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AI-Powered Analytics Automation

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Most analytics teams lose a third of their week to work that is repetitive but not quite templated: rebuilding the same monthly deck, chasing a broken tag, reconciling two platforms, writing the same QA notes again. We automate that layer by combining large language models with the APIs and scripting environments you already have — Apps Script, the GA4 and Ads APIs, BigQuery, GTM and Looker Studio — so analysts spend their time on the questions rather than the assembly.

Challenges we solve

Ask any analytics team where their week goes and the answer is rarely analysis. It is exporting, reformatting, reconciling, screenshotting, writing the same commentary with a different number in it, and re-checking tags after every release. None of it is hard. All of it is slow, and all of it is exactly the shape of work that language models plus an API can absorb. The trick is knowing which parts to automate and which to leave alone. Anything with a deterministic answer should be code. Anything requiring judgement should stay human. Language models are best in the middle: summarising, drafting, classifying, explaining and flagging — always with the underlying numbers pulled from a trusted source rather than generated. The symptom is always the same. Marketing quotes one conversion number, finance quotes another, and the meeting turns into a debate about the data instead of a decision about the business. We fix the collection layer first, prove it with test evidence, and then hand you the documentation that keeps it from drifting again.

What we deliver

Automated reporting and commentary

Scheduled pipelines that pull from GA4, Google Ads, Meta, LinkedIn, BigQuery and your CRM, assemble the numbers deterministically, and then use an LLM to draft the "what changed and why" narrative from those numbers. The figures come from the query; only the writing is generated, which is what keeps it accurate.

Google Apps Script automation

Sheets and Slides automation that most teams can maintain themselves: scheduled data pulls, formatted report builds, distribution by email or Slack, threshold alerts and approval steps. We build it, document it, and hand it over rather than making you dependent on us.

Platform API integrations

Direct integration with the GA4 Data API, Google Ads API, Meta Marketing API, Search Console API, GTM API and Looker Studio, for bulk operations that would take days by hand — mass tag audits, naming-convention enforcement, bulk campaign edits, and container diffing.

Automated tagging QA and release checks

Scripted journey replays after each deploy that compare fired events against the tagging plan, with an LLM summarising what changed in plain language and raising a ticket when something breaks. This turns tag QA from an annual audit into a per-release check.

Conversions API and Enhanced Conversions

Meta CAPI, Google Enhanced Conversions for web and leads, LinkedIn CAPI, TikTok Events API and Pinterest API for Conversions — deduplicated against browser events, hashed correctly, and match-rate monitored after launch rather than assumed. This is usually where lost conversion signal is recovered.

Anomaly detection and explanation

Statistical detection on key metrics, with automated segmentation to find where the change came from, and a generated explanation that names the likely driver and the evidence for it — delivered before the weekly meeting rather than discovered in it.

Documentation and knowledge automation

Auto-generated and continuously updated documentation: tag inventories, container change logs, metric dictionaries, dashboard catalogues and onboarding guides, kept current from the systems themselves rather than from someone remembering to update a wiki.

Implementation acceleration

Using LLMs to speed up the build itself: drafting GTM custom templates, generating data layer code from a tagging plan, writing dbt models and tests from a spec, and translating legacy implementations between platforms — always reviewed by an engineer before it ships.

Guardrails, versioning and cost control

Every automation lives in version control with a test suite, a defined owner, an audit log and a token budget. Anything that writes to a production system has an approval step. This is the part that separates a durable automation from a clever script nobody trusts.

Platforms & Tooling

  • Claude
  • OpenAI
  • Google Apps Script
  • Google Ads API
  • Meta Marketing API
  • Search Console API
  • GTM API
  • BigQuery
  • Looker Studio
  • Google Sheets
  • Slack

Process

Week 1

Time-audit

Map where analyst hours actually go and what they are worth.

Week 2

Prioritise

Score candidates by hours saved, risk and build effort.

Week 3–6

Build

Automations shipped one at a time, each with tests and an owner.

Week 7–8

Harden

Review gates, cost caps, monitoring, documentation.

Ongoing

Extend

Training and a backlog your team can work through themselves.

FAQs

It is safe when the numbers are not generated. Our pattern is strict: the figures come from a deterministic query and are inserted into the narrative; the model writes the prose around them and flags what it is unsure about. A human approves before anything goes to a client or an executive. What you must not do is ask a model to both retrieve and describe — that is where invented numbers come from.

Because your team can maintain it. For a lot of reporting automation, Apps Script sits inside the tools people already use, needs no new infrastructure, no procurement and no licence. Where volume, reliability or complexity outgrow it we move the workload to Cloud Functions or your orchestrator — but starting there is usually over-engineering.

It removes assembly work, not analysis. In practice teams end up doing more analysis rather than fewer analysts, because the questions they never had time for become answerable. If your goal is genuinely headcount reduction, we would rather say so upfront than pretend otherwise.

Token budgets per automation, caching of repeated context, small models for classification and large ones only where reasoning is needed, plus spend alerts. We report cost per run so you can see whether an automation is still worth what it costs.

Yes. The Adobe Analytics 2.0 API, Launch API and Adobe I/O all support the same patterns. The Apps Script convenience is Google-specific, so Adobe-centric automations usually run in Cloud Functions or your existing orchestration layer instead.

Then automation will faithfully reproduce the mess, faster. We will tell you if the honest first step is a tracking audit rather than an automation project.

Ready To Make Your Data Work Harder?

Let’s build a trusted measurement foundation that drives smarter decisions and measurable growth.

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