Architecture review and rescue
An independent read on a build that is slipping or heading somewhere expensive. The output is a ranked assessment of what is actually wrong, with effort and risk against each item.
Salesforce Technical Architecture
Salesforce architecture, and AI applied where it actually helps.
I work on Salesforce programmes that have grown complicated: integrations that need redesigning, releases that have slowed down, data models that no longer fit. Sixteen years building enterprise systems, ten of them on Salesforce, across banking, insurance, telecoms, public sector, and maritime.
Selected engagements
Architecture and delivery for teams at
- CaixaBank
- DNB
- Hurtigruten
- BT
- Wefox
- Crèdit Andorrà
- Arbetsförmedlingen
- Wilhelmsen
- Unifon
- IBM
- Salesforce
Engagements delivered directly and through consultancy partners. References available on request.
Where programmes get stuck
The hard part is rarely the feature.
It is usually the parts nobody owns. Integrations built for a pilot and never revisited. Automation layered on automation until no one can say what a save actually does. A data model that suited one country and now blocks four.
This tends to surface as a delivery problem: releases slip, defects reopen, the team is busy and shipping little. The cause is normally architectural, and it does not resolve by adding people.
Find the constraint, remove it, and leave the team able to keep going without me.
What I do
Five kinds of work.
Integration design
Point to point sprawl, batch jobs nobody owns, and sync patterns that will not survive the next volume step. Rebuilt around explicit contracts, idempotency, replay, and failure you can see.
Data Cloud and customer data
Ingestion, identity resolution, and activation designed for production rather than for a demo. Consumption cost and GDPR treated as design constraints, because retrofitting either is expensive.
DevOps and release automation
Environment strategy, branching, and CI/CD that a multi vendor team can actually follow. Gearset, Copado, and AutoRabbit, with AI used to shorten review and catch regressions earlier.
AI in delivery, and Agentforce where it fits
Two different things. Agentforce for customer facing automation when the use case suits it. General purpose models inside the delivery pipeline, which is where most of the measurable gain sits today.
AI
Useful AI is mostly unglamorous.
Specification review, test generation, metadata diffing, release notes, pull request analysis. Work that is repetitive, well bounded, and cheap to verify. That is where models are dependable now, and it compounds quietly.
Track record
By the numbers
16
Years building enterprise systems
10
Years on the Salesforce platform
9
Countries delivered in
Get in touch
Tell me what has stopped moving.
A short description of where the programme is stuck is enough to start. If I am not the right person for it, I will say so.