Marketing Automation & AI Agents
Automation earns its keep when it removes a task someone genuinely repeats. Most of it fails because nobody checked that first.
Start from the repeated task, not the tool
Most automation projects begin with a platform decision and work backwards toward a justification. That is why so many end as half-configured workflows nobody trusts. We start at the other end: what does someone on your team do every week that is mechanical, rule-based and annoying? That list — usually written in twenty minutes on a call — is the roadmap. Anything not on it is a tool looking for a problem.
Key takeaways
- We scope from a list of genuinely repeated tasks, not from a platform's feature set.
- Deterministic workflows beat AI agents for anything with clear rules; agents earn their place on judgement and unstructured input.
- Everything ships with a human checkpoint by default, and the checkpoint is removed only once the failure modes are understood.
- Automation built on messy data multiplies the mess — data plumbing usually comes first.
When to use a rule, and when to use a model
There is a lot of pressure right now to put a language model in the middle of everything. It is often the wrong call. If a task has clear rules — route this lead to that owner, notify this channel when a deal changes stage, generate this report on the first of the month — a deterministic workflow is cheaper, faster, and does not surprise you. Reserve models for what rules genuinely cannot do: reading unstructured input, summarising, classifying ambiguous cases, drafting a first version of something a human will edit.
The mistake we see most is an agent given autonomy over an irreversible action. Drafting an email is a good use of a model. Sending it without anyone looking is not, until you have watched it behave for a while and understand how it fails.
What we build
Marketing and CRM automation. Lead routing, lifecycle stages, enrichment, deduplication, handoff rules between marketing and sales, and the alerting that stops things sitting in a queue for a week.
Reporting pipelines. Pulling from ad platforms, Search Console, analytics, the CRM and rank or visibility tools into one warehouse so a dashboard reflects one version of reality. This is the least exciting item on the list and the one that most often unblocks everything else.
AI agents for defined jobs. Narrow, well-scoped tasks with a checkpoint: triaging inbound enquiries, drafting first-pass responses from a knowledge base, classifying and tagging content, monitoring a source set and flagging what changed.
Workflow orchestration. Wiring the above together so a change in one system reliably produces the right effect in the next, with retries and failure alerts rather than silent breakage.
Data plumbing usually comes first
Automation sits on top of data, and inherits its quality. Duplicate contact records, inconsistent stage definitions, three competing ideas of what a "lead" is — automate on top of that and you get the same mess, faster and at scale. We would rather spend the first phase cleaning and consolidating than deliver something impressive that quietly corrupts your CRM.
This is also where the honest scoping conversation happens. Sometimes the answer after a discovery pass is that you do not need an agent, you need two fields renamed and one integration fixed. We will say that.
Handover, not dependency
Automation is only worth building if it survives us. Everything ships documented, in your accounts, with the logic explained in terms your team can follow. Where a workflow depends on a model, we document the prompt, the guardrails and the known failure modes, because those are the parts that need adjusting later. We are happy to stay on for maintenance and iteration, but the test of a good build is that you could take it over without us.
How engagements run
Usually a short discovery pass to produce the repeated-task list and audit the data, then build in priority order, shipping one working thing at a time rather than disappearing for a quarter. Pricing is scoped per project for build work, or folded into a monthly retainer where automation runs alongside an SEO or paid media program and needs continuous iteration.
Frequently asked questions
Usually normal automation. If a task has clear rules, a deterministic workflow is cheaper, faster and more predictable than a model. Agents earn their place on work that genuinely needs judgement or reads unstructured input — triage, summarisation, classifying ambiguous cases, drafting a first version someone will edit.
That is the first question we ask before building anything. Workflows ship with failure alerts and retries, and anything touching an irreversible action keeps a human checkpoint until its failure modes are understood well enough to remove it safely.
No. Everything is built in your accounts and documented in terms your team can follow, including prompts, guardrails and known failure modes where a model is involved. We are happy to stay on for iteration, but the test of a good build is that you could take it over without us.
Often that is the right order. Automation inherits the quality of the data underneath it, so duplicate records and inconsistent definitions just produce the same mess faster. A discovery pass frequently concludes that the useful first step is data consolidation rather than a new workflow.
Build work is scoped per project after a discovery pass. Where automation runs alongside an SEO or paid media program and needs continuous iteration, it folds into the monthly retainer instead.
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