Marketing automation built as a system, not a stack of campaigns
Lead scoring that your sales team believes, lifecycle journeys that respond to behaviour, and a CRM sync that keeps both sides honest. Engineered by developers who integrate Salesforce for a living.
Form fills go into a list nobody works. Sales says the leads are poor; marketing says sales never follows up. Nurture emails go to people who bought last month. These are data problems with a marketing label, and they are solved with engineering.
B2B with a sales team
Scoring decides who gets a call this week. Journeys warm the rest. The CRM shows one truth for both teams. Pairs with our sales copilot.
SaaS and subscription
Trial, activation, expansion and churn-risk journeys driven by product usage, not just email opens.
Events and memberships
Registration, reminders, on-site and post-event follow-up tied to attendance data. The flows we run for event clients.
What goes wrong when it is done badly
The same nurture sequence to every contact regardless of stage. A "hot lead" alert for anyone who opened two emails. Duplicate records, each on its own journey, so one person gets three versions of the same message. Consent recorded in one system and ignored by another. Every one of these is a data problem wearing a marketing label, which is why we build the foundation before the first journey.
Architecture
Signals in, decisions in the middle, CRM on both ends
Every signal lands in one scoring layer that combines explainable rules with a model trained on your own won and lost deals. The score routes each contact into a journey; the CRM records what happened, and outcomes flow back to improve the scoring.
What we build
Four components, in this order
Data foundation
One contact and one account record, deduplicated, with consent and lifecycle stage as first-class fields. Web and product events tied to the contact. This is half the project and the half most vendors skip.
Lead scoring
Rules first, agreed with sales so they trust the number. Then a predictive model trained on closed deals, run side by side until it earns the right to decide.
Journeys
Entry and exit criteria, waits, branches, suppression. Built in your platform with naming conventions and documentation so the next person can read them.
CRM sync
Field mapping, conflict rules, error handling and monitoring. Our Salesforce integration team builds this with Apex, Flow and APIs rather than a connector nobody can debug.
Scoring
Rules-based
Model-based
Set-up time
Days
Weeks, needs history
Explainable to sales
Fully
With feature importance
Accuracy
Depends on the author
Improves with outcomes
Data needed
Little
A few hundred closed deals
Maintenance
Manual review
Retrain on schedule
Our default
Start with rules, add the model when the data supports it, keep both visible
Where AI helps
Drafting email variants from an approved brief, summarising a contact's history for the sales alert, classifying replies (interested, not now, wrong person) and suggesting send times. Every draft is reviewed; every classification can be overridden. See email marketing AI.
Process
Foundation first, then one journey, then the rest
Map
Lifecycle stages, hand-off rules, data sources, consent model. Agreed by marketing and sales in one room. Week 1.
Plumb
Deduplicate, map fields, connect events, build the sync with monitoring. Weeks 2–4.
Score
Rules agreed and live; model trained if history allows; both visible on the contact record. Weeks 3–5.
First journey
The one with the clearest value, usually sales alert plus nurture. Built, tested with real records, launched. Weeks 5–8.
Measure and extend
Conversion by score band, time to first contact, journey exits. Next journey every two to three weeks.
Usually not. Most teams already own more platform than they use. We start from the tools you have, Salesforce Marketing Cloud or otherwise, and engineer the data flows, scoring and journeys around them. We recommend a change only when the current platform cannot do what the plan needs.
What is the difference between rules-based and model-based lead scoring?
Rules-based scoring adds points for things you decide matter: a pricing page visit, a job title, a company size. Model-based scoring learns from past deals which signals actually predicted a close. Rules are transparent and quick to set up; models are more accurate once you have a few hundred outcomes to learn from. We often run both and compare.
How does AI fit into automation without annoying customers?
AI drafts; people approve. We use models to draft email variants, suggest send times, summarise a contact's history for sales and classify replies. Every outbound message is from a reviewed template or has human sign-off. Nothing fully generated reaches a customer without a check.
How long does a marketing automation project take?
A first lifecycle journey with scoring and CRM sync takes four to eight weeks. The data plumbing is most of it: field mapping, deduplication, consent flags. Subsequent journeys take days once the foundation exists.
How do you handle consent and privacy?
Consent is a field that travels with the contact, is checked by every journey step and is respected by the sync. We log the lawful basis for each message type and set retention rules. Details are on our AI data privacy page.
Ready to make AI work in production?
Tell us about your systems, your data and your constraints. We reply within one business day with a straight answer and a suggested next step.