Vertical AI Ops: What Restaurants Teach Security Teams

Most companies get AI agents wrong by treating them like a chatbox you can bolt onto anything. The restaurant world is proving the opposite: when AI is wired into operations—cameras, calls, POS, staffing, checklists—it stops being “AI theater” and starts behaving like software you can actually trust.

That’s why Palona’s move into hospitality this week is more than a product launch. It’s a case study in building multimodal, orchestrated AI that works in messy, high-stakes environments where mistakes cost real money and real trust. And if you’re responsible for AI in cybersecurity, supply chain risk, or procurement operations, the lessons translate surprisingly well.

Restaurants are supply chains in miniature: inventory arrives on tight timelines, labor is variable, demand spikes are unpredictable (hello, holiday parties and year-end catering), and fraud shows up in both physical and digital forms. When you treat restaurant ops as a real-time system, you get a blueprint for how to build AI that can also support threat detection, fraud prevention, and operational resilience.

Orchestrated AI beats “single-model” AI in real operations

If your AI strategy depends on one model vendor, you’re building on a fault line. Palona’s team described the current LLM ecosystem as “shifting sand,” and they responded the right way: by building an orchestration layer that can swap models based on cost, latency, and performance.

That choice matters for security and supply chain teams because model performance isn’t stable. Pricing changes. Regional language needs change. A model that’s great at text classification might be mediocre at structured extraction from messy transcripts. A model that’s strong on general reasoning can still be weak at domain-specific exceptions.

What orchestration looks like when it’s done for real

In practice, orchestration isn’t a buzzword—it’s an architecture decision:

• Policy-driven model routing: Send Spanish-language calls to a model that’s consistently fluent in Spanish, not just “pretty good sometimes.”
• Cost-aware fallbacks: Use smaller models for routine tasks (status updates, checklist verification) and reserve larger models for edge cases.
• Task-specific evaluation: Treat “accuracy on this workflow step” as the KPI, not “the model seems smart.”

For cybersecurity leaders, this maps cleanly to modern SOC needs:

• Route phishing triage to a fast, cheaper classifier.
• Route incident narrative generation to a stronger reasoning model.
• Route log enrichment to a model tuned for structured extraction.

Opinion: Orchestration is becoming the new SIEM. Not because it replaces security tooling, but because it becomes the control plane for decisions, cost, and reliability.

Multimodal signals are the future of anomaly detection

Text-only AI is easy to demo and hard to operationalize. Palona’s vertical push is built around a multimodal pipeline—vision (cameras), voice (calls), and text (messages/workflows)—to create a real-time view of what’s happening in a restaurant.

That’s exactly what strong threat detection looks like in 2025. The best detections don’t come from one log source; they come from correlating weak signals into a strong conclusion.

What restaurants can teach you about “real-world telemetry”

Palona Vision uses existing security cameras (no new hardware) to detect operational signals like queue length, table turnover, bottlenecks, and cleanliness. In their examples, the system can flag “cause and effect” patterns—like prep slowdown leading to order backlog.

Translate that pattern to cybersecurity and procurement risk:

• Fraud detection: Combine POS anomalies + unusual refund patterns + camera-confirmed register access.
• Insider risk: Correlate off-hours access + unusual inventory adjustments + repeated failed logins.
• Supply chain disruption: Combine delayed deliveries + stockout patterns + staffing shortfalls to predict service degradation.

In other words, multimodal AI turns operations into a detection surface.

A useful mental model: operational data is just security telemetry with a different label.

Why this matters for the “AI in Supply Chain & Procurement” series

Procurement teams already juggle supplier performance, contract compliance, and demand planning. What’s missing in many stacks is real-time ground truth. Restaurants have it: cameras, transactions, calls, and immediate outcomes.

If you can build an AI “operating system” for a restaurant, you can build one for:

• multi-site retail receiving
• warehouse pick/pack/ship
• last-mile delivery exceptions
• vendor compliance monitoring

The through-line is the same: detect early, route intelligently, execute consistently.

Vertical AI works because it earns domain data (and stops guessing)

Generalist agents fail in enterprise settings for a simple reason: they don’t have the right data, and they don’t understand the consequences of being wrong.

Palona’s leadership openly warned against going multi-industry. By narrowing to restaurants, they gained access to the stuff that actually trains useful systems: prep playbooks, call transcripts, operational checklists, POS mappings, and real exception patterns.

Verticalization is a security strategy, not just a go-to-market strategy

Here’s the uncomfortable truth: broad agents are more likely to hallucinate because they’re forced to generalize across ambiguous contexts. In security and procurement, “close enough” is still failure.

Vertical AI improves reliability because it can:

• enforce a controlled vocabulary (menu items; SKUs; contract clauses)
• ground answers in vetted sources (menu database; approved supplier catalog)
• model real workflows (opening checklist; receiving process; invoice approvals)

In supply chain and procurement, the vertical approach often looks like:

• three-way match automation (PO ↔ invoice ↔ receiving)
• contract compliance monitoring (price, terms, delivery windows)
• supplier risk scoring (performance + incidents + external signals)

My stance: if your AI project doesn’t have a “domain boundary,” it’s not a product—it’s a pilot.

Memory is where most enterprise agents quietly break

Restaurants have a brutal “memory test”: a repeat customer expects the system to remember their usual order, allergy notes, and preferences—without inventing details.

Palona found that off-the-shelf memory tooling produced errors around 30% of the time in their environment, which matches what many teams see: memory adds personalization, but it also adds failure modes (wrong recall, stale recall, cross-user leakage).

To address this, they built a custom memory system (“Muffin”) designed around four layers:

1. Structured data: stable facts (addresses, allergy information)
2. Slow-changing preferences: loyalty habits, favorite items
3. Transient/seasonal memory: context that changes with time (cold drinks in summer)
4. Regional context: time zones, language defaults

What “memory architecture” means for cybersecurity and procurement

Security teams are building agents that remember:

• asset criticality
• prior incidents on a host
• exception approvals
• business context (quarter-end close, holiday coverage)

Procurement teams want agents that remember:

• negotiated terms by supplier
• preferred alternates during shortages
• compliance exceptions and expiration dates

Two hard rules I’ve found helpful:

• Memory must be typed. If everything is shoved into a single vector store, you’ll get confident nonsense.
• Memory must be permissioned. If your agent can “remember” across tenants, locations, or roles, you’ve created an insider-risk generator.

If you’re chasing leads in enterprise AI right now, this is a strong wedge: most companies feel memory pain but don’t have a clean design for it.

Reliability frameworks matter more than model IQ

A restaurant hallucination isn’t funny. It can create fake promotions during a dinner rush, trigger chargebacks, or damage brand trust in minutes. Palona’s response was to formalize reliability using a framework they call GRACE:

• Guardrails: hard limits on what the agent can offer or approve
• Red teaming: systematically trying to break the agent
• App sec: locking down integrations (TLS, tokenization, abuse prevention)
• Compliance: grounding responses in verified menu data
• Escalation: handing off to humans for complex cases

They also validated behavior through large-scale simulation—reportedly simulating a million ways to order pizza to measure accuracy and reduce hallucinations.

Map GRACE to enterprise security operations

If you’re deploying AI into a SOC, procurement desk, or IT service workflow, GRACE translates cleanly:

• Guardrails: approval policies, allowlists/denylists, tool permission boundaries
• Red teaming: prompt injection tests, data exfil tests, tool misuse tests
• App sec: secret management, scoped tokens, audit logs, least privilege
• Compliance: ground outputs in CMDB, ticketing data, contract repositories
• Escalation: human-in-the-loop for high-impact actions (refunds, account locks, supplier changes)

One-liner worth stealing internally: If an agent can take action, it needs a safety case.

Practical next steps: apply the “digital GM” idea to your org

The most useful way to think about Palona’s approach is “a digital GM that never sleeps.” For supply chain, procurement, and cybersecurity, the equivalent is “a digital duty manager” that watches signals, flags anomalies early, and routes work to the right place.

A 30-day pilot plan that doesn’t turn into shelfware

1. Pick one workflow with real cost of failure. Examples: invoice exceptions, refund abuse, supplier delivery deviations, phishing triage.
2. Define three signals and one action. Keep it tight. Example: “If refund volume spikes + manager override rate increases + camera confirms register access, open a fraud investigation ticket.”
3. Instrument auditability from day one. Store inputs, model choice (orchestrator decision), output, and action taken.
4. Build escalation paths before automation. If escalation isn’t designed, you’ll either over-automate (risk) or under-automate (no ROI).
5. Run red-team drills weekly. Prompt injection and tool misuse aren’t edge cases anymore.

What to ask vendors (or your internal team)

• Where does grounding data come from, and how is it kept current?
• What are the guardrails on tool use and approvals?
• How do you prevent cross-location or cross-role memory leakage?
• Can you swap models without rewriting workflows?
• What’s your measurable hallucination rate on our tasks?

Where this is going in 2026

Restaurants are showing that vertical, multimodal AI can run real operations—not just answer questions. The interesting part for cybersecurity and supply chain isn’t the hospitality angle; it’s the architecture: orchestration, multimodal correlation, typed memory, and reliability frameworks designed for high-pressure environments.

If you’re building or buying AI for supply chain risk management, procurement automation, or security operations, treat this as your checklist. General assistants will keep getting smarter, but companies will keep getting burned by systems that can’t prove reliability.

If you had a “digital GM” watching your procurement and security workflows tonight—calls, transactions, access events, inventory moves—what’s the first anomaly you’d want it to catch before it turns into a loss?