I'm a Vancouver-based consultant who builds secure agent systems for teams buried in recurring analytics, reporting, research, and browser-based operations. Your agents coordinate, use the tools you already have, leave a reviewable trail — and the system stays yours after handoff.
Start with a short fit call — in person around Metro Vancouver, or remote. No polished brief or AI roadmap required.
Example workflow
Coordinator
Breaks down the goal
Data agent
Checks GA4 and warehouse data
Reporting agent
Builds the review-ready brief
Result: one sourced, reviewable deliverable
Scheduled, logged, and ready for a person to approve.
Each product packages the right agents, data connections, and review steps into one clear experience. Your team gets a tool built around the job—not another general-purpose AI interface to configure.
Purpose-built workflow
Shared agent foundation
Human review built in
Customer journey intelligence
Turn GA4 paths and real product screens into scored journeys, prioritized UX findings, AI-generated improvements, and a report your team can act on.
Explore JourneyScoreFrom signal to decision
You do not need an AI strategy deck. You need a recurring operational problem with a clear owner, usable source data, and a result someone can review.
Important briefs and checks depend on someone finding time to pull data, reconcile it, and explain what changed.
You want agents to help, but only with explicit permissions, clear boundaries, and human review where it matters.
The data exists, but moving it between analytics, warehouse, browser, and reporting tools is still a manual workflow.
Your team needs to see what ran, which sources were used, what changed, and where a person should step in.
The value is not another chatbot. It is a controlled system that can split up the work, use the right tools, keep moving, and show your team exactly what happened.
A shared workspace for people and agents
Describe the outcome in a familiar channel. The request, context, decisions, and agent activity stay together instead of disappearing across tools.
Specialists coordinate behind the scenes
A coordinating agent can ask data, research, browser, or reporting agents for help and combine their answers into one result.
Work is tracked to a real outcome
Each job has a goal, checklist, activity state, and outcome. It stays visible until the work is finished, reviewed, or clearly blocked.
Recurring and long-running execution
Recurring reports, data loads, browser tasks, and long-running jobs execute on schedule with outputs linked back to the request.
Products such as JourneyScore combine these proven data, reporting, knowledge, mapping, and browser systems around one clear customer outcome. Custom engagements use the same foundation for your team's workflow.
Org-scoped GIS built for agents: PostGIS geometries, geo image overlays, satellite STAC and spectrum layers, and features linked back to Knowledge Base documents and ClickHouse rows.
PostGIS · STAC satellite layers · LLM map tools
AI-authored analytics decks composed from live ClickHouse data and org-scoped prompts, rendered deterministically with ECharts so the same data always produces the same slide.
ClickHouse-backed · deterministic ECharts rendering
GA4 screen and page analytics turned into a visual user flow with real transition percentages, so you can see where journeys break instead of reading a table and guessing.
GA4 journeys · flow percentages · drop-off detection
A managed ClickHouse and PostgreSQL layer agents can query directly — with schema browsing, connection health, and ETL loads that keep the warehouse current without a hand-built pipeline.
ClickHouse · PostgreSQL / pgvector · managed ETL
Document storage with recursive indexing behind pgvector, so agents answer from your material instead of improvising — and the index refreshes on a schedule through Task Manager.
pgvector retrieval · scheduled reindexing
Playwright-driven browser agents for capture, form submission, and the repetitive workflows nobody should still be doing by hand — schedulable like any other task.
Playwright agents · structured extraction
Start small enough to prove useful, then expand only where the workflow earns it. Every phase has a clear output and a human review point.
Map tracking, data sources, and manual workflows to find the highest-leverage places for agents to contribute.
Connect GA4, Google Search Console, BigQuery, dbt, browser agents, and MCP tools into an orchestrated system scoped to your operations.
Ship live agents and scheduled workflows that execute autonomously — not prototypes that need hand-holding.
Leave agents running with clear boundaries, documented handoff points, and monitoring your team can manage.
I am not trying to sell you a better chatbot than the big AI labs — they will always win that race. What they cannot do is sit down with your team in Vancouver, learn how your business actually runs, and wire their models into it so the work gets done. That part is local, and that part is what I do.
Discovery goes faster in a room. I can sit with your team for the messy parts — mapping workflows, tracing data, agreeing on what an agent is allowed to touch.
Pacific time means questions get answered the same working day, and a broken pipeline on Tuesday morning is a Tuesday morning problem — not an overnight ticket.
One senior person owns your system end to end. No handoff between a sales team, a strategy team, and an offshore build team.
DigitalMachine.ai is an independent Vancouver-based consultancy. I stay close to the work from discovery through deployment, so the system does not get lost between a sales team, a strategy team, and an implementation team.
I bring 20+ years of hands-on experience across software engineering, data infrastructure, and analytics — from BigQuery pipelines and dbt models to production agent systems.
The work can begin locally in Vancouver or remotely. Either way, the engagement is built around a concrete workflow, clear access boundaries, and a handoff your team can understand.
Discuss your workflowAudit your analytics stack -- GA4, BigQuery, Tableau, Power BI, Metabase, dbt models, raw SQL -- and organize it into something your team and your agents can actually use.
Fix broken events and deploy analytics agents that continuously surface gaps -- not a one-time audit that goes stale.
Deploy ingestion agents that pull Google Search Console and GA4 data, flag anomalies, and keep organic performance visible without manual exports.
Wire and deploy MCP-connected agents with the right tool access, context boundaries, and handoff logic for your operations.
Replace brittle copy-paste workflows with browser agents and scheduled task runners that execute without supervision.
Clear boundaries matter more than AI hype. Here is how a practical engagement starts and how the resulting system stays understandable.
Choose one recurring workflow that matters, has a clear owner, and currently depends on manual data gathering or handoffs between tools. Analytics briefs, reporting pipelines, research tasks, and browser-based operations are strong starting points.
Usually, no. The goal is to connect the systems your team already trusts — such as GA4, Search Console, BigQuery, ClickHouse, dbt, internal APIs, and browser workflows — then give agents carefully scoped access to them.
Systems are designed with explicit permissions, bounded agent handoffs, workload limits, human review points, and a visible activity trail. The right level of autonomy depends on the risk of the workflow.
Yes. The engagement includes an understandable operating structure, documentation, and handoff. The aim is a system your team can monitor and own, not a black box that creates permanent vendor dependence.
No. DigitalMachine.ai is based in Vancouver and can work locally or remotely. The delivery approach is the same: start with a concrete workflow, confirm access and success criteria, then deploy in controlled stages.
Tell me what is repetitive, unreliable, or trapped between tools. I'll tell you whether it is a good fit for an agent system and what a sensible first step looks like.
Vancouver or remote · No obligation · A plain-language brief is enough
