AI Agents
AI agents that do the work, not just talk about it.
Custom agents wired into the systems your business already runs on — qualifying leads, deflecting support tickets, researching accounts, and updating records. Scoped against a real job, measured against a real eval set, and monitored once it is live.
Lead qualification agent
- 1TriggerNew lead lands in HubSpot
- 2RetrievePulls pricing docs + past deals
- 3ReasonScores fit against ICP criteria
- 4ActDrafts reply, books call, updates CRM
- 5EscalateHands off to a human when unsure
Definition
An AI agent is a system that uses a language model to decide which actions to take to complete a task, calling tools and APIs along the way rather than only returning text. Unlike a chatbot, it can read from and write to real systems — a CRM, a calendar, a database — which is what makes it useful and also what makes evaluation, guardrails, and monitoring non-negotiable.
Most AI agent projects fail for an unglamorous reason: nobody defined what “working” meant before building. We start every engagement by writing down the job, the inputs, the acceptable outputs, and the cases where the agent must stop and ask a human. That document becomes the eval suite, and the eval suite is what lets us improve the agent for years without breaking it.
Agents we build most often
- Lead qualification agents that read an inbound form, enrich it, score it against your ICP, draft the first reply, and book the call.
- Support deflection agents grounded in your help centre and past tickets, resolving the repetitive 60% and escalating the rest with full context attached.
- Internal knowledge agentsthat answer “where is the policy on X” over your Notion, Drive, and Slack history without exposing documents people shouldn't see.
- Research and monitoring agents that track competitors, summarise accounts before a call, or watch a data source and flag what changed.
- Ops agents that reconcile records across tools, chase missing information, and keep two systems in agreement without a person copying fields.
How we build them
Week one is scoping and evals — the task definition, the golden test cases, and the escalation rules. Week two is retrieval: getting your data into a form the agent can ground answers in. Weeks three and four are tools and integration, connecting the agent to the systems it needs to act on. Then we run it shadow-mode against real traffic before it touches a customer, so you see how it would have performed before it performs.
The running cost is part of the quote
Model tokens, vector storage, monitoring, and prompt maintenance are real ongoing costs, and pretending otherwise is how AI projects turn into unpleasant surprises. We give you a monthly running estimate alongside the build price, show you the levers that move it, and design the cheap steps to run on cheap models. Read our full cost breakdown if you want the numbers before you talk to us.
Keep reading
Explore more
Frequently asked
Quick answers before you reach out
A chatbot answers. An agent acts. A chatbot retrieves an answer and returns text; an agent has access to tools — your CRM, your calendar, your database, your internal APIs — and can decide which ones to use, in what order, to finish a task. That difference is why agents need evaluation, guardrails, and monitoring in a way chatbots never did.
A single-purpose agent — lead qualification, support deflection, internal document Q&A — runs $6,000 to $15,000 to build, plus roughly $400 to $1,200 per month to run. A multi-agent system covering a full process runs $15,000 to $40,000 to build, plus $1,200 to $3,500 per month. The monthly figure covers model usage, vector storage, monitoring, and ongoing prompt and eval maintenance, and we quote it honestly up front rather than surprising you in month two.
Three to five weeks for a single agent, six to ten weeks for a multi-agent system. Most of the first week is scoping and building the evaluation set, not writing prompts — because an agent you cannot measure is an agent you cannot safely improve.
Three layers. Retrieval grounds answers in your own documents rather than model memory. Guardrails constrain what the agent is allowed to say and do, with hard stops on anything involving money, contracts, or personal data. And an eval suite runs against known cases on every change, so a prompt tweak that quietly breaks something gets caught before it reaches a customer. Anything the agent is not confident about goes to a human.
Whichever fits the task and the budget — usually Claude or GPT-class models for reasoning-heavy work, and smaller, cheaper models for classification and routing steps. We build model-agnostic so you are not locked to one provider's pricing or availability, and we routinely cut running costs by moving the easy steps to a cheaper model.
You do — the code, the prompts, the eval suite, and the infrastructure, all in your accounts and your repository. We can stay on to maintain and improve it, but you are never in a position where switching away means starting over.
Ready to plan
the next launch?
Tell us what you're building. We reply within 24 hours with a clear next step — no sales calls required.