AI Agents

AI Agents Built for
Real Marketing Work

Not chatbots. Not demo projects. Production-grade AI agents that use tools, make decisions, and integrate with your existing stack — built for 7-figure marketing operations.

Discuss Your Agent

Agents We Build

Research & Analysis Agents

Gather competitor data, market insights, and performance benchmarks — synthesized into actionable reports automatically.

Ad Copy Agents

Analyze your top performers, generate variants, and test against your brand voice. Continuously improving creative at scale.

Lead Qualification Agents

24/7 lead screening that asks the right questions, scores intent, and routes qualified leads to your team instantly.

Content Production Agents

Brief-to-publish pipelines. Your agent researches, outlines, drafts, and formats — ready for final review.

Client Communication Agents

Trained on your tone and policies. Handles FAQs, status updates, and routine client comms without your team.

Operations Agents

Monitor inboxes, route tasks, update project boards, and send daily summaries — your AI office manager.

How it runs

Process

01

Map the workflow

We document the current process, the decision points, and what a correct outcome looks like for each one.

02

Define tools and guardrails

Which systems the agent can read, which it can write to, and where a human must approve.

03

Build and evaluate

Agents built on Claude or GPT-4 with real tool integrations, tested against a set of real past cases before go-live.

04

Deploy and monitor

Production deployment with tracing on every run, so you can see exactly why an agent did what it did.

What you get

  • Agent specification: tools, permissions, and escalation rules
  • Production agent(s) integrated with your CRM, inbox, or ad accounts
  • Evaluation set built from your real historical cases
  • Run-level tracing and cost monitoring
  • Handoff documentation and prompt change log

Timeline

Typical engagement: 2–4 weeks

  1. Week 1

    Audit

    We map what exists, where it breaks, and what number has to move. You get a written scope before we build.

  2. Weeks 2–3

    Build

    Built against your real accounts and data, with a staging path so nothing touches production untested.

  3. Week 4

    Deploy & handoff

    Production cut-over, monitoring and alerts, a runbook, and a recorded walkthrough for your team.

What a build looks like

B2B SaaS with inbound demo requests landing in HubSpot

Illustrative example

Before

Sales reps spend 15 minutes researching each inbound lead before deciding whether to book a call, and half the leads never get a reply.

What we build

  • A research agent that pulls company size, stack, and recent news for every new HubSpot contact and writes a summary to the record.
  • A qualifier agent that scores the lead against the ICP and either books a call via the rep’s calendar link or drops it into a nurture sequence.
  • A daily digest agent that posts the top ten scored leads to the sales Slack channel with one-line reasons.

After

Every inbound lead gets a response within minutes, reps only spend time on leads that fit, and the reasoning behind every score is visible in the CRM.

Questions we get on the call

Is this just a chatbot?

No. These agents use tools: they read your CRM, call APIs, write records, and hand off to humans. A chat interface is optional.

Which models do you use?

Claude and GPT-4 in production, chosen per task. We pick based on your evaluation set, not on preference.

How do you keep an agent from doing something wrong?

Scoped permissions, approval gates on irreversible actions, and an evaluation set built from your real past cases before anything goes live.

What does it cost to run?

Model costs are usually a small fraction of the labour they replace. We instrument cost per run so you see it from day one.

Discuss your agent