Integrations that don’t break every time something upstream changes.

Salesforce that doesn’t talk cleanly to your ERP, billing system, or data warehouse creates duplicate work and duplicate truth. I build integrations that handle failure gracefully and migrations that move your data without losing its history.

The problem

Common problems

Point-to-point integrations built without an error-handling strategy tend to fail silently — a record doesn’t sync, nobody notices for three weeks, and now two systems disagree about what’s true. Data migrations done under deadline pressure skip validation and reconciliation, and six months later someone finds duplicate accounts or orphaned records nobody can explain.

The underlying issue is usually the same: integration and migration get treated as a data-transport problem instead of a data-integrity problem. Moving records is the easy part. Deciding what happens when a sync fails, a field doesn’t map cleanly, or two systems have different versions of the same record — that’s the actual engineering.

"We don’t find out an integration failed until someone complains."

"We migrated the data, but we don’t fully trust it."

"Every new integration is built from scratch instead of on a pattern."

Business impact

Silent integration failures cost sales and support teams working hours to manually reconcile every week they go unnoticed, and a botched migration can poison trust in Salesforce data for years — teams route around bad data rather than fix its source, which defeats the point of having one system of record.

The approach

How I solve it

1

Integration audit / migration assessment

Map current data flows and quality issues before writing a line of integration code.

2

Architecture selection

Choose REST, SOAP, Platform Events, or middleware (MuleSoft) based on volume, latency, and system constraints — not on what’s trendy.

3

Build with error handling first

Retry logic, dead-letter handling, and monitoring are designed in from the start, not bolted on after the first failure.

4

Migration: extract, transform, validate, load

Deduplication and validation rules run before any production load, with a tested rollback plan.

Typical engagement: 3–6 weeks for a single integration; 6–10 weeks for a full migration, depending on data volume and source system complexity.

What changes

Outcomes from this work

Zero data loss

migration

reconciliation-verified against source

  • reduction in manual reconciliation work
  • integration uptime / error-rate
  • week typical delivery window

Outcomes are drawn from anonymized client engagements; specific figures are shared on a consultation call.

Fit

This is the right engagement if…

  • You run Salesforce alongside an ERP, billing platform, or data warehouse that currently syncs manually or through a fragile point-to-point script.
  • You’re migrating off a legacy CRM and want a rollback plan and a reconciliation step, not just a bulk import.

…and probably not if

  • You need same-day delivery on a migration involving more than a few thousand records — proper validation takes real time.
  • You’re not willing to involve the owner of the source system in discovery — integration quality depends on understanding both sides.
  • Your integration need is a single one-off CSV import — that’s a much smaller engagement than this service is scoped for; ask on the call.
Questions

Before you book a call

How long does an integration or migration typically take?
A single integration usually runs 3–6 weeks; a full data migration runs 6–10 weeks depending on volume and source complexity.
What’s the starting investment?
Scope and price are fixed in a written proposal once requirements and data volume are known.
Do you handle real-time integrations, not just batch?
Yes — REST, SOAP, and Platform Events are all in scope, chosen based on the latency your use case actually needs.
Can you work with our existing middleware (MuleSoft, Boomi, etc.)?
Yes, including auditing and improving an existing integration rather than replacing it outright.
What’s your communication cadence?
Written scope up front, async updates through the build, and a joint reconciliation review before any production cutover.
What happens if the migration reveals bad data in the source system?
It gets flagged and resolved before load, not migrated as-is. A migration that faithfully copies bad data still leaves you with bad data.

Ready to stop reconciling data by hand?

Tell me what’s connected to what, and what’s breaking. I’ll tell you what it takes to fix it properly.

Schedule consultation

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