Intelligence platforms
Your marketing, product and customer data unified in a cloud data warehouse you own. BigQuery and Dataform at the centre, with governed definitions and consistent metrics across the business.
What's the problem?
Your data lives in a dozen places. Your analytics tool knows about behaviour. Your CRM knows about customers. Your ad platforms know about spend. None of them talk to each other. When someone asks which marketing channels actually drive revenue, the answer takes weeks of spreadsheet wrangling - and nobody fully trusts it when it arrives.
A warehouse full of raw tables is just a more organised mess. The real unlock is governed definitions, a semantic layer everyone trusts, and the structure that turns unified data into something you can actually think with.
Ingestion, transformation and unification
Everything your organisation knows, flowing into one environment and making sense together.
- •BigQuery as your central hub
- •Marketing, product, customer and operational data connected
- •Dataform for governed, version-controlled transforms
- •Single customer view and account-based views
- •Composable CDP with privacy-compliant identity resolution
- •Data enrichment: CRM, call-centre, ad platform and third-party data joined to your first-party analytics
- •Structured and unstructured data - not just rows and columns
Related insights
Control, governance and sovereignty
Your data, your infrastructure. No vendor lock-in, no renting access to your own information.
- •First-party data strategy on infrastructure you own
- •Access control, security and audit trails
- •Schema evolution and breaking change management
- •Cost optimisation as data volumes grow
- •No black boxes - everything is transparent and documented
The intelligence layer
This is what separates a warehouse from a platform. Data that understands itself - so people and AI can use it with confidence. We built SEAM to make this real: define your metrics once, and every dashboard, model and agent resolves them the same way.
- •SEAM: semantic governance that sits on top of your warehouse
- •Define metrics once in YAML - dashboards, models and agents all share them
- •One version of the truth across every team, enforced by infrastructure
- •Vertex AI and Agent Development Kit for agentic data preparation
- •Full audit trails - know what was queried, how it was resolved, and why
Accessibility and activation
Intelligence trapped in a warehouse is wasted. We push it into the tools and workflows where decisions happen.
- •Reverse ETL to CRM, email and ad platforms
- •Self-serve access for analysts and stakeholders
- •Enriched data powering personalisation and targeting
- •Multi-cloud connections: Snowflake, Azure, Redshift
- •The platform that dashboards, agents and models build on
Related insights
What changes for you
Questions that used to take weeks get answered in minutes. Your analysts work from one governed dataset instead of stitching spreadsheets together. Dashboards are consistent. Models are reliable. Agents give trustworthy answers - because SEAM governs what they can access and how they interpret it.
And it compounds. Every new data source enriches everything that came before. Every product you build on top draws from the same foundation. Nothing starts from scratch.
11 sources unified
20+ analyst hours saved/wk
Eaglemoss had 11+ disconnected commercial and ERP sources. We unified them into a central BigQuery warehouse, automating manual reporting and saving the analysis team over 20 hours every week.
See our work →
Ad to enrolment connected
Full-funnel visibility
We built a Student Recruitment Intelligence Platform connecting top-of-funnel ad impressions to SITS enrolment records. The university now has full-funnel visibility into the true ROI of every channel.
Read the full story →
GenAI assistant on unified data
Call volumes reduced 20%
We integrated GA4 and call-centre data in BigQuery, then built a GenAI assistant that lets the commercial team query it in natural language. The unified data revealed UX friction points that reduced call volumes by approximately 20%.
Read the full story →
Unified source of truth
Single customer view
We centralised data from two websites and two apps into BigQuery, building unified models in Dataform. This created a single customer view for personalised, consolidated reporting across the brand portfolio.
Read the full story →Composable ELT architecture
87% cost reduction
A digital marketing organisation was paying £90k/year for a managed data platform. We replaced it with a composable GCP-native architecture in 10 weeks, cutting annual costs to £11k - an 87% reduction.
See our work →
Siloed data integrated
Within 2% variance
Marketing, Product and Finance each worked from disconnected sources, producing conflicting numbers. We unified everything into a centralised BigQuery schema with Dataform transformations. Finance and Marketing now reconcile revenue within 2%.
Read the full story →Reporting errors reduced ~40%
Modular Dataform architecture
Multiple teams were running siloed queries and fragile pipelines. We migrated everything into modular, version-controlled Dataform models with automated data quality tests. Reporting errors dropped ~40% and analysts reclaimed 10–15 hours per week.
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Analytics built from scratch
Ongoing managed engagement
Marqeta processes billions in card transactions for the world's largest fintechs. We built their analytics foundation - GA4, GTM governance, flattened BigQuery tables, multi-source ingestion - and continue to manage it.
See our work →
E-commerce intelligence
Five revenue streams unified
BFI generates revenue from memberships, subscriptions, cinema tickets, print and merchandise - all on separate platforms. We built the tracking and data layer to unify these streams into one coherent commercial view.
See our work →Semantic governance for AI agents
Define once, resolve everywhere
Every platform we build needs a governance layer - consistent metric definitions, source hierarchy, entity resolution and full audit trails. SEAM is how we deliver it. Define your business logic once in YAML and every dashboard, model and agent resolves it the same way. No replatforming required.
See it in action →How to get started
GA4 to BigQuery wizard
Your GA4 data in BigQuery with governed Dataform transformations. Fast.
Warehouse rescue
BigQuery instance nobody touches? We audit, restructure and bring it back to life.
What clients say
Collaborating with Measurelab has been an exceptional experience. Their team brings a depth of knowledge and technical expertise that greatly enhances our internal capabilities. They have operated as a seamless extension of our team, demonstrating professionalism, responsiveness, and a genuine commitment to delivering high-quality outcomes.
Where this leads
Once the platform is in place, build intelligence on top - dashboards, models, agents.
Not sure if your data is ready for AI agents? Let's find out together.
Technology we developed to help govern how AI agents access and interpret organisational data. For the technical detail.
We manage the platforms we build. Monitoring, maintenance, evolution.
Latest thinking
Recent insights from our team on intelligence platforms.
BigQuery Tips: When your query is technically correct but BigQuery won't run it
There is a particular kind of frustration that comes from staring at a query you know is correct and watching it fail. No syntax error. No logic problem. Just a wall. We hit two of them on the same project. What we were building The job was to migrate ga4_daily_snapshot for a large enterprise client from a BigQuery scheduled query into a proper Dataform pipeline. The scheduled query had been added to over time until it was too large to maintain with any confidence. Moving it to Dataform w
#146 Assimilated by Anthropic: What We Lost When the Harness Died
Anthropic's clampdown ended the OpenClaw era. Dara and Matthew cover Opus 5, fake GitHub identities in the UK safety tests and EU AI Act Article 50, then ask what was actually lost when the custom harness died, and whether the frustration since is their own fault.
BigQuery Tips: The subquery in your WHERE clause that's scanning your entire table
There is a pattern that appears in a lot of BigQuery pipelines and looks completely reasonable. You have a control table that stores the latest processed date. Rather than hardcoding a date into your query, you pull it dynamically: SELECT * FROM `project.dataset.events` WHERE event_date = ( SELECT latest FROM `project.dataset.control_table` ); The query returns the right results. The logic is clean. And if your events table is large and date-partitioned, you might be scanning the entire
#146 Assimilated by Anthropic: What We Lost When the Harness Died
Anthropic's clampdown ended the OpenClaw era. Dara and Matthew cover Opus 5, fake GitHub identities in the UK safety tests and EU AI Act Article 50, then ask what was actually lost when the custom harness died, and whether the frustration since is their own fault.
Ready to build your intelligence platform?
Whether you need a readiness assessment or a full platform build, we'll meet you where you are.