Customer & product adoption
Help people move from uncertainty to confident use.
Daniel D’Jesus — Restaurant Technology, Customer Onboarding, Product Support & AI Workflows
Better context. Better decisions.
- Restaurant Technology
- Customer Onboarding
- Product Support
- AI Workflows
10+ years inside restaurants Creating systems that connect people, operations, and information.
Scroll to explore01 / The intersection
From service to systems
Technology works better
when you understand the people using it.
A decade on the floor reading intent, timing, and constraints under pressure. I now use that same judgment in restaurant technology, onboarding, and AI workflows.
Operational systems
Connect people, timing, and information in live service.
AI-assisted building
Use agents and persistent context to research, prototype, and document.
02 / Selected workIdeas, made tangible.
Two case studies.
Restaurant operations software · Agentic workflow
LINE.
LINE is a restaurant knowledge layer — the same dish data, for the person at the table and the person serving it.
Founder / GTM. Prototyped with Carlos, a senior software engineer.
One question at the table.
Answer the question. Offer the solution. Keep service moving.
02 — Working prototype
See it in action.
One knowledge layer. Two views.
What the server needs during service.
Open full demo ↗LINE MENU HUB
Staff viewIllustrative full-service restaurant
Three fictional dishes and sample prices. Allergen filters are not food-safety clearance.
Tomahawk Ribeye
$200Sample price
Ingredients
45-day dry-aged tomahawk ribeye, salt. Grass-fed, grain-finished.
Allergens / risk
No dairy in the default preparation.
Shared grill. Large format; confirm doneness with the kitchen.
UnverifiedService notes
Sharing cut. Confirm doneness and rest time before sending.
Lobster L’Américaine
$48Sample price
Ingredients
Lobster, sauce Américaine, gnocchi, sunchoke, little gem.
Allergens / risk
Shellfish, dairy, gluten, egg.
Complex prep station. Confirm with the kitchen.
UnverifiedService notes
Half or whole. Confirm substitutions before describing the plate.
Mac n Cheese
$18Sample price
Ingredients
Pasta, béchamel, cheese.
Allergens / risk
Dairy, gluten.
Wheat in the sauce. Confirm with the kitchen.
UnverifiedService notes
86’d in this sample. Confirm availability before offering.
LINE MENU HUB
Guest viewSteakhouse
Public menu for the same three sample dishes. Kitchen notes stay in Staff view. Allergen filters are not food-safety clearance.
My allergies
No allergies selected
No dishes match these allergies. Clear the selection to see the menu.
Description
Ingredients
86’d — not available tonight
After the demo
The answer exists.
The server just can’t reach it
fast enough.
The problem is getting the right context to the person at the table — without slowing down service.
LINE is how I’m exploring that problem.
Knowledge already exists
Menu & recipes
Ingredients, allergens, pairings
Training docs
Policies, procedures, guidance
POS data
Items, modifiers, availability
Manager knowledge
Experience, context, nuance
Context in
LINE
Answers out
Staff at the table
Fast. Confident. Accurate.
01 Built
Guest-facing menu + structured restaurant knowledge
02 My role
Product discovery · workflow design · GTM · prototyping
Engineering in collaboration with a senior software engineer
03 Next
The smallest authenticated staff experience that can answer: “What do I need to know right now?”
Technical notes ↓
Guest-facing fields and operational staff fields are separate in the data model. Unknown allergen information stays unverified rather than inferred. Operational staff information requires authenticated access. The current staff experience is still being validated; this portfolio uses local illustrative fixtures, not a live restaurant.
People · Context · Better service
02 — Journal / Personal OS
HERMES.
Keep the context.
Find the next action.
Personal AI operating workflow
A change of focus shouldn’t mean losing my place.
I capture what happened, preserve the decision and the next action, and return with enough context to continue. Hermes is software from Nous Research; the personal workflow around it — with Obsidian — is mine. LINE is one project this workflow can support.
How I work with AI and context
- 01 Capture What happened, before deciding if it needs work.
- 02 Memory Decision, state, and next action in a journal note.
- 03 Context Enough to resume after a focus change.
- 04 One next action One bounded step, not a rebuilt plan.
- 05 Return Human review stays in the loop.
Not a live vault. Journals are not published.
03 / The foundationDownload full resume
Experience,
in perspective.
Hospitality experience in San Francisco.
Product and AI projects alongside it.
LINE
Founder / Product & GTM Lead
Personal AI workflow
AI Workflow Builder
EPIC Steak
Server / Customer-Facing Sales
Bon Délire
Server / Customer-Facing Sales
La Mar Cebichería Peruana
Server / Customer-Facing Sales
Cañada College
Associate Degree · Interdisciplinary Studies
Social & Behavioral Sciences
04 / What’s nextOpen to the right conversation
I’m looking for teams building software for real people, real operations, and real decisions.
Let’s talk
Where I want to contribute
Customer onboarding · Implementation support · Product support · Product specialist · Restaurant technology · Customer experience · Selective consultative sales