Custom AI agents that know your business and act inside it

A custom AI agent is a software system that uses a large language model to complete a defined job for your business — answering questions, qualifying leads, retrieving information or triggering actions — using your own data, your own rules and your own tools. Unlike a generic chatbot, it is grounded in your content, connected to systems like your CRM, and built with explicit limits on what it can say and do.

A general assistant knows nothing about your pricing, inventory, policies or pipeline, and cannot act inside your systems. An off-the-shelf website chatbot usually answers from a small FAQ file and hands everything else to a form. A custom AI agent is grounded in your actual documentation and data, can read and write to the systems you already run, follows rules you define, and escalates to a named human when it should not answer.

A single well-scoped agent typically moves from discovery to a supervised pilot in four to six weeks, with the first two weeks spent on scoping, data preparation and guardrail design rather than building. Complexity drivers are the number of systems it must integrate with, the state of your documentation and the level of compliance review required.

Cost has two parts: a one-off build covering discovery, data preparation, integration, guardrails and testing, then a monthly figure covering model usage, hosting, monitoring and improvement. The build scales with the number of integrations and the depth of review needed; running costs scale with conversation volume. We scope both after discovery, because quoting an agent before seeing your data and systems is guesswork.

Any language model can be wrong, which is why grounding and guardrails matter more than the model choice. Our agents answer only from approved sources, cite what they used, refuse questions outside their scope, and hand off to a human on pricing exceptions, complaints, regulated topics and anything they cannot verify. Every conversation is logged and reviewable, so errors get caught and corrected in the source material.

Yes, and that is usually where the value sits. Agents can read and write records in CRMs, look up orders or availability, create tickets, book meetings, enrich leads and trigger workflows in your automation platform. What is possible depends on the APIs your systems expose and your data quality — which is what the technical discovery establishes before any build work starts.

You own the agent, its configuration, its knowledge base and its conversation data. We deploy inside your infrastructure or accounts wherever possible, restrict the agent to the minimum data it needs, and document what is sent to which model provider. Nothing is used to train public models, and access controls mirror the permissions already in your systems.

No. Agents absorb repetitive, high-volume, low-risk work — the same twenty questions, the first-pass qualification, the record lookup — so your team spends more time on the conversations that need judgement. Every agent we build has defined escalation points where a human takes over, and we recommend against automating anything where being wrong is expensive.

Against a metric agreed before the build — containment rate, qualified leads created, first response time, support tickets deflected, hours returned to the team, or conversion rate on assisted sessions. We baseline it first, run the agent supervised, then compare. If an agent cannot be tied to a metric that matters, we advise not building it.

Live is the start of the useful data. We review real conversations weekly at first, fix gaps in the source material, tighten prompts and guardrails where the agent drifted, extend its tools as trust grows, and report performance against the agreed metric. You can run this yourself with the documentation we hand over, or keep us on a monthly improvement cycle.