Integrating Toast POS with Ledger1 (Varuni Edition)

Integrating your POS is the single highest-leverage move you can make in a restaurant ERP. It unlocks accurate daily sales, cash variance, menu performance, labor cost truth, and predictive ordering—without manual spreadsheets.

Why POS integration matters with Agentic AI

Agentic AI needs a trustworthy, timely stream of operational data so it can detect issues, reason over context, and act.

• Ground truth: POS is the source for sales, tenders, discounts, voids, taxes, tips, and service charges.
• Autonomous triggers: Agents can watch for anomalies (e.g., negative margin items, cash drawer variance, duplicate voids).
• Action surfaces: When an issue is found, the agent can open a ticket, message a manager, or draft a policy change.
• Prediction and planning: With clean data, the agent can forecast inventory needs and labor requirements per daypart.
• Closed-loop learning: Post-action outcomes (e.g., waste reduction) feed back to the model for continuous improvement.

Architecture overview

• Ingestion: Webhooks for near-real-time events; periodic polling for reconciliation.
• Backfill: A job fetches historical checks and items to build a consistent baseline.
• Normalization: Map Toast objects to Ledger1 models (checks, menu items, categories, tenders).
• Storage: Persist snapshots with idempotency keys to avoid duplicates.
• Observability: Structured logs and metrics for throughput, latency, and failure reasons.

Step-by-step integration (Varuni demo)

• Secrets: Add Toast API credentials to the environment store (client id/secret, refresh token, base URL).
• Connectivity test: Run the connection script to validate auth and list a few locations.
• Webhooks: Register event subscriptions for checks/line items; verify signatures on receipt.
• Backfill: Execute the backfill task to hydrate at least 90 days for reporting and baselines.
• Reconciliation: Compare daily POS sales vs. deposits, tax breakdowns, and tender totals.
• Monitoring: Alert on repeated 4xx/5xx errors, webhook gaps, or sudden volume drops.
• Incident playbook: Auto-open a task with context and suggested remediation steps.

Data model mapping

• Check → Sale with per-item detail, discounts, modifiers, and service charges.
• Menu item → Catalog item with cost and category (pizza, salad, beverage).
• Tender → Payment method (cash, card, gift) with fees when applicable.
• Employee → Labor reference for clock-in/out (if enabled) and comping authority.
• Location → Organization unit with tax jurisdiction and daypart configuration.

Common pitfalls

• Timezones: Normalize to the store timezone for EOD rolls; store UTC for analytics.
• Idempotency: Use Toast ids + event type as a uniqueness constraint.
• Partial data: Late-arriving events happen—re-run reconciliation per EOD until closed.
• Versioning: Be explicit about Toast API version; guard unknown fields.

Success metrics

• <1% unreconciled sales by day.
• <5 minutes ingestion-to-dashboard latency.
• Alert MTTD <10 minutes; false positives <5%.
• Ticket auto-triage accuracy >80%.

What you get on day one

Daily sales truth, reliable item-level margins, anomaly detection, and an Agentic AI that not only explains what went wrong but creates the task, routes it to the right role, and follows up until it is resolved.