Recommendation engines, pricing AI, and customer support agents routinely access purchase history, payment data, and PII. AutoPIL enforces what each agent can see — and logs every access decision against the policy that allowed it.
10 policies across merchandising, customer experience, and loss prevention — governing the data boundaries between personalization, pricing, and shrinkage detection in omnichannel retail environments.
policies: # Pricing analyst — cost and market data; blocked from individual customer profiles and payment records - name: pricing_analyst_policy agent_role: pricing_analyst allowed_sources: - cost_data - competitor_pricing - market_benchmarks - margin_reports denied_sources: - individual_customer_profiles - payment_records - legal_contracts allowed_tasks: - price_recommendation - margin_analysis - competitive_benchmarking denied_tasks: - customer_targeting - payment_processing max_sensitivity: medium
Point policy_path at your industry directory. AutoPIL loads every
YAML file recursively — roles, sensitivity rules, and process groups wired up
automatically from the directory structure. No additional config needed.
Policies are hot-reloaded at runtime. Extend or override any pre-built policy via the REST API without redeploying your agents or restarting services.
from autopil import ContextGuard # Point at your industry — only those policies load guard = ContextGuard( policy_path="policies/retail/", audit_db="autopil.db", ) # policies/retail/ — loads recursively # Switch verticals by changing the path — nothing else changes.
Pre-built policies for retail are included in every AutoPIL trial. Extend or override any rule via the REST API without redeploying.