AI agents that work while you sleep

It depends on the scope of the task and the systems involved. We begin with a short discovery phase to agree the task, the systems involved and how success will be measured, then design, build and test. Simpler agents with clean data and well-documented APIs sit at the shorter end; agents that touch several systems or need approval flows take longer.

Yes. Agents connect through APIs to the tools you already use, including CRMs such as HubSpot and Salesforce, helpdesks such as Zendesk and Intercom, shared inboxes, e-commerce stores such as Shopify and your own databases. Where a system has no API, we can usually work through exports, webhooks or an automation platform instead.

It hands the conversation to a person. The agent recognises when a question is outside its knowledge or policy, tells the customer it is bringing in a colleague and passes your team a short summary with the relevant details. Nobody has to repeat themselves, and each handoff is logged so we can teach the agent to handle similar cases later.

We follow SOC2-ready and GDPR-compliant engineering practices. Data stays in your environment wherever possible, personal information is masked before it reaches any model, and access is limited to what each agent needs. Every action is logged for audit. Where privacy requirements are strict, we can run open-source models inside your own cloud.

Cost depends on scope: how many systems the agent connects to, how many conversation types it handles, the languages required and the expected volume, which drives model usage costs. We provide a fixed proposal after discovery, so you know the build cost and estimated running costs before committing. Our guide on AI chatbot costs explains the main factors in more detail.

There is no single best model. OpenAI and Anthropic Claude models are strong at reasoning and conversation, Google Gemini handles long documents and multimodal input well, and open-source models can run privately at lower cost for high volumes. We are model-agnostic, test the options on your real tasks and often route different requests to different models.

A traditional chatbot follows scripts and answers questions. An AI agent understands free-form requests, decides what to do and takes action in your systems, such as issuing a refund, booking a meeting or updating a record. Most of the custom AI agents we build combine both: natural conversation on the surface and reliable, logged actions underneath.