Hospitality
What was the occasion?
She has stayed 12 times. On her last visit she was celebrating her anniversary. Your front desk found out when she mentioned it at check-in.
Large Intelligent Agentic Memory
Your platform remembers every transaction.
Except when it matters most.
LIAM is the persistent memory layer that gives your AI agents the context your systems never captured, built for any consumer brand with a loyalty program or AI initiative, from hospitality and travel to retail, fitness, and banking.
The Problem
The moments your platform keeps missing.
Half of what matters about your customer was never captured. Your CRM records what happened. Your loyalty platform tracks what was redeemed. But the context that shapes the next interaction lives nowhere in your stack.
Hospitality
He mentioned it once.
Three trips ago, in passing. He is vegan. Your property management system never captured it. Your kitchen found out at breakfast.
Travel
Eight bookings. One bucket-list moment.
Your platform saw eight transactions. One was for his daughter's wedding. You sent a generic miles summary.
Travel
They almost switched airlines.
Something went wrong on the last trip. They never complained formally. They nearly did not come back. Your CRM never knew any of it.
Fitness
Two hundred sessions. One goal.
She logged every workout for eight months. She was training for her first marathon. Your app kept recommending beginner programs.
Fitness
They came back. You did not know why they left.
You know they rejoined six months after cancelling. You do not know what made them quit. You are already making the same mistake.
Banking
She called twice. Ops treated her as new both times.
She called about the same stuck payment twice this month. Neither agent knew it was her second call. She had to explain the whole thing again.
Banking
Overdrawn every December. Flagged as new risk every time.
His business account dips into overdraft every December, seasonal freight costs, cleared by January. Ops reviews it as a fresh risk case each year, because nothing carries the pattern forward.
The Gap
Two sources of truth. Zero connection.
What your systems know
- Transactions and bookings
- Loyalty tier and points
- Purchase and session history
- Click and engagement data
×
What your customer knows
- The occasion behind this visit
- Preferences they were never asked about
- Intent driving this interaction
- Why they almost left last time
Meet LIAM
Your agents know what happened. LIAM tells them what matters.
Connect, fuse, and recall, memory that travels with the customer across every channel and every agent.
AI query: “What does this guest need for tonight's check-in?”
Sarah Chen, Stay 13 of 13
- Occasion 10th wedding anniversary
- Dietary Vegan since stay 3
- Room High floor, quiet side, extra pillows
- Note Partner does not know about the upgrade
Step 01
Connect
Sits alongside your existing stack. SDK and API connectors integrate with your CRM, PMS, loyalty platform, and mobile app. No system replacement. No migration.
Step 02
Fuse
LIAM merges enterprise behavioral data with customer-held context into a single persistent memory profile. Every preference, occasion, and intent, captured once and available always.
Step 03
Recall
O(1) deterministic recall. MCP-native. Your AI agents get the full picture in milliseconds, precise memory, not probabilistic inference.
Use Cases
Built for consumer brands where context is everything.
Hospitality
“Your guest checked in 12 times. LIAM knew about the honeymoon before they mentioned it.” Room preference, dietary requirements, honeymoon package, surfaced automatically, without a staff note or a form to fill.
Travel
“Your platform saw 8 bookings. LIAM knew one was for their daughter's wedding.” Occasion-aware upsells, pre-filled preferences and loyalty moments, delivered before the traveler has to ask.
Fitness
“Your app tracked 200 workouts. LIAM knew they were training for their first marathon.” Goal-aligned coaching, recovery-aware load and race-day nutrition, personalised to the member, not the metric.
Banking
“Your platform saw 40 transactions. LIAM knew the customer was saving for a first home.” Life-event context, product timing and service history, recalled the moment the customer reaches out.
See LIAM with your data.
Not a generic demo. We’ll show you LIAM running on actual customer profiles, real use cases, and outcomes — 15 minutes, no pitch deck, actual data.