Insurance, from the question a customer asks to the systems that answer it.

• Financial services

  AI for financial services, built to pass an audit.

• Franchising

  AI for franchise brands, researched at a scale no team does by hand.

• Generative AI
• Custom Software
• AI & Machine Learning