Restaurant OS — Decision Intelligence
SIGNATURE INTELLIGENCE LAYERAI that recommends.
You decide.
Restaurant operators are piled up with information, not knowing what to do. Operating data is only useful if it changes a decision. Achyrix proposes a specific action, shows the evidence, and brings every decision under one click.
The concept
What is Decision Intelligence?
Most restaurant analytics tools give you charts and leave you to figure out what to do. Decision Intelligence is different: it tells you what action to consider, shows you why, and waits for you to approve before anything happens.
A dashboard tells you what happened. Decision Intelligence tells you what to do about it — and explains why.
The distinction matters. Information without a recommended action creates analysis paralysis. An action without evidence creates distrust. Decision Intelligence pairs the two.
Input layer
Operational signals the system already captures.
Decision Intelligence doesn't require new data entry. It works from the operating signals your restaurant already generates through normal activity.
Sales & revenue
Transaction data by channel, time, location and item.
Order patterns
Volume, timing, item mix and modification frequency.
Kitchen timing
Prep duration, station load and throughput patterns.
Inventory signals
Stock movement against consumption where inventory tracking is active.
Labour patterns
Staffing levels correlated with service demand where tracked.
Operational events
Refunds, voids, order modifications and exception patterns.
Available signal types depend on which Restaurant OS modules are active in your deployment. We confirm data coverage honestly during a demo.
Architecture
From operating data to an approved decision.
The loop is deliberately visible. Where a system recommends action, the reasoning has to be inspectable.
Decision Intelligence
42 AI agents. You approve every action.
Every recommendation flows through a human-in-the-loop approval inbox. Review evidence, adjust parameters, and approve — nothing executes without your explicit sign-off.
Revenue & Pricing
- ✓Menu price optimization
- ✓Delivery yield management
- ✓Dynamic pricing
- ✓Margin tracking
- ✓Competitor benchmarking
- ✓Promotional yield analysis
Kitchen & Prep
- ✓Auto prep lists
- ✓Waste alerts
- ✓Recipe cost tracking
- ✓Supplier monitoring
Workforce & Schedule
- ✓Shift optimization
- ✓Labor forecasting
- ✓Peak staffing
- ✓Overtime compliance
Inventory & Purchasing
- ✓Auto-reorder triggers
- ✓Expiration alerts
- ✓Stock transfers
- ✓Invoice comparison
- ✓Sustainability tracking
Compliance & Security
- ✓Health code logging
- ✓Temperature monitoring
- ✓Allergen validation
- ✓Audit trails
- ✓Fraud detection
Autonomous specialists
Observing POS data, costs, and throughput 24/7
Swarm intelligence
How 42 agents decide together.
Instead of a single AI making messy assumptions, Achyrix uses 42 specialized agents that operate simultaneously. They act as a team, reading the same data and negotiating conflicts before proposing an action.
The Conflict-Resolution Stack
When agents disagree (e.g., the Marketing Agent wants to push a promotion, but the Inventory Agent predicts a stockout), they use a deterministic rule stack to resolve it. The operator only sees the safe, final decision.
- 1
Safety & Compliance
Overrides all other priorities. Health and safety always wins.
- 2
Fraud Detection
Holds any suspicious financial or inventory actions until manual review.
- 3
Stockout Prevention
Down-ranks promotional expansion if ingredient supply is critically low.
- 4
Labor Law & Coverage
Gates payroll optimization to ensure minimum staffing limits are met.
- 5
Cash Preservation
Can down-rank aggressive growth actions during periods of tight cash flow.
Evidence, not intuition
Every recommendation
should explain why.
A recommendation without visible reasoning is just an opinion with better formatting. Achyrix attaches the data that produced each suggestion so the person approving can evaluate it properly.
A specific, actionable operational suggestion.
The data pattern, comparison, or trend that generated this recommendation — shown alongside it.
The expected operational effect, where quantifiable from historical data.
Human control
Automation without surrendering judgment.
Achyrix does not auto-execute recommendations. Every consequential action waits for explicit human approval. The person operating the restaurant stays in control.
Approve
Accept the recommendation. The action is recorded with who approved it.
Reject
Decline the recommendation. The reasoning stays on record for learning.
Review later
Defer the decision. The recommendation returns at a scheduled time.
Audit trail
Every recommendation, decision and outcome is traceable.
Differentiation
What makes it decision intelligence.
Grounded in your data
Recommendations come from your own operating history, not a generic benchmark.
Specific actions
A proposal you can accept or reject, rather than a chart to interpret.
Evidence attached
The figures behind the recommendation are shown with it.
Human approval
Nothing consequential executes without an operator saying yes.
Traceable
What was recommended, and who decided, stays on the record.
Operationally framed
Written in terms of the shift, the menu and the stock — not model outputs.
Questions
Frequently asked about Decision Intelligence.
- Specific recommendation types depend on the data your operation captures and which Restaurant OS modules are active. We walk through this honestly during a demo rather than promising categories in advance.
- No. Every consequential recommendation waits for explicit human approval. The operator reviews the evidence, sees why the system thinks what it thinks, and then approves or rejects.
- It works from the operating data already captured by your active Restaurant OS modules — sales, orders, kitchen timing, and other signals. No separate data entry or import is required.
- Yes. Every recommendation is presented with the evidence and reasoning behind it. If you cannot see why the system suggested something, we have failed at the design.
- The rejection is recorded along with the reasoning. Over time, this feedback improves future recommendations. Nothing bad happens — the system learns from your judgment.
- A dashboard shows you what happened and leaves you to figure out what to do. Decision Intelligence proposes a specific action, shows the evidence, and waits for approval. It is the step after analytics.
What kind of recommendations does it make?+
Does it make decisions automatically?+
What data does it need?+
Can I see why a recommendation was made?+
What happens when I reject a recommendation?+
Is this different from a dashboard?+
A recommendation you cannot audit is just an opinion with better formatting.
See Decision Intelligence in context.
We'll walk through how recommendations work with your actual operational data and workflow.