Edmonton, Alberta — and remote across Canada

I build the integrations and platforms that have to survive production.

Ten years of enterprise Salesforce and ERP integration, high-throughput distributed systems, and full-stack SaaS delivery. QueueOne is one person on purpose: you get the architect who writes the code, not a bench of juniors and a status deck.

The first thing I ask is not what your stack is. It is what breaks when this does not work, and who gets the phone call.

Tell me what is breaking Or read what I have shipped

1,000req/sec: contractual SLA on a 25+ API micro-service architecture, met in production. As an employee of a consultancy, for a major Canadian airline
3platforms: taken to live use — one I co-founded, one a client's business was built on, one launched with its first investors
10years: across enterprise integration, distributed systems and full-stack SaaS delivery
201kimpressions / mo: organic search on my own product in July 2026, from 85 pages earning their first impressions. The same discipline this site is built on

What I do

Four things, in the order I am usually called for them.

Integration & data migration

The promise is not that I will connect your systems. It is that your integration survives a replay, a spike, and an outage.

Salesforce, ERP and legacy systems wired correctly: event-driven, idempotent, reconcilable. Most integrations work on the happy path and quietly corrupt data on the second delivery of the same event. That is the part I build for.

Salesforce Platform Events & CDC
SAP HANA B1 over OData
MuleSoft Anypoint, DataWeave, API Manager
Azure Service Bus back-pressure
DLQs, retry scopes, reconciliation & backfill
Salesforce ↔ PeopleSoft bi-directional sync

Platform builds, zero to production

Built and shipped, not prototyped — auth, billing, tenancy, queues and observability included.

Multi-tenant SaaS taken the whole distance. Three platforms of mine have reached live use — a consumer product three years in market, a client platform an Edmonton business was founded on, and an investor platform that launched with its first cohort. A fourth, KeystoneOS, is in private beta.

Multi-tenant architecture
Stripe billing
WorkOS auth
Postgres
Queues and background execution
Observability from day one

AI systems that do real work

Sold on the auditing, cost control, idempotency and evaluation — the parts that decide whether it survives contact with production.

Retrieval running in production and agents built with the audit trail attached — not a prompt in a wrapper. RAG-based search serves a live consumer product, and the first-party multi-agent framework, with scheduled execution and an audit trail over every run, is in beta alongside the platform it was built for.

Multi-agent orchestration with scheduled execution
Audit trails over agent runs
pgvector semantic search
Vector recommenders
Evaluation and cost control

Technical due diligence & rescue

A fixed-scope audit of a stalled or inherited system, delivered as a prioritized roadmap you can act on.

Short, fast to close, and the cheapest way to find out what you actually have. One audit of a stalled system — codebase, infrastructure and functional gaps — produced a prioritized roadmap the client acted on and resumed development against. This is also where a fractional CTO engagement model fits, if that is the shape you need.

Codebase and infrastructure audit
Functional gap analysis
Prioritized remediation roadmap
Legacy modernization planning

Selected work

Systems that are still running.

Some clients I can name and some I cannot, and some of this I built as an employee rather than as QueueOne — so each one says which. Where a client is withheld, the sector and the stack are not. Read the case studies.

KeystoneOS

My own product — in private beta

A multi-tenant operating system for real-estate teams, where the hard part is ingesting other people's events without corrupting your own data.

Idempotent webhook ingestion keyed on provider event IDs, anonymous-to-identified event backfill, CREA RESO OData sync, and pgvector semantic search over listings.

Postgres · pgvector · Stripe · WorkOS · Twilio · Nylas · SendGrid · PostHog

A major Canadian airline

As an employee of a consultancy, on the client's programme

A 25+ API micro-service architecture carrying a 1,000 req/sec SLA, in front of reservation systems that do not forgive sloppy integration.

Booking, seat maps, flight search and automated baggage handling, integrated against airline reservation systems under a contractual throughput SLA.

Amadeus Altea · Sabre · Azure Service Bus · Micro-services · DLQs and retry scopes

Surefire Fundraising

As QueueOne — built from nothing, still maintained

The business did not exist before the platform went live. That is the whole brief: it had to work from the first day, because there was no manual process to fall back to.

A Laravel platform handling catalog, checkout and transactional email for an Edmonton client whose business was built on top of it.

Laravel · Stripe · Mailgun · PHP

What's The Deal

As co-founder — I built the platform

Three years live on both app stores, with geofenced push in the hands of real people, RAG search behind it, and organic traffic that arrived on purpose.

A Flutter app on the App Store and Google Play using location, geofencing, maps and push, with RAG-based search and a vector recommender behind it.

Flutter · Next.js · Google Cloud Pub/Sub · RAG search · Vector recommenders · Geofencing · Push notifications

How I work

The delivery model is AI-native, and I will show you the machinery.

Every consultancy now says it uses AI. Very few will show you what that actually means in their repository. Mine ships with the operating instructions committed alongside the code: a project brief the agents read before they write anything, domain skills that encode the rules a new contributor would otherwise learn by breaking something, and architecture diagrams checked against the source by a script rather than trusted to stay accurate.

The point is not speed for its own sake. It is that the context lives in the repository instead of in one person's head — which is exactly the property you want from a solo practice you are trusting with a production system.

The other half is not in any repository. I have built three products of my own, which means I have made the decision you are about to make with my own money on the other side of it. That tends to change which questions get asked first. Here is how I work.

Tell me what is breaking.

An integration that loses events, a platform that stalled before launch, an inherited codebase nobody wants to touch, or something that has to be built from nothing. Describe it in a paragraph and I will tell you whether I am the right person for it.

Start a conversation hello@queueone.io