The problem
The team ran a typical ops setup on Salesforce: lead routing, a pipeline view, weekly reports, and a handful of custom fields held together with formula cells. They were paying for seats they didn't need, waiting weeks for small changes, and re-exporting to spreadsheets for reports no admin could configure cleanly.
The bigger obstacle was getting off Salesforce at all. Their data lived in Salesforce's proprietary object model — not a regular database — so the usual “point an ETL at the DB” approach wasn't available. Every off-the-shelf tool we looked at either covered the standard objects only, or charged per-row for data they already owned.
Phase 1 — Days 1–10 · Build the migration tool
Step zero: build something that could actually pull the data out. The PM described the Salesforce objects they cared about (leads, accounts, opportunities, activities, three custom objects, four custom fields per) as a requirement. The Requirements agent produced the spec. The Schema agent inferred a target relational model. The Integration agent wrapped Salesforce's SOAP + REST APIs into a typed client.
The Pages agent built a small browser tool for ops to drive the extraction: object picker, incremental sync, retry on rate limit, validation report. Ten days start to first end-to-end run. Salesforce's API rate limits dominated the timeline more than anything generated.
Phase 2 — Days 11–30 · Migrate the data
The tool they just built ran nightly against production Salesforce, incrementally dumping into a staging Postgres instance. Ops ran validation queries; engineering patched three data quirks (dates in two formats, a nullable foreign key to a deleted object, one field the admin had been using as free-text). Three weeks, mostly waiting on data — not engineering.
Phase 2 (in parallel) — Days 15–35 · Build the replacement app
Once the target schema was stable, the team used the same AlgorithmShift graph to build the replacement app:
- Design agent — calm, neutral theme matching their brand.
- Schema agent — 14 tables, foreign keys, indexes. No hand-authored migrations.
- Pages agent — six core screens + two admin views, generated in parallel once the schema was stable.
- Integration agent — hooked into their existing HubSpot, Segment, and internal APIs.
- Tests + Security agents — coverage + an OWASP pass on every generated route before release.
Three rounds of revisions, all scoped to individual pages. Approvals flowed through Review, tracked by artifact hash.
Phase 3 — Days 36–45 · Cutover + training
Final migration run over a weekend. Schema bundle exported via export-only mode, applied through their Liquibase pipeline — zero changes to their CI. Ops training on Monday, staff using the new tool by Tuesday, Salesforce contracts wound down by Friday.
The quote
“We got off Salesforce in 45 days. Ten of those were a custom migration tool we had to build because there's no clean way to get data out of a Salesforce proprietary DB. The thing that made it possible wasn't that it was fast — it was that we built the migration tool andthe replacement app on the same platform, so everything our ops team learned carried forward.”
What made it work
- The same graph built both tools. The migration extractor and the replacement app share the schema, the design tokens, the auth model. Not two separate projects with two separate maintenance stories.
- Integration agent handled Salesforce's APIs.SOAP + REST, rate limits, incremental sync — typed and audited. No hand-rolled client; secrets aliased in the tools manager.
- Migrations they could ship their way. Dev ran managed. Staging + prod used export_only — SQL bundle in their Liquibase pipeline. Zero changes to their existing CI.
- Every feature traces to intent. The VP of Ops can click any screen and see the requirement + approval behind it.