Layer 03 / 04
The stack that turns data into revenue.
We build and run CRM, automation, media, and AI as a single operation. The marketing machine runs while you sleep.
Modern marketing is a technology discipline, but most companies have translated that fact incorrectly. The tool count grows. Integrations accumulate as patches on patches. Reports contradict each other. The team spends a significant share of its time not reading data, but collecting it. This is not a people problem. It is an architecture problem.
A company’s marketing intelligence is only as strong as the weakest link in its data chain. If customer behaviour is unmeasured or mismeasured, every question about which message should reach which segment, and which channel deserves the next budget allocation, becomes guesswork. Our approach is to design this chain as a whole: from the data collection point through the CRM, into automation, and into AI: one flow, measurable output.
Investing in data infrastructure does not mean adding tools. A properly built CDP, functioning server-side tracking, and a well-modelled identity resolution make every daily decision by the marketing team less dependent on estimation and more grounded in observation. When attribution becomes reliable, spend moves away from underperforming channels. When lifecycle automation responds to behaviour, a revenue layer forms that requires no human trigger. When AI workflows handle routine operational time, that time opens up for decisions.
Building this structure is not a one-time project. Tools change, channels evolve, AI capabilities grow every quarter. That is why we stay inside the operation: we prepare the integration when a new channel opens, cut the tool that has become redundant at audit, and add a new use case to the AI workflow library each month. The goal is not a stack that has grown beyond your control; it is an operation your team owns and that gets sharper every month.
If you’re ready to work at this level, a discovery call is the right starting point. In that first conversation we assess your stack, your goals, and the gaps between them. Not a proposal, just clarity.
The MarTech stack is out of control at most companies: an average of 18-25 tools, 4-6 vendor invoices, and data silos that never fully talk. The failure pattern repeats itself in a predictable loop: attribution is unreliable because the customer profile is fragmented; the profile is fragmented because integrations are patchwork; because they're patchwork, half the team builds manual reports instead of reading dashboards. We break this loop: audit the stack, embed AI inside the operation, and run it as a single daily flow. The five points below summarise the concrete inflection points we observe consistently across clients.
25-40% tool cost reduction
A properly built CDP and server-side tracking pays for itself within two years. Recovering lost attribution data (post-iOS 14.5 reporting arrived 30-50% incomplete), reducing media spend waste, and cutting tool licences compound into a measurable return. MarTech cost drops 25-40% over 12 months. The decision quality gained from reliable data is not captured in that number; it's on top of it.
60-75% manual email reduction
A behavior-based sequence built once doesn't restart for each new customer; it scales. A lifecycle email, onboarding series, or win-back campaign written with precision reaches hundreds of thousands at the same quality. In the first 90 days after automation goes live, our clients see the share of manually sent emails drop 60-75% while conversion rates hold or improve.
30-50% manual work reduction
We deploy LLMs as output-producing operation components, not conversation partners. Customer interview to CRM summary cuts a 4-hour task to 8 minutes. A content draft from a brief takes 10 minutes; the editor refines instead of starts from scratch. Sales sequence personalisation generates distinct copy for each prospect. Net effect: 30-50% of the team's operational hours are freed: fewer hours spent on assembly, more on decisions.
15-25% paid attribution correction
Server-side tracking plus UTM discipline plus unified identity in the CDP turns "which channel actually drove this?" from estimation into evidence. Incrementality testing measures each channel's true contribution to growth; media allocation gets rebuilt on that number, not last-click fiction. Typical outcome: the paid channel's claimed attribution share drops 15-25%; the same budget produces higher MER.
05
Data processing inventory, consent management, server-side hashing, and PII separation in the warehouse are architecture decisions, not compliance checkboxes. Only hashed values reach ad platforms; PII lives in a separate warehouse schema with role-based access. The marketing team discusses campaign performance, not legal exposure. Audit-ready from day one: DPIA and processing records prepared when needed.
in this layer
01
CDP setup, server-side tracking, identity resolution, BigQuery/Snowflake data warehouse. Fragmented data consolidated into a single customer profile; attribution built on evidence, not platform claims. Every flow designed to GDPR/KVKK rules from the start, not retrofitted.
02
Behavior-based email, SMS, push, and in-app sequences on HubSpot, Customer.io, or Klaviyo. Every touchpoint from lead capture to loyalty is measured. Event-driven triggers via n8n/Make run at 3 AM without anyone touching a keyboard.
03
We move LLMs from chat windows into the operation itself: interview transcript synthesis, lead scoring, content drafts, sales sequence personalisation, ticket triage. Every flow runs with human-in-the-loop approval, is logged, and can be reversed. Net time saving: 30-50% of repetitive operational hours.
04
HubSpot, Salesforce, or custom CRM setup; segmentation; lifecycle marketing.
05
Performance-focused media buying, programmatic, attribution analysis.
go deeper
deliverable
We map a sprawling 18-tool stack down to a consolidated 6 to 8.
We audit the existing MarTech stack against license, usage, data flow, and cost. The output is a concrete roadmap toward a 30 to 50 percent annual cost reduction and a single coherent data layer.
use case
n8n plus LLM automations for customer ops: real tasks, measurable savings.
We automate repetitive work (support triage, lead qualification, content moderation, reporting) using n8n and LLMs. The team always keeps control over what the system does.
deliverable
Event tracking, server-side GTM, CDP, warehouse, reverse-ETL. GDPR-ready, one source of truth.
We build the full path of customer data, from the moment it is captured to the moment it is used. Event schema, server-side collection, CDP integration, warehouse modelling, and reverse-ETL, with consent management built in.
how we work
Five phases, monthly partnership rhythm. The first 10 weeks are intensive: audit, architecture, integration, AI workflow library; ongoing optimisation follows. MarTech operations doesn't end: tools change, channels evolve, AI capabilities grow every quarter. Each phase delivers standalone value and prepares the next. No phase waits in limbo for another to finish.
01
We map current tools, invoices, features actually used, and data flow. Each tool's business value gets a number: active users, workflows it triggers, annual licence cost. Overlapping features become visible; what can be cut becomes obvious. The audit finding is rarely a surprise; teams arrive knowing "something is wrong", we express it in numbers.
02
We design the target stack and data flow: which system is the master record, which are downstream consumers, whether a CDP is necessary or a reverse ETL is the cleaner alternative, and how server-side tracking gets built. Decisions land in a written document and get reviewed with your teams. Architecture is always calibrated to the complexity your team can own, not the perfect system, the manageable and extensible one.
03
We build the HubSpot ↔ Customer.io ↔ Segment ↔ BigQuery line. Server-side GTM, Conversion API (Meta CAPI + Google Enhanced Conversions), identity resolution. Each integration gets validated with test data and passes QA before going to production. Once identity resolution is live, anonymous-to-known user matching begins and attribution corrects retroactively.
04
We build AI-assisted flows on n8n or Make: interview transcript to CRM summary, lead scoring model, content draft generation, sales sequence personalisation, support ticket triage. Every flow runs with human-in-the-loop approval; outputs are logged and the team sees exactly what it approved. Error states have fallback paths and notification by default.
05
A monthly recurring ritual: workflow performance, attribution quality, licence cost review, new use case proposals. beynart stays as partner, but the goal is to strengthen your team. Every workflow and integration has a runbook; 1-2 people from your team go through the training programme. After 6-12 months your team runs it independently; we shift to advisor mode.
frequently asked
by the numbers
10x
Data pipeline speed
Realtime
AI operations
99.9%
Automation uptime
8+
Integrated platforms
Let's find the layer that fits your need and map where the architecture begins.
We bring the systems and the products already built for it, ready to run, not just to pitch.
