AI agents for customer support and operations that reach production

Most agent projects stall between the demo and the support queue

A demo answers ten curated questions. Production answers whatever customers type at 2 a.m., against a knowledge base that is half out of date, while a CRM lookup times out and nobody owns the quality number. That gap is where agent projects stall: the pilot looks good, nobody can say how often it is right, and the team is reluctant to point it at real tickets. We close the gap by writing the evaluation set before the agent, connecting it to your actual systems with scoped permissions, and agreeing in advance on when it hands over to a person. An agent is accepted when it passes the evals, not when it sounds convincing.

• 01Scoping document with the target tickets or tasks, the success metric, a risk list and escalation rules
• 02Eval set built from your real conversations and used as the acceptance criteria for launch
• 03Agent with tool access to your helpdesk, CRM, order system or WhatsApp Business, using scoped credentials
• 04Retrieval over your knowledge base and policies, with source citations in the answers
• 05Guardrails, human handoff and an audit log of every action the agent takes
• 06Dashboard for cost, quality and escalation rate, plus a runbook for your team
• 07AgentOps after launch: monitoring, regression evals on every model or prompt change, and monthly review

• Support agent for order and account questions

  A support team answers the same order-status, return and billing questions all day. The agent resolves the ones that have a clear answer in your helpdesk and order system, cites where each answer came from, and passes everything else to a person with the full conversation attached.

• Intake agent for inbound requests

  Requests arrive through a web form, email and chat with uneven detail. The agent asks for what is missing, classifies the request against your own criteria and routes it to the right owner with a short brief, so nobody starts from a blank thread.

• Internal copilot for sales operations

  Account executives lose time on CRM updates and quote requests. The copilot drafts call summaries, proposes CRM updates and prepares quote inputs, and it writes to the CRM only after a person confirms the change.

1. 01

   Scope and write the evals

   We pick one high-volume workflow, such as order-status tickets, returns or lead qualification, and define what a correct answer looks like. With your support lead we assemble an eval set from real conversations, including the ugly ones. That set becomes the definition of done.

2. 02

   Build and integrate

   We build the agent against your helpdesk or CRM (Zendesk, Intercom, Freshdesk, HubSpot, Salesforce) and your channels, including web chat, email and WhatsApp. Each tool call uses scoped credentials, and every write action is logged. Retrieval runs over your own knowledge base, with answers that cite their sources.

3. 03

   Evaluate, harden and shadow

   We run the evals on every change, test adversarial inputs and then run the agent in shadow mode on live tickets, drafting replies that your team reviews. We do not switch on customer-facing answers until the agent passes the threshold you approved.

4. 04

   Launch with handoff

   We roll out by channel or customer segment, with clear takeover rules: low confidence, billing disputes, angry customers, anything outside the scope. Your team sees the full transcript and the reason for each handoff.

5. 05

   Operate with AgentOps

   After launch we watch quality, cost per conversation and escalation rate, rerun the evals when a model or prompt changes, and review failures with you each month. You can run this yourself using the runbook, or keep us on a retainer.

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