AI Agents That Handle Your Routine Work

AI agents that handle inbound support across email, chat, and web forms. The agent understands the request, looks up the customer's context in your CRM and support tool, drafts a response, and either sends it or routes to a human depending on the case. Trained on your actual support history, your product documentation, and your escalation rules. Resolution rates typically run between fifty and seventy-five per cent of inbound volume without human involvement, with full audit trail on every interaction.

Agents that engage inbound leads — on your website, in response to form submissions, or as a follow-up to ads — and run them through your actual qualification process. Budget, timeline, fit, decision-maker, urgency. Qualified leads land in your CRM with the conversation attached and the right rep notified. Unqualified leads are nurtured or politely declined. Response time from form submission to first agent message is typically under thirty seconds.

Agents that read documents — supplier invoices, contracts, clinical letters, claims forms, compliance submissions — extract the structured data, validate it against your rules, and push it into the right system. Replaces hours of manual data entry per day with seconds of agent processing. Built for environments where accuracy matters and an audit trail is mandatory.

Copilots that work alongside your team, not instead of them. Pull customer history into one view during a call. Draft proposals from a structured brief. Surface the right policy clause during a compliance review. Summarise long email threads. Generate first-draft responses for a human to refine. Designed to make experienced staff faster, not to replace them.

Agents that execute multi-step processes across your tools — receive a trigger, look up information in System A, make a decision, take action in System B, notify a human in System C, log the outcome. The difference between this and traditional automation is that agents handle the cases that don't fit a fixed rule. They use language understanding and reasoning to deal with exceptions, ambiguous inputs, and edge cases — and they hand off to a human when they should.

AI voice agents for inbound and outbound calls, where the use case justifies it. Appointment confirmations, reminder calls, simple intake, after-hours triage. Built on production voice infrastructure (Twilio, Vapi, Retell), integrated with your CRM and scheduling systems. Voice is a higher bar than chat — we're selective about where it actually works.

A multi-clinic allied health network receiving after-hours enquiries via web form, phone callback, and email — none of which could be handled until the next morning. An AI agent now responds within thirty seconds, asks the right intake questions (presenting condition, urgency, referral source, preferred clinic, preferred clinician), books appointments directly into the practice management system where suitable slots exist, and escalates clinically urgent cases to an on-call clinician with the full intake attached. Outcome: same-day response on every enquiry, intake-to-first-appointment time reduced from days to hours, and clinical staff arriving in the morning to organised, ready-to-confirm appointments rather than a queue of voicemails.

A B2B services firm receiving high inbound volume from paid ads and content, with sales reps spending the majority of their week on unqualified calls. An AI agent now engages every inbound form submission within seconds, runs the firm's qualification framework as a natural conversation, gathers the budget, authority, timeline, and use-case signals the reps actually need, and books a meeting directly into the rep's calendar when the lead clears the bar. Unqualified leads enter a tailored nurture sequence. Outcome: rep calendars filled with qualified meetings rather than cold introductions, response time from form to first meaningful interaction reduced from hours to under a minute.

An operations team receiving several hundred supplier invoices per week across PDF email attachments, EDI feeds, and a supplier portal. Previously, invoices were keyed by hand into the ERP, with regular errors and a one-to-three-day lag. An AI agent now reads each invoice, extracts the line items, matches them against the relevant purchase order, flags exceptions for human review, and pushes clean transactions into the ERP. Outcome: ninety per cent of invoices straight-through processed within minutes of receipt, finance team time on AP reduced by more than thirty hours per week, and exceptions handled the same day rather than at month-end.

A regulated services business with a small compliance team handling a heavy load of policy queries, document reviews, and regulator submissions. An internal compliance copilot now answers staff questions against the current policy library, surfaces the right clauses during document reviews, drafts first-draft regulator responses for the compliance team to refine, and tracks every interaction for audit. Outcome: response time on internal compliance queries down from hours to seconds, compliance team time on routine queries dropped sharply, and audit trail produced as a side-effect of normal operations.

Specific case studies are available on request under NDA where the client engagement allows.

Every agent we deliver runs with explicit boundaries on what it does autonomously and what it escalates to a human. The boundaries are decided by you, written down, and configurable. New agents start with tighter boundaries — more escalation, more human review — and the boundaries widen as the agent's track record on your data justifies it. We don't ship agents that make important decisions without human oversight on day one.

The first version of an agent handles a tightly defined workflow with a clear definition of success. We measure its actual performance — resolution rate, accuracy, escalation rate, customer satisfaction, business outcome — against the baseline of how the work was being done before. Expansion to adjacent workflows only happens once the first one has proven out. This sounds slow. It's the reason our agents are still running in production a year later.

We don't drop a generic chatbot onto your website. Every agent is built around your actual processes, your historical interaction data, your product or service detail, and your operational rules. The work the agent does should be indistinguishable in quality from what a well-trained staff member would do — because the agent has been trained on what your well-trained staff actually do.

Every agent has a written evaluation set — real examples of inputs the agent will see, with the correct outputs documented. The agent has to perform against that evaluation set before it goes live, and the evaluation set runs continuously after launch to catch regressions. This is normal practice in serious AI work, and missing in most of the AI products on the market.

Every agent interaction is logged, searchable, and reviewable. You can read every conversation, every decision, every tool call. Compliance and operations teams have full visibility. If the regulator asks, you can answer.

We build with the right model for the job — Anthropic's Claude family, OpenAI's GPT models, and open-source models where data residency or cost requires it. The choice is based on capability, cost, latency, and data handling requirements, not on a vendor relationship. Models are swappable as the landscape changes, which it does often.