Product discovery & positioning
Evolving the idea and identity from Dinely into a broader restaurant-intelligence product, then shaping the pilot around the questions that matter most.
Restaurant intelligence · 0 → 1
Building restaurant intelligence from the menu outward.
An Incub8-owned product connecting AI-assisted menu setup, guest QR journeys, ordering, restaurant operations, and a measurable recommendation loop.
- Product
- TablePulse
- Stage
- 0 → 1
- Status
- Pilot · In progress
- Engagement
- January 2026 → Present
01 / Our own product
The full product burden, carried by us.
TablePulse is where we apply our 0 → 1 practice to ourselves. The product started with the menu: one familiar surface shared by every restaurant and every guest. Making that surface genuinely useful meant working outward—into setup, ordering, table state, kitchen execution, behavioral evidence, and the decisions an operator makes next.
Formerly Dinely, TablePulse has been an Incub8-owned product since January 2026. There is no client boundary or handoff. Product direction, positioning, experience design, engineering, cloud delivery, pilot operations, and the consequences of each decision remain one continuing responsibility.
02 / What we are building
One team across every layer of the product.
We carry TablePulse from the restaurant’s first setup step through the guest experience, live service, product evidence, and the platform operating underneath it.
Brand, UX & design system
Designing the marketing, restaurant, operational, kitchen, and guest experiences as one coherent product family.
AI-assisted menu ingestion
Turning imperfect PDFs and photos into normalized, operator-reviewed categories, items, prices, descriptions, and dietary information.
Restaurant & menu platform
Building organizations, restaurants, menus, pricing, availability, tags, add-ons, promotions, and publishing lifecycles.
Guest QR & ordering
Creating instant, no-account browsing, preferences, item decisions, customization, cart behavior, ordering, and service requests.
Floor & kitchen operations
Connecting dining areas, tables, sessions, staff, service state, order routing, tickets, and kitchen-display workflows.
Analytics & recommendations
Capturing anonymous behavior, generating weekly evidence-backed opportunities, tracking actions, and comparing what happens next.
Platform, cloud & pilot delivery
Operating APIs, workers, data, authentication, object storage, infrastructure, deployment environments, invites, demos, feedback, and maintenance.
03 / Product problem
A menu is both a guest interface and an operating-system input.
For a guest, the interaction has to feel immediate: scan, understand, choose, and continue without an account or an app. For the restaurant, the same experience depends on structured menu data, live availability, pricing, promotions, table context, service state, and kitchen routing.
The product also has to learn without overstating what it knows. Anonymous browsing and order behavior can reveal a pattern, but useful recommendations require stable evidence, careful thresholds, and language that distinguishes what changed from what caused it.
Setup frictionAnonymous guest behaviorLive menu changesTable & session stateKitchen routingEvidence quality
04 / Product and engineering judgment
Simple at the surface. Deliberate underneath.
Start with the menu, model the restaurant
The first experience stays approachable while the underlying model accounts for restaurants, tables, staff, sessions, promotions, orders, kitchen stations, and insight history.
Keep slow AI work off the request path
Uploads and everyday actions remain responsive while menu parsing runs as a background job with progress, bounded retries, fallback handling, and explicit failure states.
Preserve behavior before interpreting it
Validated, append-only guest events remain the source evidence so reports and recommendation logic can evolve without rewriting history.
Snapshot operational truth
Order lines retain the item, price, station, modifiers, and instructions used at order time, even when the live menu changes later.
Put evidence ahead of AI prose
Deterministic analysis finds the pattern and supporting numbers. AI explains verified evidence or suggests a next step rather than inventing the metric.
Treat the pilot as product work
Invites, onboarding progress, demos, feedback, job health, and usage visibility are built into the platform because early learning depends on them.
05 / Product system
From source file to learning loop.
TablePulse connects setup, guest intent, service execution, and product learning through one shared restaurant model rather than assembling a set of disconnected tools.
01Restaurant foundation
OrganizationsRestaurantsLocationsDining areasTablesStaff
02Menu intelligence
File uploadAI parsingReviewMenus & itemsPricingPromotions
03Guest experience
QR entryPreferencesDiscoveryItem detailsAdd-onsCart
04Live operations
Table sessionsService requestsOrdersFloor approvalStation routingKitchen display
05Evidence layer
Anonymous eventsMetricsWeekly reportsRecommendationsActionsImpact comparison
06Platform foundation
Web applicationsAPIWorkersPostgreSQLObject storageAuthentication & cloud
06 / In active development
A product still teaching us what it needs to become.
TablePulse remains a work in progress and is being shaped through an invite-only pilot. We are continuing to refine the path from an existing menu to a live restaurant workflow, strengthen floor and kitchen operations, and learn which insights become genuinely useful actions for operators.
That unfinished state is part of the story. It shows how we work when the uncertainty, tradeoffs, operational edge cases, and long-term responsibility are ours—not only when we are advising someone else through them.
EngagementJan 2026 → Present
Stage0 → 1
RelationshipIncub8-owned product
Start a conversation / 05
Bring us the
hard part.
Tell us what you are trying to prove, build, or untangle. We will respond with useful questions—not a generic sales deck.
Discuss your producthello@incub8labs.com ↗