We design and build the complete data stack — from tracking implementation and attribution engineering to AI-powered analytics agents — for agencies and ecommerce brands worldwide.
We cover every layer of the analytics stack — from the first data point captured to the AI agent that interprets it. Each service is built to production standard, not prototype.
From GA4 event modeling to server-side tracking, we implement analytics that actually captures what matters. Consent-compliant, structured, and built for long-term reliability.
Most attribution pipelines are broken. We diagnose and rebuild them at the SQL level — fixing click-ID priority chains, session deduplication, and last-click logic directly in Dataform.
We build production RAG agents and autonomous analytics bots that query your data warehouse, detect anomalies, and surface insights — without manual intervention.
End-to-end pipelines from any source into BigQuery. Incremental loading, partitioning, deduplication, and scheduling — built for daily production use with zero manual maintenance.
Client-side and server-side GTM implementation, Consent Mode v2 integration, and custom attribution scripts that go beyond what standard setups can capture — including first-click and last-click HTML tags.
Real projects, real infrastructure, real results. No prototypes, no proofs-of-concept — everything here is live and running for active clients.
A broken GA4 attribution environment with three silent bugs: Meta campaigns appearing under Google source, sessions duplicating and inflating counts, and a last-click cookie that never updated after the first visit — making "last-click" attribution identical to first-click.
Rebuilt the entire Dataform environment from scratch. Implemented a click-ID-first priority chain (gclid → google, fbclid → meta, before any UTM fallback), resolved attribution at event level before aggregation to eliminate duplication, and migrated last-click tracking to sessionStorage for true overwrite behaviour.
Clean dual first-click and last-click attribution across four reporting tables. Session counts accurate. Revenue attribution now trusted by the client's marketing team for budget decisions.
A digital agency needed weekly ecommerce performance reporting without analyst time. Reporting was manual, delayed by days, and provided no investigation capability — the team only knew something dropped, not why.
Built an autonomous n8n AI agent connected to BigQuery. Every Monday it queries week-over-week metrics, and — if revenue or purchases drop more than 10% — automatically calls a channel breakdown tool and then a device breakdown tool before writing its own structured Slack report. No human triggers the investigation; the agent decides when one is needed.
Zero manual reporting. The agent runs autonomously every Monday, investigates anomalies without being asked, and delivers structured findings with channel and device breakdowns directly to the agency's Slack workspace.
The client's team needed to query BigQuery analytics data daily without writing SQL. Off-the-shelf BI tools gave static dashboards with no ability to ask follow-up questions, and required heavy setup for each new analysis.
Deployed a production RAG agent — Python, Streamlit, and Claude/Gemini API — that translates plain-English questions into BigQuery SQL, executes them safely, and returns answers with Plotly charts and raw data tables. A BM25-based few-shot memory system auto-promotes successful queries via thumbs-up feedback, improving SQL accuracy with every session.
A self-improving analytics interface, live at a private URL, used daily by the client team. The system gets more accurate over time without any engineering involvement — successful queries become future examples automatically.
Practical, deep-dive articles written from real project work — not tutorials repackaging documentation, but the edge cases and lessons that only come from production implementations.
Why your session counts are lying to you, how to find the GROUP BY that's causing it, and the exact CTE pattern to fix it permanently.
The full architecture of a production RAG agent: semantic layer YAML, BM25 few-shot retrieval, BigQuery safety validation, and Plotly charts.
The 1000-day cookie with a guard check that never overwrites — and how to identify if this is affecting your attribution data right now.
The requests library that breaks bracket encoding, the date=1 parameter nobody mentions, and Cloudflare 403s that vanish with one header.
How to build an AI agent that investigates its own anomalies — tools, system prompt design, and the decision logic that makes it autonomous.
Whether you need to fix broken attribution, build a data pipeline, deploy an AI agent, or start from scratch — we want to hear about it. Tell us what you're working on.
We respond within 1 business day.
We'll be in touch within 1 business day.