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MyLawn Brain

In Build · Function: Consumer / Digital · Where it runs: Cloud Run + Cloud SQL

The first initiative to consolidate Scotts's 150 years of lawn-care expertise into one auditable, always-consistent intelligence layer that every consumer channel can call.

Problem

Scotts has the deepest lawn care expertise in the industry — codified in product labels (the regulatory law for when, how, and where each product can be applied), knowledge articles, training materials, and decades of R&D guidance. The material is not missing — it's scattered. PDFs, spreadsheets, internal docs, no single machine-readable form.

As a result, different consumer channels can give different advice. The app says one thing, the website another, customer service a third. No single structured source is feeding every channel.

What it does

  • Extracts structured data from every active product label and supporting article using agentic AI — approved grass types, prohibited grasses, listed weeds, diseases, pests, application rates, temperature constraints, seeding restrictions, frequency limits.
  • Structures the legacy: 26 grass types, 152 weed species, 37 diseases, 31 pests, 14 regions, 11 seasons. 100+ products organized into 48 functional subcategories by the job they do (not just the shelf they sit on).
  • Reasons: 126 rules evaluate against a 48-field user state vector — grass type, region, photo analysis (weeds, disease, bare spots), weather (GDD, soil temp, humidity), and full action history. 14 hard safety rules + 10 soft advisory rules, codified straight from the label.
  • Serves every consumer touchpoint with the same recommendation: MyLawn App, the website, Sierra Chat, CRM/email — one brain, many channels.

Every recommendation is traceable — you can see exactly which rule fired and why.

Who owns it

  • Business owner: Consumer Digital / I&TC
  • CoE AI Solution Lead: [TBD]
  • Tech stack: Python rule engine + Cloud SQL Postgres + Cloud Run, agentic AI extraction pipeline, React admin UI (brain-admin)

Where it runs

  • Backend / rule engine: brain-services — Cloud Run, Cloud SQL (Postgres), private behind a Cloud Function BFF
  • Admin UI: brain-admin — Vite + React, for managing the rule corpus and previewing recommendations
  • GCP project: mkt-dev-0-0

Data it touches

Data source Classification Access path
Product labels (PDF + structured) Public + Regulated Agentic extraction pipeline → structured Postgres
Knowledge articles, training material Internal One-time ETL into structured form
Active product catalog Internal Synced from source systems
User profile (grass type, region, action history) Consumer PII Read via authenticated API, no PII in prompts
Weather (GDD, soil temp, humidity) Public External API

Governance status

  • AI Governance Committee reviewed: Pending formal review — extraction pipeline and rule engine treated as agentic AI under FY27 governance pattern
  • Human-in-the-loop required for: Rule changes promoted to production (validated via simulation against historical journeys)
  • Vendor / model risk noted: Yes — agentic extraction uses third-party model; outputs reviewed before promoting to rule corpus

Outputs

  • Products — tailored recommendations from 100+ SKUs across 10 categories
  • Actions — engagement tracking; what the consumer did, when, what to do next
  • Lawn Score — lawn-condition assessment from photo analysis + history
  • Guardrails — 14 hard constraints (e.g. "never seed within 16 weeks of non-seed-safe pre-emergent") + 10 soft advisories prevent bad advice across every channel
  • Alerts — proactive outreach based on GDD, weather, and timing windows
  • Simulation — validate rule changes before promoting to production

Patterns reused

  • Agentic extraction → structured-data pattern is reusable for other label-and-document corpora (e.g. safety data sheets, OG&L technical bulletins)
  • Rule-engine + state-vector evaluation pattern is reusable for any recommendation surface that needs traceability and guardrails

What's next

  • Expanding rule coverage to lime/sulfur, aeration, dethatching
  • Weed-specific product matching (photo identifies dandelion → recommend products that list dandelion on the label)
  • Multi-year journey simulation to validate recommendation quality over time
  • Production rollout across all four consumer channels (app, web, Sierra, CRM)

Changelog

  • 2026-05-21 — Initial entry on the hub.