Industries Marketing & MarTech
Modernizing the newmarketing stacks with AI.
AI Engineering and software development for marketing companies.
Platforms that rely on human behavior, analyzing purchase patterns and withold large amounts of data for future references. We help marketing teams make better decisions, place better ads, content and boost their outreach and deliverability efforts.
EmailDeliverability.comDeliverability, Built Into The Marketer's Editor
The deliverability software of EmailMarketing.com, Troy Ericson's group. We built the product: a Chrome extension that optimizes an email for inbox placement inside the marketer's own ESP editor, and the admin system behind it.
- 40+
- ESPs supported
- 640+
- Businesses on the software
- $250M+
- Generated for their clients
Why marketing teamstrust us with delivery.
An off-brand claim shipped at volume, or a lift number nobody can reproduce, costs real budget and real trust. This is why we pay special attention to the most sensitive parts of the system.
The identity layer moved
If your audiences keep shrinking while third-party signal thins, the targeting did not get worse, the ground did. Reach belongs to whoever can resolve consented first-party data into one person, and that resolution has to be a system you own.
Generation is cheap, brand is not
Your team can generate five hundred variants in an afternoon, then spend three weeks getting them through tone, claim, and legal review in thirty locales. The review loop is the thing that has to be engineered, not the model.
Attribution is a model, not a report
Last click flatters the cheapest channel and every channel claims the same conversion. A number the budget review will trust has to be reproducible from raw events and tested for incrementality.
Consent is plumbing now
When a deletion request lands, it has to reach every system holding a copy of that customer, and suppression that fails to suppress comes back as a fine. Consent state has to travel with the data rather than live in a checklist.
The money is spent before the numbers land
Every ad platform reports its own version of the win, and the outcome data sits in a CRM or a warehouse on a schedule nobody wrote down. Spend and outcomes have to be reconciled in one ledger, fast enough to still change the buy.
What marketing demandsfrom engineering and AI.
01Audiences built on prediction
A hand-written segment is a guess about who might convert, and budget follows the guess. Propensity, expected value, and churn risk scored on first-party behaviour turn the segment into a prediction, and show which campaigns were only finding people who would have bought anyway.
02Content operations that scale approval
Generation scales in an afternoon and approval does not, which is where content programmes actually stall. Meeting it takes automated brand and claim checks, localisation backed by translation memory, and human sign-off placed only where the risk genuinely sits.
03Lifecycle decisions made in real time
A journey drawn as static branches matches the customer behaviour of the quarter it was designed in, then quietly ages. Decisioning that picks the next message, channel, and moment under frequency and fatigue constraints keeps working as behaviour shifts.
04A data foundation that carries the models
Every model above the warehouse is capped by the quality of what is in it, which is why measurement projects so often turn out to be data projects under another name. A clean event schema, identity resolution that survives logged-out sessions, and activation straight from the warehouse are the real deliverables.
Real-life stories of triumph.
Get In Touch
As soon as I hired Nortik for my first big development project, I couldn’t stop hiring them for more. The team is incredibly professional. They’ve built some amazing things for my business!
Nortik’s Impact
8 dedicated engineers across 3 projects, scaling the infrastructure that sends 40M emails per month.
Discover more of our insights.
Recent Posts
The martech stack has 15,000 tools. AI earns a slot by deleting handoffs, not adding features
Marketing teams don't need another AI-powered tool. The use cases that stick, from campaign assembly to audience queries to creative testing, remove handoffs between people, not clicks between screens.
What an AI agent actually costs to run in production
Token bills are the visible line item. The real costs hide in the architecture: retries, tool calls, human review loops. A breakdown from live systems.
Why AI prototypes fail in production (and how to ship one that doesn't)
Most AI prototypes stall somewhere between the demo and the deploy. The problem is rarely the model. It's the evals, guardrails, and infrastructure nobody scoped.
Frequently asked questions
Everything teams usually want to know before building on their marketing data. Not here? Ask us directly.
We rebuild it on your own data: conversions modelled in the warehouse rather than read off each platform's self-report, incrementality tested with holdouts or geo experiments, and a media mix model where the channel mix justifies one.
The test we hold it to is whether a number survives someone senior asking how it was produced. If it can't be reproduced from raw events, it isn't measurement.
We built the platform WeGenerate's pay-per-call operation runs on, where Ringba call analytics land every fifteen minutes and get reconciled against ad spend in one profit ledger.
Consent state travels with the event rather than being checked once at the tag, so what you may collect, model, and activate is enforced by the pipeline instead of by convention.
Deletion and suppression are designed to propagate to every downstream copy: warehouse, CDP, ESP, and ad platform. A suppression list that arrives a day late is the same as not having one.
Only with the loop around it. Brand voice and claim rules encoded as automatic checks, retrieval from your approved source material, evaluation sets that catch regressions when a model changes, and human approval concentrated where the risk actually is.
Where a claim is regulated or a locale is legally sensitive, generation drafts and a person signs. We build that boundary in rather than pretending it isn't there.
Lumoo is the clearest example: an AI workspace where a brand's own catalog becomes on-model imagery, built for fashion and retail names including Gant and Gina Tricot.
We work with it. Segment, HubSpot, Salesforce, Braze, Klaviyo, GA4, the ad platforms, and whatever warehouse you're on. The value is usually in the seams between those systems, not in another migration.
Where a tool genuinely can't do the job, we say so plainly, but replacing a stack is a decision with a business case behind it, not a default.
That's a large part of what we do: AI features inside someone else's platform, built multi-tenant from the start, with per-tenant isolation, cost controls, and latency budgets your customers will notice if you get them wrong.
Model spend behaves like COGS in a product like yours, so we design around unit economics: caching, routing, and model choice per workload, decided up front instead of discovered at scale.
You do. Code, trained models, pipelines, and documentation are yours, along with the infrastructure access to run them without us.
We hand over a codebase your own team can pick up. Nothing we build is designed to keep you dependent on us.