Marketing spend is scaling.

Are your operations?

We advise how to build your data, AI and MarTech infrastructure that lets your marketing scale, and surfaces your impact on the business.

Are your Marketing operations helping or hindering?

The marketing function you built for execution at one scale is expected to continue to perform as you grow. As you scale you add campaigns, channels, technology, agencies, and people to continue to deliver. Meanwhile, AI is constantly changing the realm of possibility for marketers. Systems get bolted onto systems and strain as processes change, data is siloed across more channels with no way to bring it together, and measurement frameworks designed around last year’s assumptions no longer match today’s business. Friction increases, and demonstrating impact becomes more complex.

This is not a failure of the marketing team, it is an operational inflection point. To get to the next stage of growth, you need your marketing infrastructure, systems and processes to ‘just work’.

We diagnose your operational problems, looking at end-to-end systems, data and technology architecture, ways of working, measurement frameworks, and AI readiness. We approach these problems with an operational design lens: what is the desired outcome, what resources are available, what does the future look like for your company, how can we design a future-proof system that scales with you.

Simon spent more than 7 years at Arm reaching director level in marketing operations working across data architecture, marketing automation, marketing insights, SEO, advertising and campaigns. He wrote Arm's first brand measurement framework, and unified data from over 30 marketing channels into a single AI and Data platform, which was used to provide the insights that directly informed budget decisions on the company's largest marketing investments. He is now leads every Build Operations engagement, applying his domain knowledge to support more companies with their marketing operational challenges.

You’re in the
right place if:

  • Your marketing data is siloed. Campaign performance lives in separate tools, CRM records are inconsistent, and nobody has a single, reliable view of what the marketing function is producing commercially.

  • Leadership cannot see what marketing is contributing to pipeline and revenue. The CFO has asked. The CTO has flagged it. Or marketing has tried to answer and produced a report that didn't land.

  • You are a scaling company — Series B and above — where marketing investment is increasing but the measurement infrastructure has not kept pace. The spend is growing. The accountability is not.

  • You are an established technology business — semiconductor, industrial AI, enterprise software, automotive tech — that is increasing marketing investment for the AI era but does not have the infrastructure to justify or optimise that spend.

  • You are looking to transform your marketing operations with AI but don’t know where to start or how to design it in a future-proof way, so that you’re not running new transformation programs every quarter.

You’re in the
wrong place if:

  • Your primary need is creative campaign execution rather than measurement and infrastructure. Build Operations is the foundation — it does not replace the campaign programme. Build Pipeline covers the commercial GTM layer.

  • You are an early-stage company with a small marketing function and no significant data architecture problem yet. The infrastructure question becomes acute at scale — if you are not there yet, a lighter-touch attribution model may be the right starting point.

  • You are looking for a platform implementation partner to configure an existing CRM without strategic input on the underlying data model. We design the architecture and direct the implementation — we do not do commodity CRM administration.

What Build Operations looks like in practice

Every engagement begins with a diagnosis of where your marketing operations are now vs where they need to be for the future. The work that follows is designed for your company’s real-world commercial cycle, not one borrowed from a B2B SaaS template.

  • A diagnostic of the existing infrastructure — CRM architecture, data taxonomy, attribution model, reporting framework, and tool stack. The audit identifies where the data is breaking, why the attribution model is producing inaccurate outputs, and what needs to be rebuilt versus reconfigured. Fixed price. The starting point for most Build Operations engagements.

  • Architecture-first CRM design for deep tech commercial cycles. We define the data model, pipeline stage taxonomy, lead scoring logic, and integration architecture before any platform is configured — because configuring the wrong architecture in the right platform produces the same broken output faster. We work with Marketo, Salesforce, and HubSpot, and with the custom data layers that complex deep tech programmes require.

  • A unified data model that connects campaign data, CRM data, website data, and partner programme data into a single, coherent view. Designed for the actual buying journey of a deep tech company — not a standard e-commerce or SaaS funnel model that does not account for OEM partner relationships, multi-stakeholder evaluation cycles, or hyperscaler co-marketing activity.

  • Custom attribution models built for long, technically complex sales cycles. We design the attribution logic that correctly apportions pipeline and revenue credit across the full buying journey — from first content touch through analyst briefing, event attendance, and hyperscaler co-marketing to design-win conversation. The output is an attribution model that the CFO can interrogate and the sales team will not dispute.

  • Marketing reporting that translates campaign and pipeline activity into the commercial language that boards and CFOs use. Not a marketing dashboard — a commercial reporting framework that connects marketing investment to pipeline contribution, revenue influence, and cost efficiency. Designed to answer the question leadership is actually asking, not to display the metrics the marketing team finds most flattering.

  • Marketing infrastructure designed for an AI-first world. This means integrating AI tooling into the workflow architecture, not as a bolt-on, but as a structural component of how the marketing function produces, distributes, and measures output. For deep tech companies increasing marketing investment in the AI era, this is the infrastructure that makes that investment optimisable at scale.

Where to start

Most Build Operations engagements begin with a fixed-price audit — a diagnostic of where the infrastructure has broken and what needs to be rebuilt. The audit produces a clear remediation plan and serves as the basis for the build that follows.

Infrastructure Audit — Fixed price A diagnostic engagement covering CRM architecture, data taxonomy, attribution model, reporting framework, and marketing tool stack. Output: an honest assessment of what is broken, why, what needs to be rebuilt, and in what order. Typical duration: 2–4 weeks depending on the complexity of the existing stack.

Designed for companies that know something is wrong but are not sure where the problem actually sits — or companies that want an independent assessment before committing to a build.

AI-Native Marketing Operations Infrastructure design that integrates AI tooling as a structural component of the marketing operations stack — not as a workflow add-on. For deep tech companies investing significantly in marketing for the AI era, this is the infrastructure that makes that investment optimisable, measurable, and scalable.

Demand Infrastructure Build A full marketing infrastructure build for scaling companies launching their first properly structured marketing function — or rebuilding one that has broken under growth. CRM architecture, data model, attribution framework, reporting structure, and tool stack integration. Built for the actual commercial cycle of a deep tech company.

Measurement Retainer Ongoing data governance, attribution maintenance, and marketing measurement support. For companies post-build that need the infrastructure to remain accurate as the commercial programme scales, the product portfolio expands, and the reporting requirements evolve. Includes quarterly board-ready reporting reviews.

If the data doesn't add up and leadership is starting to ask why — the audit is the right first move.

We work with a small number of scaling deep tech companies at any one time. If the marketing infrastructure has broken under growth and you need an honest assessment of where the problem sits — get in touch.

This is not a pitch. It is a direct conversation about what is broken and what it would take to fix it. The audit is a fixed-price, bounded engagement — designed so you can see how we work before committing to a full build.

FAQ

  • The audit covers five areas: CRM architecture (data model, object structure, pipeline stage definitions, lead scoring logic), attribution model (how pipeline and revenue credit is currently assigned, and where the model is producing inaccurate outputs), data taxonomy (whether campaign data, CRM data, and website data share a consistent naming structure that allows them to be joined reliably), reporting framework (what marketing is currently reporting to leadership and whether it answers the commercial questions being asked), and tool stack integration (where data is being lost, duplicated, or incorrectly transformed between platforms). The output is a written assessment with a prioritised remediation plan. It is not a slide deck of recommendations — it is a diagnostic with a clear build sequence.

  • A marketing ops hire understands the tooling but typically lacks the deep tech domain knowledge to design an attribution model that accounts for OEM partner relationships, hyperscaler co-marketing programmes, or multi-stakeholder design-win evaluation cycles. A MarTech agency knows the platforms but approaches configuration as a best-practice exercise — which produces a standard implementation rather than a data architecture designed for your specific commercial cycle. We combine deep tech domain knowledge with marketing data architecture — the two disciplines that are both necessary and rarely found together. The resulting infrastructure is built for how your company actually sells, not for how a generic B2B SaaS company sells.

  • We work with Salesforce, HubSpot, and Marketo at the CRM and marketing automation layer. At the data layer, we design the underlying data model and integration architecture — and can work with existing data infrastructure including data warehouses, BI tools, and custom attribution systems. We do not recommend a platform before we understand the commercial cycle and data requirements. The platform is the implementation vehicle for the architecture, not the starting point for the design.

  • An infrastructure audit typically takes 2–4 weeks. A full build — CRM redesign, data architecture, attribution model, and reporting framework — typically takes 8–16 weeks depending on the complexity of the existing stack, the volume of historical data that needs to be migrated or corrected, and the integrations required. Companies with years of CRM technical debt should expect the upper end of that range. The build timeline is always defined in the audit output — we do not estimate before we have seen the existing infrastructure.

  • Board-ready marketing reporting answers four questions the board and CFO are actually asking: how much pipeline did marketing contribute this period; what is the cost of generating that pipeline; which activities drove the most commercial output; and is marketing spend increasing or decreasing in efficiency. It does not include marketing-specific metrics — open rates, impressions, sessions — unless they are connected to a commercial outcome. The reporting framework we design produces a single view that connects campaign investment to pipeline contribution and revenue influence, expressed in the commercial language that financial leadership uses.

  • AI-native marketing operations means designing the marketing infrastructure — data architecture, workflow systems, content production processes, and measurement frameworks — with AI tooling as a structural component rather than a post-hoc addition. For deep tech companies, this matters because the volume and technical complexity of the content and data they need to produce and manage is increasing faster than headcount. An AI-native infrastructure allows a lean marketing function to operate at greater scale — producing technically credible content, managing complex partner programmes, and maintaining data governance — without a proportional increase in resource. The key design principle is that AI tooling should be embedded in the workflow architecture, not layered on top of a process that was designed without it.