ERP + POS + CRM, unified into a single bidirectional sync layer. iSimplifyMe-built. iSimplifyMe-operated. In production today.
What is a multi-system retail integration?
A multi-system retail integration unifies a retail or distribution operation’s three core systems — an ERP (inventory and customer master), a Square POS (transaction capture), and a HubSpot CRM (sales and marketing) — into a single bidirectional sync layer with daily reconciliation, vendor-API spec-drift handling, and idempotent upserts. iSimplifyMe operates one in production today: roughly 12,000 products, 3,000 B2B accounts, and 900 daily deals flow across the three systems through an iSM-built Node and TypeScript sync layer running on three scheduled jobs. The next phase moves the daily sync from a local LaunchAgent to AWS Lambda + EventBridge with Layer 3 retrieval observability on top.
The setup
A regional B2B-and-retail distributor of high-SKU tangible goods. Two physical locations, contractor and architect and builder accounts plus retail walk-in, a 30,000-plus active item catalog, three vendor systems already in place — none of which talked to each other. Sales reps were re-typing every qualified lead from their CRM into their ERP to generate a quote: double entry, data drift across systems, dropped leads on busy days.
iSimplifyMe was engaged to unify the three systems without a rip-and-replace. The mandate: keep the existing tools, build the integration layer, operate it.
Architecture
Phase 1 runs in production today as three scheduled jobs against a Node and TypeScript sync layer. Phase 2 (ghosted) replaces the local LaunchAgent with AWS Lambda + EventBridge and layers Layer 3 retrieval observability on top.
Why this is harder than it looks
Vendor API spec drift
Documented field names, enum values, and date-filter semantics consistently lag the live API. iSimplifyMe uses a probe-first methodology: hit the live endpoint with a sample of at least 200 records, dump field distributions, treat the probe as authoritative — never the spec. Without it, a sync built against the published documentation produces silently-wrong stage mappings that look right in dev and break against real customer data.
Full-catalog timeouts
The vendor’s 30-second SQL ceiling kills any naïve full-customer sweep at page 50 of 142. The fix is to chunk by sales rep — or any partition that stays under 500 records per chunk — and serialize. Same dataset, no timeouts, completes in under 6 minutes.
Marketing-contact tier economics
HubSpot’s marketing-contact cap makes 25,000 retail walk-in syncs economically infeasible — the overage alone would run roughly $5,800/month. The decision: B2B-only sync. Retail data stays in the ERP as the system of record, where reporting already lives.
Bidirectional sync without webhooks
Until the vendor adds change webhooks, daily incremental polls with `modifiedSince` timestamps plus idempotent upserts keyed on the source-system ID close the loop. Each pipe auto-detects whether the vendor’s production URL is live and flips on its own when the cutover lands — no code change required.
What’s running in production today
| Pipe | Direction | Volume | Schedule |
|---|---|---|---|
| ERP products | → CRM | ~12,000 products | Daily 6:00 AM CT |
| ERP customers | → CRM | ~3,000 B2B accounts + ~1,800 linked contacts | Daily 6:30 AM CT (chunked) |
| POS orders | → CRM deals | ~900 deals | Daily 6:00 AM CT |
| ERP quotes | → CRM deals | ~26,000 records (current onboarding) | Daily 7:00 AM CT |
Three scheduled jobs running daily, comprehensive logging, alerting on failure, vendor spec drift handled at the mapper layer. iSimplifyMe-operated. Not handed off.
Build log
- Phase 0
Audit + scope (April 2026)
CRM audit complete. Object counts mapped: ~2,000 contacts, ~700 companies, ~880 deals, custom lifecycle stages and lead statuses, two pipelines. Custom property schema, vendor API token validation, pipeline structure documented.
- Phase 1a
Read pipelines live (April 2026)
POS-to-CRM order sync went live mid-April; ~880 historical orders backfilled. ERP customer sync went live a few days later, B2B-only, chunked by sales rep to bypass the 30-second SQL timeout — 23,500 of 28,300 records resolved per run (~83% B2B coverage). 17 custom product properties, 4 deal properties, 13 company properties, 10 contact properties created in the CRM. Three scheduled daily jobs running.
- Phase 1b
Quotes onboarding (May 2026, in progress)
ERP Quotes v2 sync code shipped May 1. ~26,000 staging quotes being backfilled into a new CRM pipeline. One deal per opportunity — multiple revisions roll up into a single deal carrying the latest revision’s data. 16 custom deal properties added. Auto-detects vendor staging vs production via probe and flips on its own when the vendor cutover lands. Daily incremental cron handles 12-15 modified quotes per day in seconds.
- Phase 2
Vendor write APIs + AWS infrastructure (planned)
Vendor write endpoints (POST customers, POST quotes) are the next ask still open with the ERP vendor. When they land, the CRM card scaffold — already built, already installed — goes live with quote creation directly inside the CRM. No more re-typing. Concurrently: migrate the three scheduled jobs from a local LaunchAgent to AWS Lambda + EventBridge cron, with Layer 3 retrieval observability layered on top. The AWS account is iSM-owned and operated.
- Phase 3
Automation (after Phase 2)
Webhook subscription on quote status change replaces polling. Convert-to-order endpoint closes the lifecycle loop. Automated CRM email sequences fire off quote events: 24-hour follow-up after quote sent, thank-you on accepted, win-back on declined, re-quote nudge on expired, post-purchase sequence on converted.
Built and operated, not delivered.
Most "AI for retail" engagements end with a slide deck and a hand-off to the client’s internal team.
This one is a running production system iSimplifyMe operates daily. Three scheduled jobs. Vendor API quirks tracked in our own probe scripts. ~26,000 records being onboarded right now. When Phase 2 lands, the AWS account it runs in will be ours too.
Frequently asked questions
How is this different from a typical AI consulting engagement?
Most AI consulting engagements end at a recommendation. This one started with an integration audit and shipped a production sync layer iSimplifyMe operates daily — three scheduled jobs, vendor spec drift handled at the mapper level, alerts wired to our on-call. We didn’t recommend the architecture; we built it and we run it.
Who operates the integration after it’s built?
iSimplifyMe. The scheduled jobs, the daily sync logs, the vendor API probes, the alert wiring, and (in Phase 2) the AWS Lambda + EventBridge infrastructure all run on iSM-owned accounts and iSM-monitored channels. The client doesn’t need an internal data engineering team to keep it running.
How does Layer 3 retrieval observability fit into a retail integration?
In Phase 2, every record flowing through the sync layer becomes addressable as a chunk-level retrieval target with metadata: source system, sync timestamp, customer tier, product category. That makes the integration queryable through the same Layer 3 retrieval interface that powers iSimplifyMe’s regulated-industry concierges — same substrate, same access pattern. Operations can ask "show me every B2B account with no quote activity in 30 days and a credit limit over $50K" and get a retrieval-shaped answer with citations to the underlying records.
What if the vendor’s API doesn’t support what you need?
It usually doesn’t, fully. Every vendor API has spec drift, partial coverage, or missing endpoints. We treat the spec as aspirational documentation and probe live APIs first; we file follow-up notes back to the vendor with reproducible evidence; we work around timeouts and rate limits at the application layer; and when an endpoint is genuinely missing, we run a phased build that starts with what’s already exposed and lights up the rest as the vendor ships it.
How long does Phase 1 take from kickoff?
Roughly 2-3 weeks for the CRM audit + first read pipelines (POS-to-CRM and ERP-to-CRM products + customers) running daily in production. Quote ingestion adds another 2 weeks once the vendor exposes the read endpoint. Phase 2 (AWS Lambda + Layer 3) is estimated at 2-3 additional weeks. Each phase is independently useful — if the vendor takes longer on a downstream endpoint, the upstream pipes still ship.
Discuss a multi-system integration
If you’re running an ERP + POS + CRM (or similar) and your team is hand-typing data between them, we can talk through what the integration layer looks like for your stack.
- Discovery call30 min · Free · No deck — actual mechanics
- Phase 1 timeline2–3 weeks to first pipes live in production
- iSM-operatedAWS account, monitoring, alerts — all on us