Short version: one mid-market operations team ran on Salesforce. Migrating off took 45 days end-to-end. Ten of those days were spent building a custom migration tool on AlgorithmShift — because Salesforce stores data in a proprietary object model, no off-the-shelf ETL covered the custom objects the team actually cared about.
The replacement app costs 1/8 of the original licensing, eliminated ~22 hours / week of manual work, and runs entirely on their own stack.
Long version below. Names are withheld per the customer's preference; we can make introductions on request.
The situation
- Team of ~40 people across ops, sales ops, and customer success.
- Running a typical Salesforce setup: lead routing rules, pipeline views, weekly reports, custom fields held together with formula cells.
- Pain points:
- Changes took 2–3 weeks of admin work to roll out.
- Reports needed spreadsheet export → Excel → PDF.
- Half the seats were effectively read-only viewers paying a full license.
- Every custom dashboard was a brittle plugin.
Why it took 45 days and not 10
The generative part of this is fast. What dominates a real Salesforce migration is getting the data out cleanly.
Salesforce stores data in a proprietary object model — not a regular database — exposed through SOAP + REST APIs with rate limits, eventual-consistency quirks, and custom-object schemas that vary tenant-to-tenant. Every off-the-shelf ETL we evaluated either covered the standard objects only, or charged per-row for data the customer already owned. So step zero was building our own extractor.
Phase 1 — Days 1–10 · The migration tool
The PM described the Salesforce objects they cared about as a requirement: leads, accounts, opportunities, activities, three custom objects, four custom fields each. The Requirements agent produced the spec. From there:
- Schema agent inferred a target relational model — 14 tables plus an audit-log table they hadn't explicitly asked for but clearly needed.
- Integration agent wrapped Salesforce's SOAP + REST APIs into a typed client with aliased credentials. Rate-limit handling, batch cursoring, incremental sync, retries on 429s.
- Pages agent built a small browser tool for ops to drive the extraction: object picker, sync-progress bar, failure report, diff-against-last-run.
- Tests + Security ran on every route before we let anyone use it internally.
Ten days from first prompt to first successful end-to-end extraction run. Salesforce's API rate limits dominated the wall-clock more than anything the agents generated.
Phase 2 — Days 11–30 · Migrate the data
The tool we just built ran nightly against production Salesforce, incrementally dumping into a staging Postgres instance. Ops ran validation queries; engineering patched three data quirks:
- Dates stored in two formats across objects (
YYYY-MM-DDandMM/DD/YYYYon the same field, depending on who created the record). - A nullable foreign key pointing to a deleted object (
account_idreferencing accounts archived 2+ years ago). - One custom field the admin had repurposed as free-text notes, which needed structured parsing before it landed in the new schema.
Three weeks, mostly waiting on data — not engineering. The extractor was incremental, so we re-ran every night without paying the full extraction cost each time.
Phase 2 (in parallel) — Days 15–35 · Build the replacement app
Once the target schema was stable, the same AlgorithmShift graph built the replacement app. Six screens (inbound leads, pipeline, weekly report, customer detail, admin settings, activity timeline), 14 tables, 2 additional integrations (their email + calendar systems), role-based access tied to their existing Okta.
Design + Schema ran in parallel on day 15. Pages agent produced the six core screens in parallel; two rounds of scoped revisions:
- "Make the bulk-select action bar sticky when scrolled."
- "Move the filter chips above the table, not inside it."
Both landed as targeted patches, not whole-page regenerations. Tests + Security ran on every route before the first internal demo on day 28.
Phase 3 — Days 36–45 · Cutover + training
- Day 36–40: Final full migration run against production Salesforce over a weekend. Validation queries confirmed parity.
- Day 41: Schema bundle exported via export-only mode, applied through their Liquibase pipeline. Platform recorded apply state per environment — their team never left their existing CI.
- Day 42: Cutover at 10am. Ops staff trained over lunch; ~6 questions in the afternoon, none of them platform issues.
- Day 43–45: Monitoring, residual bugs (two — both data, not platform), contract wind-down with Salesforce.
What we measured after
| Metric | Before | After | Delta |
|---|---|---|---|
| Licensing cost | $X / month | $X / 8 | 88% reduction |
| Hours / week on manual exports | ~22 | ~1 | −21 hrs / wk |
| Time to ship a small change | 2–3 weeks | Same day | — |
| Team members on read-only viewer licenses | 18 | 0 | everyone has full access |
Why it worked
What we'd do differently
- Start data-quality audits on day 1, not day 11. The three data quirks we hit in phase 2 would have surfaced earlier if we'd sampled a few hundred rows before building the full extractor. Two days saved on retry.
- Parallelise phase 2 sooner. We had the target schema stable by day 14; we waited until day 15 to start the replacement app. Nothing was blocking us — team habit of sequential work kicked in.
- Run security agent against the migration tool earlier. We caught one issue (a debug log that contained raw SF session tokens) only in phase 3. Not catastrophic — dev-only — but worth catching earlier.
What's still Salesforce-like they didn't replace
They kept Salesforce for two things:
- Partner-facing views that sync with partner Slack channels. Those integrations were deep and expensive to rebuild.
- Historical reporting for the first 18 months. Frozen instance, read-only, kept for audit.
Roughly 15% of their Salesforce footprint. The other 85% is now an AlgorithmShift app.
The ask
If you're the ops leader at a team that's outgrown what Salesforce gives you for the money, book a walkthrough. We'll show the platform with your actual workflow in mind. If it's obviously wrong for your case, we'll say so — we're not trying to be a Salesforce replacement for everyone.