AI proof
AI intelligence grounded in decision-service evidence.
SavorQ connects orders, SavorQ Voice review, routing decisions, outcomes, labels, station snapshots, costs, fees, kitchen timing, refunds, payments, and reports into an intelligence layer that remains operator-reviewed.
Captured demandKitchen handoffMargin review
Reviewed insight layer
OrdersDemand and mixSavorQ VoiceCall, transcript, and review signalMenu COGSCost baselineChannel feesContribution signalKDS timingExecution signalOperator reviewCorrection loopProof model
Restaurant intelligence is only credible when claims map to inspectable evidence.
SavorQ does not treat AI as a disconnected chat layer. The current substrate records decisions, outcomes, labels, snapshots, model registry entries, and experiment scaffolding before Wave 3 adaptive learning is claimed.
Review workflow
AI-assisted insight stays explainable, reviewed, and tied to action.
Identify margin, timing, refund, voice-review, and channel exceptions.
Show the operational signals behind the recommendation.
Queue insight and phone-order corrections for manager or owner evaluation.
Apply operational changes through existing controls.
Claim tiers
AI claims are split into live substrate, Wave 2 readiness, and roadmap.
Decisions, outcomes, training rows, snapshots, registry records, and experiment scaffolding exist today. Fine-tune readiness depends on labeled examples. Real adaptive learning remains a Wave 3 pilot-data claim.
Live substrate
Routing decisions, outcome events, training rows, station snapshots, model registry records, and experiment scaffolding can be evidenced from the decision service.
Wave 2 ready
Fine-tune preparation becomes credible when enough labeled success and failure examples exist for a tenant and store.
Roadmap
Closed-loop adaptive learning remains gated on pilot data, active model deployments, experiment results, and operator review.
AI profit orchestration suite
AI capabilities designed around reviewed restaurant profit action.
SavorQ does not claim to replace operators or ship autonomous closed-loop ML today. It gives operators a reviewed intelligence loop that connects demand, voice, kitchen execution, margin, inventory, refunds, and multi-store performance.
AI profit leak detection
Detect margin leakage across orders, channels, modifiers, refunds, delivery fees, and COGS so managers know what deserves review.
SavorQ Voice intelligence
Capture phone demand with transcript review, menu-aware parsing, allergen flags, modifier matching, and manager correction before kitchen handoff.
Channel profitability AI
Compare POS, owned online, marketplace, and phone demand by contribution, refund pressure, fee profile, prep burden, and COGS context.
Menu margin optimizer
Recommend price, modifier, bundle, and item reviews using order mix, food cost, refunds, and contribution signal.
Kitchen load prediction
Predict service pressure and station bottlenecks from KDS timing, channel mix, order size, menu complexity, and rush patterns.
Refund and exception intelligence
Detect repeat refund causes, missing-item patterns, late-order risk, marketplace disputes, and operational exceptions for review.
AI operations copilot
Help managers ask why margin moved, which channel hurt contribution, and what should be reviewed before the next rush.
Multi-store benchmarking AI
Compare locations by channel performance, prep time, refund rate, menu margin, phone-order conversion, and review patterns.
Inventory and COGS intelligence
Connect menu demand to stock pressure, waste risk, ingredient cost drift, and purchasing alerts that operators can review.
Reviewed action queue
Turn AI findings into explainable recommendations for owner or manager approval instead of silent operational changes.
Reviewed insight
Decision support for margin leaks, service drag, voice demand, and exceptions.
Margin exception review
Surface fee drift, modifier pricing issues, and contribution changes for operator review.
Kitchen timing signals
Use KDS outcomes to highlight prep bottlenecks and service patterns.
Channel health review
Compare order mix, refunds, and economics across owned and third-party channels.
Operational context for AI
Keep insight grounded in order, voice review, cost, kitchen, refund, and reporting data.
Voice-order review
Use SavorQ Voice signals to review transcripts, parsed orders, modifier choices, corrections, allergen context, and status handoff.
Demo