Operational analytics
Live dashboards for occupancy, revenue per channel, response time, lead quality, agent performance — the operational layer the team actually uses.
Service · AI & AnalyticsAI & Analytics
AI & Analytics
for hospitality operators
Applied AI, lead scoring, chatbots, content tools, and analytics layers that improve operational decisions and automation across hospitality and real estate.
H-Studio Indonesia · Plate set 2026Bali · APAC
Overview
AI without operational context is a demo.
We connect AI to the workflows that actually run the business — lead qualification, guest communication, content production, and operational reporting. Each project starts with a question worth answering, not with a model worth shipping.
from $1,500·4–10 weeks
01 / What we build
What we build
AI lead scoring
Score inquiries automatically by intent, budget, and readiness so the sales team knows what to prioritise — before they reply.
AI chatbots & assistants
24/7 AI agents on WhatsApp, Instagram DMs, or the website — qualifying leads, handling FAQs, and handing off to a human when it matters.
Content tools
Multilingual content generation, editorial assistants, and translation pipelines tuned to your brand voice and target search keywords.
01 · How we start
Every engagement starts with clear scope.
Some projects begin with a short System Audit. Others with a focused website, booking platform, CRM, or custom portal. In every case we define the flow, system logic, responsibilities, and delivery plan before production work starts.
5 days
Architecture Sprint, optional
€5k+
Focused builds
1 studio
Scope, build and handover
01 · Phase
Workflows & data mapped
We document workflows, roles, data and integrations with you.
02 · Phase
Scope & delivery plan
Scope, system logic and the delivery plan are signed off before any code.
03 · Phase
Production build
Implementation with hosting, monitoring and analytics from day one.
04 · Phase
Handover
Hand-off including documentation — your team can own and extend it.
Questions we often get
Scenarios where teams like yours reach out.
Scenario · 01
“Do we need a lot of data to start?”
Not necessarily. We frequently start with operational dashboards built on existing data, then introduce AI components as the data layer matures.
Scenario · 02
“Will the AI replace our team?”
No. The goal is to absorb low-value repetitive work — first-touch qualification, FAQs, status updates — so the team focuses on high-value conversations.
Scenario · 03
“Which AI providers do you use?”
OpenAI, Anthropic Claude, and open-source models on Hugging Face — chosen per workflow based on latency, language support, and cost. We avoid single-vendor lock-in; the prompt and orchestration layer is provider-agnostic.
Scenario · 04
“Can the AI concierge actually convert bookings, or is it just deflection?”
Both. We design the flow so high-intent inquiries hit a human within 2 minutes, while low-intent FAQ and pre-stay questions are answered instantly. Conversion lift is measured against a baseline, not assumed.
Scenario · 05
“What analytics tools do you integrate with?”
GA4, Plausible, PostHog, Mixpanel, Looker Studio, and custom data warehouses on Supabase or BigQuery. Most operators end up with a thin custom dashboard for daily ops and a deeper Looker / Metabase layer for monthly review.
How we deliver
How we deliver
01 · Read
Discovery & Audit
Operations mapping, inquiry channels, competitor scan and an agreed information architecture before any design starts.
02 · Read
Design & Prototype
Design system on semantic tokens, key pages prototyped, content validated with your team before engineering kicks off.
03 · Read
Engineering & QA
Iterative releases on staging. You can click through every feature before it moves to production.
04 · Read
Launch & Operate
Cutover to production, health-probe monitoring, and optimisation against real booking and conversion data once it is live.
Why H-Studio
An engineering studio — not an agency.
- 01
Operational context first
Every AI build starts with a real business question — 'why are direct bookings flat?', 'why do agents miss SLA?'. The model is the answer mechanism, never the goal.
- 02
Multilingual by design
EN + ID native, with Mandarin, Russian, Arabic, and Korean as routine add-ons. AI agents respond in the guest's language, log in English for the operator, and never mix the two.
- 03
Privacy-aware data layer
PII is segregated from prompt context, consent flags are honored at the row level, and prompts are versioned and auditable — so the AI layer survives a privacy review, not just a demo.
- 04
Cost-controlled, not on-demand bill shock
Token budgets per workflow, model fallback ladders (Sonnet → Haiku → cached), and monthly cost reports so the AI layer is a known line item, not a surprise invoice from OpenAI.
What operators usually ask.
Get started · 011
Let’s build what
moves you forward.
From idea to infrastructure — we help you design, launch, and scale systems that perform.