NexaMarTech

    AI-Powered Marketing Automation Services

    We design and implement marketing automation that removes manual work from lead handling, nurturing and reporting — so your team spends time on strategy instead of list uploads.

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    The problem most teams actually have

    Very few marketing teams fail because they lack a tool. They fail because the tools they own were configured in a hurry, by different people, against different definitions of what a lead is. Forms write to fields nobody reads, scoring rules were tuned once in 2021 and never revisited, and the CRM quietly disagrees with the marketing platform. The result is a team that spends its week reconciling data instead of running programs.

    • Leads sit unactioned because routing depends on someone noticing an inbox
    • Marketing and sales use different words for the same lifecycle stage
    • Nurture journeys fire on time delays instead of real buying behaviour
    • Reporting cannot be reconciled with the CRM, so nobody trusts either
    • Automation lives in one person's head with no documentation or owner

    What AI-powered marketing automation includes

    We treat marketing automation as an operations and data problem first and a campaign problem second. Every engagement produces a documented contact model, a workflow blueprint your team can read, and running automation you own outright — not a black box that requires us to maintain it.

    • Lifecycle and nurture campaign design
    • Lead scoring and routing rules
    • CRM automation and data hygiene
    • Email and SMS automation
    • Customer segmentation
    • Automated marketing reporting

    Key capabilities in detail

    Lead capture and data intake

    Every source of demand — forms, events, chat, ads, partner lists, offline conversations — mapped to one contact and account model, with validation applied at the point of entry rather than cleaned up later.

    • Form, chat and landing-page capture with progressive profiling
    • Offline and event lead import with deduplication rules
    • Email and domain validation, plus bot and spam filtering
    • UTM, source and campaign attribution stamped at first touch
    • Consent capture and preference storage aligned to GDPR requirements

    Lead qualification and scoring

    A qualification model both marketing and sales sign off on, combining firmographic fit with behavioural intent — and, where the free-text signal matters, an LLM classifier that reads what the prospect actually wrote.

    • Fit scoring from firmographic and technographic enrichment
    • Behavioural scoring weighted by observed correlation to closed-won
    • AI classification of free-text form fields and inbound messages
    • Score decay so a warm lead from six months ago is not treated as hot
    • Agreed MQL and SQL thresholds documented and version-controlled

    CRM automation and routing

    The CRM is the system of record, so automation writes to it rather than around it. Assignment, task creation and lifecycle transitions happen without a human moving a record between views.

    • Territory, round-robin and account-based assignment rules
    • SLA timers with escalation when first response is missed
    • Automatic task, activity and opportunity creation
    • Duplicate detection, merging and ongoing data hygiene jobs
    • Bidirectional sync between CRM and marketing platform with conflict rules

    Email automation and lifecycle journeys

    Journeys triggered by behaviour and lifecycle state instead of arbitrary time delays, with deliverability treated as a first-class engineering concern.

    • Onboarding, nurture, re-engagement and win-back journeys
    • Branching logic driven by product usage or CRM state
    • Transactional and lifecycle send separation for deliverability
    • SPF, DKIM and DMARC alignment on every sending domain
    • Send-time, subject and content testing with real significance thresholds

    Customer segmentation

    Segments defined once, centrally, and reused everywhere — so the segment in your ad platform means the same thing as the segment in your email tool.

    • Lifecycle, value and behavioural segmentation models
    • RFM and propensity segments for e-commerce and subscription models
    • Suppression and exclusion lists maintained automatically
    • Audience sync to ad platforms via conversion and audience APIs

    AI workflows and AI agents

    Where a task requires judgment on unstructured input, we add a model — with a defined scope, an evaluation set and a human review path. Where deterministic rules are sufficient, we keep them deterministic.

    • Inbound message triage, routing and summarisation agents
    • Content assistance for briefs, variants and localisation
    • Enrichment agents that research accounts before a call
    • Anomaly and churn-risk detection on lifecycle data
    • Guardrails, evaluation sets and human-in-the-loop review points

    Reporting, data integration and orchestration

    An orchestration layer connects the systems that were never designed to talk to each other, and reporting closes the loop from source to pipeline.

    • API and webhook integrations between CRM, product, billing and support
    • Orchestration in n8n, Make or a custom service depending on scale
    • Closed-loop reporting from first touch to revenue
    • Automated recurring reports delivered to Slack, email or dashboards
    • Error handling, retries and alerting so silent failures surface fast

    What marketing automation covers

    Marketing automation is the set of workflows, data rules and integrations that move a contact from first touch to qualified opportunity without manual intervention. We treat it as an operations problem, not a campaign problem.

    • Capture: forms, events and offline sources mapped to a single contact model
    • Qualify: scoring based on fit and behaviour, with agreed MQL definitions
    • Route: assignment rules, SLAs and CRM task creation
    • Nurture: branching journeys triggered by behaviour rather than time alone
    • Report: closed-loop attribution from source to pipeline

    How we implement it

    Every engagement starts with an audit of the current stack and data model. We document what exists, what breaks, and what should be retired before we build anything new.

    • Stack and data audit with a documented contact/account model
    • Workflow blueprint reviewed with marketing and sales
    • Build in a sandbox, test with seeded records, then migrate
    • Enablement docs and handover so your team owns the system

    Use cases

    Typical situations where teams bring us in for this work.

    Inbound demo request triage

    Requests are classified, enriched, scored and assigned within seconds, with an SLA timer and Slack alert instead of a shared inbox rota.

    Product-led trial conversion

    Product events drive lifecycle journeys, so a trial that activates a key feature gets a different sequence than one that stalls at setup.

    Account-based orchestration

    Account-level signals aggregate contact behaviour into a single buying-group view that triggers coordinated sales and marketing action.

    E-commerce lifecycle automation

    Browse, cart, post-purchase and win-back flows built on RFM segments and synced back to paid channels as audiences.

    Event and webinar follow-up

    Attendance and engagement data flow into scoring and routing automatically, so follow-up happens the same day rather than the next week.

    Marketing reporting automation

    Weekly performance packs assembled from CRM, ad platform and warehouse data and delivered without anyone opening a spreadsheet.

    How NexaMarTech works

    The same delivery method every engagement follows — see our full process for more detail.

    1. 01

      Audit

      We document the current stack, data model, active workflows and where records actually break. You get the audit whether or not you continue with us.

    2. 02

      Blueprint

      A workflow and data blueprint reviewed with both marketing and sales, including agreed lifecycle definitions and success measures.

    3. 03

      Build

      Implementation in a sandbox or staging environment, tested with seeded records that cover the edge cases, not just the happy path.

    4. 04

      Validate

      Shadow-mode running where the automation makes decisions without acting, so you can compare its output to the human process before switching over.

    5. 05

      Launch

      Phased cutover with rollback plans, monitoring and alerting configured before anything touches production traffic.

    6. 06

      Enable and optimise

      Documentation, runbooks and team training, followed by an optimisation cycle against the measures agreed in the blueprint.

    Technologies & platforms

    We are vendor-neutral. These are the platforms we work with most often on this service.

    Automation platforms

    HubSpotSalesforce Marketing Cloud Account EngagementKlaviyoBrazeCustomer.ioMailchimp

    CRM

    HubSpot CRMSalesforcePipedriveZoho

    Orchestration

    n8nZapierMakeCustom TypeScript servicesSupabase Edge Functions

    AI and LLMs

    OpenAIAnthropic ClaudeGoogle GeminiVector search and RAG

    Data

    BigQueryPostgresdbtServer-side GTMSegment

    Benefits & business outcomes

    • Fewer manual handoffs between marketing and sales
    • Consistent lead definitions across teams
    • Reporting that reconciles with the CRM

    Relevant case studies

    SaaS

    AI Lead Qualification & Routing for a B2B SaaS Platform

    Inbound demo requests were triaged by hand in a shared inbox. Follow-up took over a day on average and sales had no consistent definition of a qualified lead.

    Read the case study     Professional services

    An Internal AI Knowledge Assistant for a Regulated Professional Services Firm

    Consultants spent significant time locating precedent documents and prior project material spread across several repositories.

    Read the case study

    Frequently asked questions

    Which marketing automation platforms do you work with?

    Most commonly HubSpot, Salesforce Marketing Cloud Account Engagement, Klaviyo, Braze, Customer.io and Mailchimp, plus orchestration layers such as n8n, Make and custom services.

    Do you migrate from an existing platform?

    Yes. Migrations are run in parallel: we rebuild and validate journeys in the new platform before switching traffic, so no lifecycle sends are lost in the cutover.

    How long does an implementation take?

    A focused implementation typically runs four to eight weeks depending on the number of journeys, integrations and the state of existing data.

    Related resources

    Free website audit tool

    Performance, SEO, security and AI-readiness scoring in one run.

    Case studies

    Challenge, architecture, stack and measured results from real engagements.

    How we work

    Our discovery-to-optimization delivery method, step by step.

    Insights blog

    Articles on AI marketing, automation, analytics and AI search visibility.

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    Find out what your automation is actually doing

    Send us your stack and the workflows you think are running. We will tell you which ones are, which ones silently broke, and what to fix first.

    Talk to NexaMarTech

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