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Layer 03 / 04

MarTech & AI Operations

The stack that turns data into revenue.

MarTech & AI Operations — service area visual

MarTech & AI Operations

03 / 04

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.

01

Why this layer matters

A poorly architected layer slows the brand. A properly built one compounds like interest.

  • 01

    Speed

    Modular infrastructure compresses time-to-market by 3-5x.

  • 02

    Scale

    We build foundations that carry today's operation into tomorrow's traffic.

  • 03

    Visibility

    Ready to make every decision at the right time, signaled by data.

02

Services in this layer

  • 01 / 05

    Data Infrastructure & CDP

    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.

    Segment RudderStack BigQuery Snowflake

  • 02 / 05

    Automation & Lifecycle Flows

    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.

    HubSpot Customer.io Klaviyo n8n Make

  • 03 / 05

    AI Operations Layer

    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.

    OpenAI LangChain n8n

  • 04 / 05

    CRM Implementation & Management

    HubSpot, Salesforce, or custom CRM setup; segmentation; lifecycle marketing.

    HubSpot Salesforce

  • 05 / 05

    Media Planning & Buying

    Performance-focused media buying, programmatic, attribution analysis.

    Meta Ads Google Ads
Sinusoidal signal-flow composition — data streams representing the MarTech and AI operations layer

OPS

The stack shrinks, the speed compounds.

03

How we work

We hold to the same discipline at every layer: listen first, measure, build, then improve.

  1. 01

    Discovery & alignment

    We clarify your brand goals, current state, and success metrics. We take the time to make sure we don't answer the wrong question well.

  2. 02

    Strategy & design

    We turn data-driven insight into an executable roadmap. AI accelerates the hypothesis at every decision point; we steer the direction together with your team.

  3. 03

    Build & integration

    We build the solutions and connect them to your existing systems. We ship without breaking your operations.

  4. 04

    Measure & iterate

    Launch isn't the finish line. We monitor performance, test hypotheses, and compound the wins.

04

Go deeper into this layer

deliverable

MarTech Stack Audit

We map a sprawling 18-tool stack down to a consolidated 6 to 8.

MarTech & AI Operations — section visual

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.

Why this matters

Most marketing organisations are spread across 18 to 25 SaaS tools. Three of them do nearly the same job, two are used by a single person, and one quietly auto-renewed last quarter. That sprawl is the invisible part of the monthly bill — and the main reason “customer data is never complete anywhere” keeps showing up. A MarTech stack audit puts the whole picture into one document, so the decision can be made at the same table as budget and data strategy.

The cost isn’t only the subscription. Overlapping tools split your data, tie teams to different versions of the truth, and create a tangle that has to be taught to every new hire. In a typical mid-sized stack, it’s no exaggeration to say a meaningful share of budget goes to overlapping or idle tools. A stack that grows unchecked eventually slows marketing down instead of speeding it up.

How to know you need an audit

Some symptoms quietly signal that the stack has gotten out of hand. If you recognize a few of these, the audit’s return is very likely higher than its cost.

  • You can’t produce the full list of marketing tools from a single document.
  • No one can answer “who uses this tool?” clearly.
  • The annual renewal invoices surprise you when they arrive.
  • You see the same data with different numbers in two different tools.
  • Teaching the tools to a new team member takes days.

These symptoms aren’t cause for panic on their own; but together, the stack has probably grown from accumulation rather than need. An audit is the fastest, lowest-risk way to bring that accumulation back to order.

What we deliver

We inventory all your marketing tools and surface overlaps and costs. The output isn’t a critique list — it’s a decidable, prioritized consolidation plan.

  • Full stack inventory. What each tool does, who uses it, how many seats are paid for, and when contracts expire — all in one table.
  • Data flow map. Which tool reads what data from where and writes back to where. Duplicate or contradicting flows are flagged. Most hidden cost hides in the manual work that missing integration creates.
  • Consolidation recommendation. Keep, migrate, retire, or replace decisions. Each one comes with annual savings estimates and a transition risk profile.
  • A 12-month integration plan. A sequenced migration calendar (CRM, automation, analytics, ads), with an owning team and success criteria for each step.

Our approach

We don’t judge any tool on sentiment. We run every tool through a decision framework — because “rarely used” alone isn’t grounds to drop it. We score each tool on four axes:

  1. Usage. Is the tool actually used, how often, and by how many people? Idle licenses are the first signal.
  2. Value. Does the tool do a unique job, or does it repeat work another tool could also do?
  3. Cost. What’s the total annual cost of ownership — not just the subscription, but maintenance and integration effort?
  4. Switching risk. How hard is it to drop or replace this tool; is data portable, does the team need retraining?

These scores place each tool into one of four decisions: keep (high value, reasonable cost), merge (overlapping, movable into a better tool), stop renewing (low usage, low value), and review (high value but high cost or high switching risk). We start with the quick wins — the low-risk, clear-saving decisions. Savings aren’t the only goal: sometimes the right decision isn’t dropping a tool but finally turning on a feature you’ve been paying for, or building the missing integration between two tools. The framework’s purpose isn’t to shrink the stack but to make every lira you spend earn its keep.

Process

  • Week 1: Collect every contract and invoice; interview tool owners (8 to 15 people). Deliverable: a full stack inventory and cost table.
  • Week 2: Map data flows; document existing integrations and ETL jobs. Deliverable: an overlap map and a per-tool scorecard.
  • Week 3: Consolidation workshop with the CMO, CFO, and IT lead; align on decisions. Deliverable: prioritized consolidation decisions.
  • Week 4: Twelve-month plan, contract renewal calendar, scope document for the first migration. Deliverable: a phased roadmap and an executive summary.

What happens after the audit

The audit doesn’t end with a report; the real value shows up in acting on the decisions. The roadmap puts the most concrete, lowest-risk steps first: you shut down idle licenses, then merge overlapping subscriptions, and handle the high-switching-risk decisions gradually, last. Replacing every tool at once is neither necessary nor wise. Most teams collect the quick wins in the first quarter, then work through the remaining decisions in order, aligned to the contract renewal calendar.

Sample outcome

For a multi-brand DTC group, we cut the stack from 22 tools to 9. Annual SaaS cost dropped from $480k to $260k. We merged the overlapping email and automation tools, shut down the idle licenses, and built the missing integration between the CRM and the warehouse, removing a manual data-transfer step. More importantly, once customer data flowed into a single warehouse, attribution accuracy moved from 58% to 87%, and the marketing budget was reallocated accordingly. A modest gain, but one that paid back immediately.

Who it’s for

This work fits best for teams whose stack grew without a plan over the years and now runs past fifteen tools. Where several teams have bought tools separately, renewal invoices come as a surprise, and no one holds the full list, an audit pays back fast. By contrast, a comprehensive audit can be overkill for a very small team with only a handful of tools that already tracks its spend clearly; in that case a simple inventory table is enough. The sector isn’t the deciding factor: tool accumulation happens the same way in retail, manufacturing, and services; only the kinds of tools in the stack change.

FAQ

Will the audit disrupt our current work? No. The inventory and analysis run largely in the background; we ask your team only for short interviews and invoice access. No tool is shut off without your decision.

Will you end up trying to sell us specific tools? No. The audit is vendor-independent. The recommendation always rests on your needs and your existing stack; dropping a tool and keeping a tool are both valid outcomes.

Will we actually see the savings? The roadmap includes estimated annual savings for each decision. The most concrete savings usually come from shutting down idle licenses and merging overlapping subscriptions; these show up within the first quarter.

How often should we repeat the audit? Stacks tend to grow back. A short review once a year is enough for most teams to catch new overlaps early.

To audit your stack and turn it into a concrete consolidation plan inside the MarTech and AI operations pillar, talk to us.

use-case

AI Workflow Library

n8n plus LLM automations for customer ops — real tasks, measurable savings.

MarTech & AI Operations — section visual

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.

Why this matters

“Let’s automate this with AI” tends to stay at the slide level. Real automation is more than calling a model. If no one has decided where the input comes from, where the output is written, who steps in when the result is wrong, or how cost is tracked, the system either does not run or quietly starts producing wrong answers. beynart’s AI workflow library is a set of production-grade pieces where those details are already solved. We adapt them to your needs instead of writing every workflow from scratch.

The cost of inaction is concrete, but it gets ignored because it stays invisible. We routinely see marketing and operations teams spend a full day a week on repetitive work — screening leads, classifying incoming tickets, writing meeting notes into the CRM, preparing weekly reports. When that work stays manual even though it’s rule-bound and high-volume, the team spends its time on copy-paste routine instead of strategy. And ad-hoc, unaudited AI that lives in one person’s browser tab creates inconsistent output, lost knowledge, and quiet compliance risk on top of it.

Common failure points

Understanding why pilots don’t scale is more instructive than defining the solution. We see the same four patterns over and over.

  • Single-person dependency. The workflow lives in one person’s head and one browser tab. When they go on leave or leave the company, the knowledge goes with them.
  • Undocumented prompts. Where did the prompt that worked go, which version was good, why did it change? Nobody knows. Every run starts from scratch.
  • No validation. Output ships straight through. When the model has an off day, there’s no check to catch it, so the mistake reaches the customer.
  • Invisible cost. Because token spend and time saved go unmeasured, whether the investment is worth it stays an open argument; the budget owner isn’t convinced, the project stalls.

The library is designed to close each of these four patterns: ownership is written into the catalogue, prompts are versioned, checkpoints inspect output, and reporting makes cost visible.

What we deliver

We turn repetitive tasks into a library your team can own and build on — not a one-off set of prompts.

  • A workflow catalogue. Twelve-plus templates covering lead qualification, ticket triage, content summarisation, call transcription, and post-call CRM updates. Each documented with defined inputs, expected output, and an owner.
  • Self-hosted n8n environment. Cloud or on-prem options with audit logs, versioning, and role-based access. Your data does not leave your perimeter, and every run leaves a trace.
  • LLM integration layer. Support for OpenAI, Anthropic, Azure OpenAI, and self-hosted Ollama. A documented mapping of which model fits which task at the right cost-quality point.
  • Human-in-the-loop checkpoints. Approval gates at uncertainty thresholds, output quality scoring, anomaly alerts, and a monthly audit report. On a risky task the flow stops and a human steps in.

Our approach

We don’t try to automate every task. Automating a task at the wrong stage costs more than leaving it manual. So we work from a maturity model and grade workflows across four levels:

  1. Manual. The task is done entirely by hand. Low-volume or highly variable tasks should stay here; the automation investment won’t pay back.
  2. Assisted. A human is in the driver’s seat, AI assists. It drafts, a human edits and approves. Most creative or judgment-heavy tasks live healthiest here.
  3. Supervised automation. AI runs end to end, but every output passes a human approval before it ships. Ideal for medium-risk, medium-volume tasks.
  4. Full automation. The workflow runs without human intervention; humans only review exceptions and anomalies. Only mature, low-risk, high-volume tasks graduate to this level.

We decide whether to automate a task with three questions: What does a mistake cost? How often does the task repeat? How predictable is the output? A workflow doesn’t jump to the top level in one go; it climbs between levels over time. We don’t move a task to full automation until it has run at the assisted level for months and built up trust. This gradual climb lets the team get used to the new process and lets the error rate be watched and brought down to an acceptable threshold.

Process

  • Week 1: Process inventory — which repetitive tasks are good automation candidates and which decisions should stay human. Deliverable: a prioritized task map and a quick-win list.
  • Week 2: Pilot selection (typically 2 to 3 workflows), n8n environment setup, first prototype. Deliverable: working pilot workflows and measured time savings.
  • Week 3: Human-in-the-loop checkpoints, log and alert configuration, team training. Deliverable: the validation layer and a handover guide.
  • Week 4 onward: Measurement and expansion — one or two new workflows per month; the failures are retired. Deliverable: a run dashboard and a next-quarter roadmap.

What we promise — and what we don’t

To be honest, AI doesn’t solve every task, and the “let’s automate everything” promise usually ends in disappointment. Creative strategy, delicate negotiation, and judgment calls are human work; we don’t recommend automating them. Our promise is more modest and more reliable: take repetitive, rule-bound, high-volume work off your team’s shoulders and move it into safe workflows, so people’s time is freed for the work that genuinely needs a person.

Sample outcome

For an e-commerce group’s customer support team, we automated lead qualification and standard FAQ responses. For the first weeks the tasks ran at the assisted level; once the error rate fell to an acceptable threshold, they were promoted to supervised automation. Response time dropped from 6 hours to 12 minutes, and tickets handled per support headcount went up 3.2x. In the 14% of cases where the AI was uncertain, the workflow routed straight to a human — no loss of control. Not extravagant, but a repeatable gain.

Who it’s for

This work fits best for teams that have experimented with a few prompts but couldn’t turn that into a lasting process. Marketing and operations teams with repetitive, high-volume, clearly-ruled tasks see the fastest return. By contrast, the return on building a library is low for very small teams that don’t yet have a clear repetitive task or whose work is entirely creative judgment; in that case it makes more sense to pilot a few tasks at the assisted level first. The sector isn’t the deciding factor: the same method works in retail, manufacturing, and services; only which tasks to prioritize changes.

FAQ

Is our data or are our prompts used to train the model? No. n8n runs in a self-hosted environment, and we build the LLM integration on enterprise settings where your inputs are not included in the model provider’s training data. Data boundaries are the first step of the setup.

Will it work with our existing tools, or do we need to buy new ones? In most cases it works with your existing tools. We integrate workflows with your CRM, content system, and messaging tools; we don’t force a move to a new platform.

What happens if the AI produces a wrong output? That’s exactly what the human-in-the-loop checkpoints are for. Risky tasks require human approval, outputs are scored before they ship, and every run is written to the audit log.

How do we measure results? The run dashboard shows each workflow’s frequency, the time it saves, and its token cost. You see return on investment in concrete numbers within the first quarter.

To adapt this library to your operations inside the MarTech and AI operations pillar, talk to us.

deliverable

Customer Data Pipeline

Event tracking, server-side GTM, CDP, warehouse, reverse-ETL — GDPR-ready, one source of truth.

MarTech & AI Operations — section visual

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.

Why this matters

Customer data is captured differently across channels in most companies. Names conflict, half the events arrive incomplete, and nobody owns a GDPR or KVKK inventory. “Is our attribution actually correct?” stays unanswered. A customer data pipeline puts this on a contract: why each event exists, which user consent it was captured under, where it lives, and who consumes it. In the pipeline beynart builds, an event is defined once and read the same way in every downstream system.

The cost of inaction grows over time. The longer data stays scattered and undefined, the more conflicting records pile up and the more expensive they are to fix later. The same customer gets two different campaigns, a loyal customer is annoyed by a “new customer” offer, and because incomplete data reaches the ad platforms, budget flows to the wrong channels. A pipeline tied to one source of truth, by contrast, is the only solid foundation for accurate segmentation, trustworthy attribution, and an audit-ready consent inventory.

Familiar symptoms

The symptoms of scattered data get described the same way in every team. If you recognize a few of these, the problem isn’t the data itself — it’s the scatter.

  • “Which number is right?” Two teams pull the same metric from two systems and the numbers don’t match. Half the meeting goes to arguing about the source.
  • Missing events. Some conversions never arrive because of browser ad-blockers; the attribution report looks worse than reality.
  • Duplicate records. The same customer appears multiple times under different IDs across channels. Campaigns go out twice and reports inflate.
  • Ownerless events. No one knows why an event was added, which consent it was captured under, or who consumes it.

The common root of these symptoms is the absence of a single source of truth. The pipeline is designed to fix that root at the source — not to hide the symptom in a report.

What we deliver

We tie data to one source of truth from the moment it’s captured to the moment it’s used. The goal isn’t a dashboard — it’s a clearly-owned data layer every team can rely on in daily decisions.

  • Event tracking schema. A tracking plan in Mixpanel, Amplitude, or Snowplow format. Forty to eighty events and properties with owners, trigger location, and consent category. Schema validation runs in CI.
  • Server-side GTM and CDP. Server-side collection (sGTM) instead of in-browser; routing to a CDP through Segment, RudderStack, or our own connector. Clean data sent to ad platforms via the Conversions API.
  • Warehouse modelling. Event-level tables built with dbt into user, session, and conversion marts. Identity resolution rules (anonymous to known) documented and tested.
  • Reverse-ETL. Segment streams from the warehouse to CRM, email, ads, and support tools via Hightouch or Census. “Why is the campaign data late again?” stops happening.

Our approach

We measure data before we move it. Moving bad data faster only amplifies the problem. We position the work against a data maturity model:

  1. Scattered. Data sits in silos, moved by manual exports. There’s no single source of truth. This is where most teams start.
  2. Connected. Systems are integrated but consistency isn’t guaranteed. Data flows, but quality, missing-event, and identity problems persist.
  3. Managed. There’s one source-of-truth data layer, quality is measured, and every event has an owner. Teams trust the same number.
  4. Activated. The data layer feeds not just reporting but automated segmentation, activation through reverse-ETL, and triggered workflows.

We usually find teams at the “scattered” or “connected” level, and the target is typically “managed”; “activated” is the next quarter’s work. We prioritize on two criteria: which data affects the most decisions, and which source produces the most errors. We don’t recommend skipping levels — building activation first for a team at the “connected” level does nothing but spread bad data faster. Identity and quality settle first, then activation comes.

Process

  • Weeks 1–2: Event inventory and tracking plan; consent and KVKK/GDPR strategy workshop with legal, product, and marketing. Deliverable: a tracking plan document and a data quality scorecard.
  • Weeks 3–4: Server-side GTM setup, CDP integration, first 20 events live. Deliverable: a working collection layer and CDP flow.
  • Weeks 5–6: Warehouse models, identity resolution, dashboard quality reports. Deliverable: dbt marts and identity-matching rules.
  • Weeks 7–8: Reverse-ETL flows, stakeholder training, runbook, and data quality alerts. Deliverable: activation flows and a maintenance runbook.

A pipeline doesn’t live without ownership

The technical build is half the job. For the pipeline not to degrade over time, every event and every data field needs an owner: who approves a new event when it’s added, who looks at a quality alert when it fires, who updates the “customer segment” definition? We make these responsibilities explicit during handover. A pipeline with undefined ownership scatters again within a few months; that’s why the runbook and training aren’t an optional add-on to the build but a required part of it.

Sample outcome

For an omnichannel retail group, we moved 64 events onto a single pipeline in 8 weeks. Data loss caused by browser ad-blockers fell from 22% to 3%. Sending clean data to the Conversions API improved ROAS reporting accuracy by 19%. We defined the tracking plan and event ownership together with the team, so the build held up after handover instead of degrading; the KVKK inventory passed its first audit on the first attempt. A modest but lasting improvement.

Who it’s for

This work fits best for teams that touch their customers across more than one channel and whose data is spread across several separate systems. Teams that invest in advertising, need to trust their attribution, and are expected to be audit-ready see the highest return. By contrast, building an end-to-end pipeline can be premature for a very small team running on a single system with already-clean data; in that case a tracking plan and simple quality rules are enough to start. The sector isn’t the deciding factor: the same scatter shows up in retail, manufacturing, and services; only the names and number of the sources change.

FAQ

Do we have to replace our existing CRM and tools? No. We build the pipeline on top of your existing tools. The goal isn’t to swap your systems but to remove the inconsistency between them and tie them all to a single truth.

How are consent and KVKK/GDPR handled? In the first weeks we run a consent workshop with legal, product, and marketing. Every event is tagged with a consent category; access boundaries, retention periods, and deletion requests are built into the pipeline from the start and stay auditable.

How far back do we have to clean the data? You don’t have to clean everything up front. We first make the live data used in decisions manageable, then stage historical cleanup by impact.

Does the pipeline stay current? Yes. The collection and reverse-ETL flows sync sources on a schedule; data quality alerts stop a broken source from silently shipping bad data.

To build your data pipeline end to end inside the MarTech and AI operations pillar, talk to us.

05

Related work

by the numbers

We start at strategy and end in production.

10x

Data pipeline speed

Realtime

AI operations

99.9%

Automation uptime

8+

Integrated platforms

our approach

We turn the pillar into an operational architecture

From strategy conversation to implementation — case-specific, ongoing.

How we work

capabilities

Stand-out capabilities within this pillar

Each one independent or composed; shaped to the case.

Data infrastructure

Event tracking, warehouse and activation layer — set up from scratch or refactored.

Automation

Workflows that connect ad, CRM and customer experience as one system.

AI operations

We embed AI in the operational layer — not as a feature, but as a muscle.

Not sure where to start?

Let's find the layer that fits your need and map where the architecture begins.

Talk to us

customer voice

Working under MarTech & AI Operations, everything from strategy to execution is measured and evolves. beynart is an engineering partner running everything under one architecture.

Ali Rıza Tuncer

Founder, beynart

Explore our case studies

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contact

The right people, from the first message.

No gatekeepers, no brief relay. The team that talks strategy is the team that builds the system. Tell us where you want to grow — we bring the architecture.

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