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Embedded delivery · Marketing, RevOps & applied AI

The Forward Deployed Engineer for marketing & RevOps

Borrowed from how AI labs ship software: an engineer embedded in your team who builds marketing and RevOps systems inside the HubSpot, Salesforce, or CMS stack you already run. Working code, not slide decks or deliverables thrown over the wall.

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The problem

Your systems should work together. Your team should not be the integration layer.

The symptoms we see most often in B2B marketing and revenue operations:

  • Manual handoffs between tools and teams
  • Spreadsheet-driven processes that don't scale
  • Disconnected data across CRM, marketing, and support
  • Slow campaign delivery and repetitive setup
  • Incomplete or inconsistent CRM records
  • Duplicated work and reporting by hand
  • AI experiments that never reach production systems
  • Redundant and misaligned automation systems

Why Floodlight

An engineer on your team, not a report on your desk

What changes when the person who designs the system is the person who builds it, working inside your stack.

Builds, not advises

Production marketing and RevOps systems, shipped not recommendations you have to implement yourself.

Inside your stack

Works in HubSpot, Salesforce, and the CMS, data, and tools you already run, augmenting rather than ripping out.

Marketing & RevOps fluent

Engineering embedded in the domain: lead routing, lifecycle, reporting, and workflow automation.

You stay in control

You sign off strategy, quality, and brand; the embedded engineer ships the work.

How we work

Embedded from day one

How an embedded engagement runs, working alongside your team, in your systems.

  1. Assess

    Evidence-led diagnosis of your stack, processes, and goals.

  2. Embed

    An engineer joins your team, inside your CRM and tools.

  3. Build

    Production marketing and RevOps systems, shipped iteratively.

  4. Optimise

    Measure, refine, and keep improving together.

What we build

One connected operating system

Marketing, operations and AI are not separate projects. They are one system, and most of the pain sits where they meet. We assess first, then build across all three, inside the platforms you already run.

Marketing & Content

Marketing Operations

Campaign operations, lifecycle, segmentation and reporting, so launching a campaign stops depending on who remembers the process.

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AI-Search Content

Clear, source-backed content built to be understood, extracted and correctly attributed by people and AI systems.

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Content Infrastructure

Structured content systems your team can reuse across the website, applications and other channels.

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Operations & Revenue

CRM & RevOps Engineering

Lifecycle, routing, scoring, pipeline and reporting, engineered inside HubSpot, Salesforce or Pardot.

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MarTech Stack Architecture

Audit, rationalise and connect the tools you own, so data moves properly and you stop paying for overlap.

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Workflow Automation

Reliable automation across n8n, Make.com and APIs, with error handling, monitoring and your team approving what matters.

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Applied AI

AI Strategy & Readiness

An evidence-led view of where AI will pay back, and a prioritised path from pilot to production.

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AI Agents & Applications

Assistants and multi-step AI workflows connected to your CRM and data, with your team signing off what ships.

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AI Governance & Enablement

The policies, oversight and training that let your organisation use AI safely and actually adopt it.

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Productised solutions

AI systems for the work your team does every day

Services are what we practise. Solutions are what we've productised. These are the builds we've already shaped into repeatable systems: problem-led, built with OpenAI, Anthropic and other suitable foundation models, inside the stack you already run.

AI Marketing Ops Assistant

Briefs, assets, tracking, and campaign records, with approval stages your team controls.

AI CRM Assistant

Summarises records, flags gaps, prepares follow-ups, and supports pipeline reviews.

AI Knowledge Assistant

Answers staff questions from approved internal sources, with citations and permissions.

Lead Management & Routing

Capture, enrich, dedupe, score, route, and monitor, with service-level alerts.

AI Content Operations System

Research to publish, with fact-checking and human approval built in.

AI Customer Support Assistant

Searches approved knowledge, routes tickets, and escalates sensitive cases to people.

Technology ecosystem

We work with the tools and models you already use

Model-neutral by design. Built using OpenAI and Anthropic technology, and other suitable foundation models, connected to the platforms your business runs on.

AI & application layer

  • OpenAI (GPT)
  • Anthropic (Claude)
  • other foundation models
  • agent frameworks
  • retrieval systems

CRM & marketing platforms

  • HubSpot
  • Salesforce
  • Account Engagement / Pardot

Automation & integration

  • n8n
  • Make.com
  • APIs
  • webhooks
  • Airtable
  • Google Workspace
  • Microsoft 365

Content & CMS

  • HubSpot CMS
  • WordPress
  • headless / structured CMS

Evidence

Proof, when it's real, never before

We publish a client result only when the result, period, method, evidence, and permission are all confirmed. Until then, we show representative workflows and capability demonstrations, clearly labelled as such.

Internal implementation

StayOps: a production operations platform

A working application we built and run ourselves: user accounts and authentication, share-link invites for external collaborators, and automated daily backups, deployed on our own server infrastructure and in live daily use. We include it because it is the clearest answer to "do you actually build things?" It is not a prototype or a demo environment, but software in production, with the operational responsibility that comes with it.

Anonymised engagement

CRM and email platform migration into HubSpot, using AI

A migration of customer relationship and email marketing data into HubSpot, with AI applied to the parts of migration work that are normally slow and manual: mapping fields between systems, classifying and cleaning records, and flagging what needed a human decision rather than guessing at it. Migrations fail on data quality, not on the platform switch. Applying AI to the mapping and cleaning stages, with a person approving the ambiguous cases, is what keeps the timeline realistic without accepting a dirty database at the other end.

Who you work with

Led by a senior practitioner, not handed to juniors

Edwin Raymond
Lead practitioner, marketing operations & RevOps engineering

Fifteen years in B2B marketing operations, with five of them as senior marketing automation lead at Criteo, a NASDAQ-listed technology company, running marketing operations across the Americas, Europe and Asia-Pacific. That work included the global rollout of Pardot Connected Campaigns and Lightning across three regions, an account-based marketing programme built on Demandbase, Salesforce and Pardot, and the governance frameworks the regional teams ran on.

Floodlight is the continuation of that work with the implementation attached: Salesforce and HubSpot CRM architecture, lead scoring and routing, attribution reporting, and AI workflow systems built with n8n, Make.com and the OpenAI and Anthropic APIs.

An engineering degree underneath it, BEng (Hons) Electrical Engineering, Queen Mary's University of London, which is the honest reason "marketing engineer" is a description rather than a slogan.

  • Strategy and implementation stay with the same team
  • Works inside your existing CRM, CMS, and automation stack
  • Human oversight and governance at every stage
  • Systems documented and handed over, so you own the result
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No rip-and-replace

We work with the stack you already have

Engineering across the platforms your business runs on: CRM, CMS, and the automation layer that connects them.

Leading CMS & CRM platforms

Insights

Latest from the Floodlight blog

Move your next marketing, RevOps or AI project into production

A short conversation about the systems you run, the work still being done by hand, and whether an embedded build is the right answer, including when it isn't.

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