Restaurant Chatbot Development That Stops Double-Bookings and Lost Orders

OpenTable shows two free tables at 7:30. Your chatbot confirms a party of four—then the host stand realizes those tables were already promised to a walk-in list and a private event note living only in the POS. One bad sync turns into comped drinks, a slammed server section, and a one-star review.

That’s the uncomfortable truth about restaurant chatbot development: it’s not a marketing widget. It’s operational software that touches scarce, time-bound inventory (seats) and even scarcer capacity (the kitchen). When it’s disconnected, it doesn’t merely “underperform”—it creates new failure modes you didn’t have before.

A system-integrated bot solves the real problems: double-bookings, lost or duplicate orders, menu drift, and kitchen overload. But “integrated” has a precise meaning: two-way data sync with your reservation platform, your POS, and the operational logic that turns “Order placed” into “Ticket fired on the line.” Ideally, that sync is near real-time availability, not a stale cache and a prayer.

In this guide, we’ll break down what restaurant chatbot integration actually requires, where projects go wrong, and what a practical rollout plan looks like. We’ll stay grounded in rush-hour reality—no-shows, 86’d items, pacing rules—because that’s where your bot either earns trust or gets shut off.

At Buzzi.ai, we build tailor-made AI agents and multichannel bots (including WhatsApp) designed to integrate into the stack you already run, not replace it. The goal isn’t a clever conversation. The goal is fewer interrupts, fewer exceptions, and confirmations that your staff can treat as true.

Restaurant chatbot development isn’t UX—it's ops infrastructure

Most chatbot projects start with copy: greetings, tone, FAQs, maybe a few flows. Restaurant chatbot development has to start with constraints. You’re not selling content; you’re selling table inventory and kitchen throughput—both perishable, both governed by rules, and both subject to human override at the worst possible moment.

That’s why the highest-leverage question isn’t “What should the bot say?” It’s “What system decides what’s true?” Once you answer that, the conversation becomes an interface to operations, not a layer of confusion on top of it.

Generic chatbot vs restaurant chatbot: what changes

A generic FAQ bot can happily answer “What are your hours?” all day. If it’s wrong, the cost is annoyance. A restaurant reservation chatbot is different: it’s making promises about a specific time slot, a table configuration, and policies like deposits or late-arrival windows.

Restaurants operate on scarce, time-bound inventory: seats and kitchen capacity. That forces the bot to reason about capacity management, turn times, pacing, party size constraints, high chairs, allergies, and those “special notes” that too often live in someone’s head.

Here’s a quick vignette:

• Generic bot: “We’re open 11–10.”
• Restaurant bot: “We can do 7:15 or 8:00 for 4. Any high chair needed? I’ll note the shellfish allergy. Our late-arrival policy holds the table for 15 minutes—should we book 7:30 instead?”

Success metrics follow that difference. The goal isn’t “engagement.” It’s fewer interruptions at the host stand and fewer kitchen exceptions.

The three systems your bot must respect (or it will fail)

In practice, restaurant chatbot development touches three operational domains:

• Reservation/guest management (OpenTable, Resy, SevenRooms): table inventory, pacing rules, guest profiles, notes, cancellations.
• POS (Toast, Square for Restaurants, Clover): menu, pricing, modifiers, taxes, discounts, tenders, tickets, refunds/voids.
• Ordering + KDS: execution reality—prep times, throttling, routing, and whether the kitchen can actually deliver on the promised pickup time. (Yes, your kitchen display system (KDS) matters even if the bot never “sees” it.)

Common stacks differ by segment. Independents might run Square + OpenTable + a basic online ordering system. Multi-location brands might run Toast + SevenRooms + multiple delivery and loyalty tools. Your bot has to work with the restaurant tech stack you have, not the stack in a demo deck.

A simple rule: pick a source of truth for each data type

Integration fails when two systems both “kind of” own the same thing. The fix is simple to say and hard to implement: for each data type, choose the system of truth and treat everything else as derived.

• Availability: reservation system (it knows holds, pacing, floor plan rules).
• Menu/prices/modifiers: POS (it’s where reconciliation happens).
• Pickup promises: kitchen pacing logic (sometimes POS order throttles, sometimes a dedicated layer).

Then define permissions: which systems can only read, and which can write. A bot that “checks availability” but cannot create a booking in the same system is setting you up for phantom inventory.

Scenario: the POS flags an item as 86’d. A system-integrated bot must stop selling it across all channels within minutes. That’s menu data synchronization as an operational requirement, not a nice-to-have.

This ops-first mindset pairs naturally with workflow and process automation services, because the chatbot is only as reliable as the workflows it automates.

The real cost of a disconnected restaurant chatbot (failure modes)

The easiest restaurant chatbot to build is the one that doesn’t touch anything: it answers questions and hands off to a phone number. The bot “works” in a demo because it never commits to outcomes.

The moment you ask it to book tables or take orders, disconnected architecture shows up as four expensive failure modes. These are operational costs that don’t appear on a marketing dashboard but do appear on a Friday night.

Double-bookings and phantom availability

One-way “check availability” is the integration trap. It’s usually a cached snapshot, lacking turn-time rules, manual blocks, pacing logic, and those last-minute host edits that keep service sane. Even if you query a reservation system API, you can still lose to race conditions when multiple channels are competing for the same slots.

Customers treat your bot’s confirmation as a promise. That’s why real-time availability isn’t a feature—it’s risk control. When the bot overpromises, the host stand pays the price: reshuffling sections, comping drinks, and managing emotion.

Anecdote pattern you’ve likely lived: the bot confirms an 8-top at 7:00. But the floor plan can’t seat it without splitting tables that were being held for turn times. Chaos ensues. The bot didn’t “book a table”; it booked conflict.

Lost orders, duplicate tickets, and payment mismatches

Standalone ordering flows often fail at the handoff. The customer completes a chat checkout, receives a confirmation, and then… no ticket ever reaches the POS. Or worse: it reaches twice, because your system retried a request without an idempotency key and created duplicate orders.

This is where POS system integration becomes non-negotiable. A chatbot that can’t verify “order exists in POS with ID X” doesn’t really take orders—it collects wishes.

Payment mismatches compound it. Refunds and voids have restaurant-specific rules; you need a mapping between what the bot considers “refunded” and what the POS considers “refunded.” Without that, support load spikes and reconciliation becomes a weekly ritual.

Menu drift: prices, modifiers, and 86’s don't match reality

Menus change daily. Modifiers change hourly. And modifiers are where most bots break: spice levels, cook temps, add-ons, substitutions, and “no onions” notes that need to map cleanly into POS structures.

A “manual menu upload” looks cheap until it becomes permanent. Then you’re paying an ongoing maintenance tax to keep your bot from selling the wrong item at the wrong price. Drift creates chargebacks, staff rework, and awkward customer conversations.

Example: lunch menu ends at 3pm, but the online ordering chatbot still offers it at 3:15. Kitchen rejects. Guest gets upset because the bot already confirmed. That’s not a UX issue; it’s a system integrity issue.

Kitchen overload from bad promises

A bot can increase demand faster than your kitchen can fulfill it. If it blindly accepts orders and quotes aggressive ETAs, you trade “more orders” for “more angry customers,” which is a bad swap.

You need order throttling, prep-time buffers, and per-channel pacing. Promise accuracy beats promise speed. Especially at peak.

Rush-hour contrast: without throttling, the bot keeps accepting 15-minute pickup promises until the line collapses. With throttling, it shifts to 35–50 minute windows (or pauses ordering) and preserves kitchen integrity. The latter feels slower; it performs better.

What ‘real-time two-way sync’ looks like in practice

“Two-way sync” is often used like a slogan. In restaurant chatbot development, it has a specific operational meaning: the bot can read the live state from systems of record, write changes back to those systems, and then confirm the resulting state with stable IDs.

It also means the bot can handle reality: edits by staff, cancellations, and partial failures. The sync is not just data movement; it’s state management under pressure.

Reservations: read availability, write bookings, then confirm back

A reliable reservation flow is basically a transaction. It needs to be designed like one.

Typical flow:

1. User request: “Table for 4 at 7:30 tonight.”
2. Availability query: bot checks the reservation platform for valid slots (accounting for turn times/pacing).
3. Tentative hold/lock: if supported, place a short hold so another channel can’t grab the same slot mid-flow.
4. Create reservation: write booking into the reservation system; store the reservation ID.
5. Confirm: send the guest a confirmation that references the system-of-record ID and includes modify/cancel actions consistent with policy.

Holds matter most at high-traffic times because chat introduces latency: users ask questions, hesitate, change party size. Without locks, you’re racing other channels.

Ordering: from chat to POS to KDS without re-entry

Ordering should be “chat in front, ticket in back.” No retyping. No screenshots. No staff translation layer. That means your bot must validate the cart against the POS, compute prices consistently, and only confirm once the POS order is created.

Operational flow:

1. Build cart: items + modifiers + notes (structured where possible).
2. Validate: ensure items/modifiers exist, combos are allowed, and 86 status is respected.
3. Price: calculate subtotal, taxes, fees, and discounts with POS-aligned logic.
4. Confirm + pay: collect payment if needed (or “pay on pickup”).
5. Create POS order: write the order to POS; store order ID; route to KDS as your system does today.

Retry logic is where systems either become robust or chaotic. Use idempotency: if the customer’s connection drops and the bot retries, you want “same order ID confirmed” not “duplicate ticket fired.” This is a core principle in modern payments and ordering flows; Stripe’s explanation of idempotency keys is a good reference point: https://docs.stripe.com/api/idempotent_requests.

Example transcript moment (simplified): the guest tries to order a burger “medium rare” that your POS only allows for dine-in. The bot should catch that validation and offer a compliant alternative, instead of creating a ticket the kitchen will reject.

This is also where brand-specific integrations show up: a Toast POS chatbot needs to map cleanly into Toast’s menu structures; a Square for Restaurants integration must respect catalog objects and order states.

State management: the bot must remember what the restaurant considers true

Bots have two kinds of memory:

• Session state: temporary context like “party size = 4” while the conversation is active.
• Durable state: facts tied to system-of-record IDs: reservation ID, POS order ID, cancellation status.

The durable state must survive channel switches and human edits. If the host moves a 7:30 reservation to 7:45, the bot should reflect that when the guest asks, “Is my table still at 7:30?” That requires either webhook updates or re-fetching from the system of truth.

Audit trails matter too. When disputes happen (“We never got the order”), you want an event history: requests, responses, and what the POS ultimately confirmed.

Integration targets: what to connect (and what to avoid early)

Integration scope is where good intentions go to die. The winning approach is to connect the systems that define truth for the workflows you’re automating, then defer everything else until your core flows are stable.

In restaurant chatbot development, the core flows are usually: book/modify/cancel reservations, place/track orders, and reflect menu/availability changes accurately.

Reservation platforms: OpenTable, Resy, SevenRooms (and what differs)

From the outside, reservation systems look interchangeable. From an integration standpoint, they’re not. Capabilities vary: some expose robust availability queries and guest notes; others have limitations around holds, cancellations, or policy objects.

When evaluating OpenTable integration, Resy integration, or SevenRooms, use a capability checklist like:

• Can we query real-time availability with pacing and turn-time rules applied?
• Can we create, modify, and cancel reservations via API?
• Are temporary holds/locks supported?
• Can we attach guest notes (allergies, celebrations) as structured fields?
• Are no-show policies, deposits, and late-arrival windows accessible?
• Do webhooks exist for cancellations/changes?

Start with the platform you already operationalize daily. A bot cannot fix a reservation workflow the team doesn’t consistently use.

Helpful starting points: OpenTable for Restaurants help and Resy (for partner/integration pathways, Resy typically routes through partner programs).

POS systems: Toast, Square for Restaurants, Clover

Your POS is not just payments. It’s your operational ledger. Integrations need to map the objects the staff actually uses: menu items, modifier groups, taxes, discounts, tender types, and order states.

Good POS system integration also respects that staff can still edit and void orders. If a manager comps an item or changes a modifier, the bot shouldn’t “fight” it. It should reconcile to what the POS now considers true.

A common failure: modifier mismatch. The bot offers “extra spicy” but the POS has “spicy level: mild/medium/hot.” Orders fail validation, or worse, they pass with incorrect data and the kitchen improvises. “Good mapping” means the bot’s choices are generated from the POS modifier schema, not from a hardcoded script.

Developer documentation examples include Toast developer docs and Square’s APIs for catalog and orders: https://developer.squareup.com/docs.

Online ordering + delivery: keep the bot honest about fulfillment

If you already use an online food ordering system, decide whether the chatbot creates orders there or directly in the POS. The key is to avoid dual entry and conflicting status updates.

Delivery marketplaces often limit direct integration. Early on, prefer a controlled handoff: the bot can capture intent and provide clear next steps rather than “pretending” it can fully automate a marketplace flow.

Also: keep ETAs honest. A bot that quotes delivery when the kitchen is above threshold is manufacturing disappointment. Better behavior: offer delivery only when capacity is available; otherwise suggest pickup windows.

Integration patterns that actually work: APIs, webhooks, middleware

You can build restaurant chatbot integration three ways: direct APIs, webhooks-driven event sync, and middleware/iPaaS. In reality, you usually use a mix: APIs for commands, webhooks for updates, middleware for normalization and governance.

The difference between a demo bot and an operational one is not the LLM. It’s the integration pattern—and the failure handling around it.

Direct API integrations: fastest when the stack is stable

Direct integration is best when your POS and reservation platforms have mature, accessible APIs and your stack doesn’t change every quarter. It’s also the fastest path to shipping a reliable MVP.

But you need to design for boring constraints: rate limits, pagination, versioning, and occasional downtime. You also need two-way data sync semantics that prevent duplicates and partial commits.

Concrete retry example: network fails after you send “create order,” but before you receive a response. If you retry without idempotency, you may create a second ticket. If you retry with an idempotency key, the POS returns the original result and you display “Order confirmed (ID 18452)” reliably.

Webhooks for real-time updates (the missing piece in many bots)

Polling is how you build lag. Webhooks are how you build trust.

With webhooks, the systems tell the bot when something changes: cancellation, table status change, item 86, order accepted, order ready. This is the difference between “the bot said it’s fine” and “the bot reflects the floor in near real time.”

Design for real-world webhook behavior: events can arrive out of order, arrive twice, or be delayed. Store events for replay and troubleshooting. Treat webhooks like a data stream, not a single notification.

Example: a cancellation webhook fires for a 7:30 slot. The bot can immediately offer that slot to someone on a waitlist flow, improving utilization without adding host work.

Middleware/iPaaS: when you have multiple locations and messy systems

Middleware becomes attractive when you’re multi-location, multi-vendor, or you need a canonical service for menus and availability. It can normalize data across regions and route requests to the correct credentials per store.

The tradeoff is real: you add another vendor and another layer of complexity. But you also gain observability, change control, and a single place to implement logging and monitoring—things that matter when you’re running a restaurant chatbot development company for multi-location brands style rollout.

Scenario: one brand runs Toast everywhere, but uses different reservation providers by region. Middleware can provide a unified “availability” interface to the bot while mapping to each regional platform behind the scenes.

Fallbacks and manual overrides: design for the 2% that matters

The bot should be confident only when the systems are confident. When core dependencies are down, the bot must degrade gracefully. Do not “guess” availability or accept orders that you can’t commit.

Practical controls include:

• Human-in-the-loop escalation: create a callback ticket, trigger an SMS to staff, or route to a dashboard.
• Safe mode: pause ordering if POS is unreachable; shift reservations to request-only if the reservation API is down.
• Ops controls: adjust prep-time buffers and throttling thresholds from a simple control panel.

Example policy: “If reservation API is unavailable, collect request details and promise a staff confirmation—do not confirm a time slot.” That single rule prevents a cascade of double-bookings.

Designing the guest experience that matches the back-of-house truth

Guests don’t care about your architecture. They care about whether the confirmation is true, whether the ETA is believable, and whether changes are easy.

The best conversational design is the one that makes operational constraints feel natural. That’s the hidden art of restaurant chatbot development: translating back-of-house truth into front-of-house clarity.

Conversation design that reduces exceptions

Great bots ask fewer questions—but the right questions. For reservations, that’s usually party size, time flexibility, and any essential notes (high chair, allergy). For ordering, it’s fulfillment mode (pickup/delivery/dine-in), timing constraints, and modifier choices.

When precision matters, use structured options. Time slots and modifiers should be selectable, not free-text. Free-text is where exceptions breed.

Mini-script when no slots are available:

• “We’re fully booked at 7:30.”
• “Next available is 7:45–8:15 window. Want that?”
• “Or I can add you to the waitlist and message you if a table opens.”

This keeps the guest moving while keeping the host stand sane.

Menus and modifiers: make ‘accuracy’ feel effortless

Accuracy doesn’t have to feel rigid. The trick is to guide choices the way a great server does: narrow options, then confirm.

When an item is 86’d, don’t just say “unavailable.” Offer substitutions that keep the ticket valid. Keep pricing transparent (taxes/fees) to reduce “surprise” disputes later.

Example: the guest wants fries but they’re 86’d. The bot responds: “Fries are unavailable right now. Would you like side salad or roasted potatoes instead?” That’s menu data synchronization turned into a good experience.

Omnichannel reality: phone, web, WhatsApp, Instagram DMs

Restaurants are inherently omnichannel: phone calls, walk-ins, web orders, and DMs. A successful bot respects that guests will message you where they already are.

That’s why multichannel chatbot deployment matters. A WhatsApp restaurant chatbot can deflect calls after hours and capture high-intent orders; Instagram DMs can become a reservation entry point; web chat can handle order tracking.

The non-negotiable is consistency: same inventory, same policies, same confirmations across channels. If the bot behaves differently on WhatsApp than on web, staff will lose trust because guests arrive with conflicting promises.

Multi-location brands: one chatbot, many realities

Multi-location is where “just copy the bot” fails. Each store has different hours, menu variance, staffing levels, local rules, and sometimes different tech vendors. If you don’t encode those differences, your bot will confidently do the wrong thing at scale.

Strong restaurant chatbot development for groups treats “location” as a first-class concept, not an afterthought.

Location routing: the hidden requirement

Before the bot quotes times or prices, it needs to know which store it’s serving. The routing can be based on geolocation, ZIP, “nearest store,” or explicit selection. But it has to happen early.

Then the bot must respect per-location hours, holiday closures, and reservation rules—and map each store to the correct POS and reservation credentials. This is where real-time queries matter; a single store with a private event block can invalidate generic answers.

Example: a guest says “book tonight.” The bot should first ask, “Which location?” before offering real-time availability. Otherwise you’re generating promises before you’ve selected the system of truth.

Menu and pricing variance without chaos

Brands want consistency; stores need flexibility. The operational solution is a canonical menu structure (categories, naming) with per-store item IDs, taxes, pricing, and availability. That’s how you avoid chaos while allowing local variance.

Sync schedules should match reality:

• Near-real-time: availability and order status.
• Daily baseline: full menu refresh (prices, categories).
• Instant: 86 events and urgent updates.

Example: one location has alcohol delivery restrictions. The bot enforces it automatically based on store policy, preventing compliance issues and awkward staff interventions.

Analytics and governance across stores

When you scale, you need shared metrics and role-based control. Regional ops should see standardized KPIs across locations; store managers should have local controls (pause ordering, adjust prep times).

Governance also includes audit logs: who changed what, when, and why. That matters when you’re debugging an incident or iterating on flows.

“What good looks like” weekly for a regional manager:

• Order creation success rate in POS
• Reservation accuracy rate (no discrepancies at check-in)
• Exception rate (handoffs to humans)
• Menu sync freshness and 86 propagation time
• Peak-hour throttling activations and outcomes

ROI and metrics: what to measure after launch (and what to ignore)

The biggest ROI mistake is measuring chat metrics instead of operational metrics. “Messages per session” doesn’t pay for itself. Fewer exceptions does.

For restaurant chatbot development, the KPI set should prove that the bot is making the operation calmer and more accurate—not just busier.

Core KPI set for integrated restaurant chatbots

Here are practical KPIs you can collect without building a data science team:

• % orders successfully created in POS: confirmed chats that result in a POS order ID.
• Order exception rate: % of orders requiring staff intervention (invalid modifiers, unavailable items, payment issues).
• Duplicate ticket rate: frequency of duplicates caused by retries or race conditions.
• Reservation accuracy rate: reservations confirmed by bot that match the reservation system at check-in.
• No-show / cancellation handling success: can guests modify/cancel without calling?
• Average time-to-resolution for escalations: how fast humans close the 2% edge cases.
• Call deflection: reduction in calls for ordering/booking/status checks.
• Peak-hour interruption reduction: fewer “can you check this?” moments at host/kitchen.
• CSAT / review mentions: specifically tied to ordering and booking accuracy.

Revenue lift that doesn’t come from spam

Revenue lift is real, but it tends to come from boring places: capturing after-hours demand, reducing abandonment with instant confirmations, and offering upsells that respect constraints.

A good bot doesn’t upsell by default. It upsells when the kitchen can handle it and when items are actually available. Example: suggest dessert add-ons only when prep-time buffers allow and the station isn’t overloaded.

This is where omnichannel ordering pays off: more capture with less labor, without turning your bot into a pushy marketer.

Maintenance metrics: the unsexy predictor of long-term success

The best predictor of long-term success is not how fancy your AI is. It’s how well you maintain system integrity.

Track:

• Menu sync freshness: time since last successful baseline sync.
• Webhook failure rate: missed/delayed events; replay success.
• Integration error budget: how many failures per week are acceptable before you adjust.
• Time-to-fix: when vendors change APIs, how quickly do you recover?
• Cost of ownership: engineering + ops time versus labor saved.

A monthly ops review checklist keeps the system healthy and prevents the slow creep of drift.

How Buzzi.ai builds system-integrated restaurant chatbots (project plan)

If you want a bot that holds up during rush hour, the project plan has to prioritize integration and reliability before polish. At Buzzi.ai, we treat restaurant chatbot development like launching ops infrastructure: you define truth sources, implement safe workflows, and only then optimize the conversation.

This is what restaurant chatbot development services for POS integration looks like when it’s built to ship.

Discovery: map the real workflow before writing prompts

Discovery isn’t a “requirements meeting.” It’s a workflow map that matches how your team actually runs service.

A 30-minute discovery questionnaire we use with ops leads typically covers:

• What POS do you use (and how many locations)?
• What reservation platform is the system of truth?
• Do you have an existing online ordering system and KDS flow?
• Which channels matter most (web chat, WhatsApp, Instagram DMs, phone deflection)?
• Which policies are no-compromise (deposits, no-show fees, cutoff times, refund rules)?
• What are your peak periods and pacing rules?
• What are the highest-cost failures today (double-bookings, missed tickets, menu drift)?

From this we define sources of truth and the failure costs per workflow, so we can prioritize the paths that matter.

Build: integrate first, then polish conversation

We build the hardest paths first because they define whether the system is real:

• Create reservation
• Create order
• Cancel/modify reservation
• Cancel/void/refund order as supported by your policies
• Handle 86 events and menu updates
• Retries with idempotency and clear messaging

We implement logging, monitoring, and escalation from day one. Then we add multichannel chatbot deployment once the core flows are stable. It’s better to be accurate on one channel than inconsistent on five.

Example milestone plan:

1. Week 1–2: integration setup (POS + reservations), menu sync, basic flows.
2. Week 3: webhook/event handling, idempotency, safe mode and escalation.
3. Week 4: pilot launch at one location; staff training on overrides.
4. Week 5–6: iterate based on transcripts and exception logs; expand use cases or channels.

Pilot: launch where the pain is highest (not where it’s easiest)

Pilots fail when teams choose the easiest store instead of the most representative one. We prefer a location where the pain is real: the busiest host stand, the most frequent phone interruptions, the peak ordering window that currently breaks.

A pilot scorecard might include:

• POS order creation success rate above an agreed threshold
• Reservation accuracy rate with minimal host intervention
• Exception categories logged and shrinking week over week
• Clear rollback criteria and a documented “safe mode”
• Staff feedback captured from hosts and kitchen leads

We also tune order throttling and buffers during the pilot, because that’s where promise accuracy is won.

Scale: multi-location rollout with governance

Scaling is mostly governance and configuration. We template flows, parameterize menus/hours/policies by location, and centralize monitoring while preserving local control.

“Add a new location” should be a repeatable playbook: map credentials, validate menu sync, test reservation creation, run a peak simulation, train staff on overrides, then go live. Done well, adding a store can be measured in days, not months.

If you want to go deeper on building the bot itself, our AI chatbot and virtual assistant development work is designed specifically for system-integrated deployments, not surface-level chat.

Conclusion

Restaurant chatbot development succeeds only when it’s wired into reservations, POS, and kitchen reality. Two-way sync prevents the expensive failures—double-bookings, lost orders, and menu drift—that make staff distrust automation.

Webhooks, idempotency, and fallbacks are the difference between a demo and an operational tool. And if you’re multi-location, routing and governance matter as much as conversation design.

If you’re evaluating a restaurant chatbot, start with your integrations—not your script. Talk to Buzzi.ai about building a system-integrated bot that syncs with your reservations and POS and holds up during rush hour.

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