Proposal for [Client] Hotels
Strategic data and AI advisory, with a path to build.
Your presenters.
David Stocker
Product strategy, embedded analytics, and AI implementation. David leads client engagements from first conversation through delivery, with a focus on commercial outcomes and AI-ready data architecture.
Johnathan Brooks
Enterprise data platforms, AI/ML systems, data engineering, and security. Johnathan leads architecture across Astrodata's most complex platform builds and sets the technical standard for every engagement.
30-person data and AI consultancy · Senior-to-principal team · Active partnerships with Snowflake, Omni Analytics, and Anthropic
The next 90 days set five years of cost and capability.
In a partnership with Astrodata, [Client]’s 2026 technology strategy gets the architecture right. Data readiness is critical to the success of any AI model, and the unified guest profile is the foundation. Commercial execution (B2C and B2B) is the highest business priority, and every agent in the 2026 to 2028+ roadmap depends on a governed, scalable, comprehensive data layer.
Astrodata has already shipped the same architecture in production, can compress the timeline from “directionally right” to “in market,” and can stay long enough to make sure the build matches the vision.
The patterns [Client] is going to need, already in production elsewhere.
A unified analytics platform powering agentic and conversational workloads at the largest virtual care provider in the United States.
An agentic search experience that lets 150 million health plan members find providers by asking questions in natural language, and powers appointment scheduling.
An agent that lets business process managers explore ERP data, enrich it with external context, and take action on forecasts and recommendations.
30-person data + AI consultancy. Senior-to-principal team. Active partnerships with Snowflake, Omni Analytics, and Anthropic.
A unified data layer with an AI control plane on top.
An ongoing advisory partnership alongside a flexible build capability.
Ongoing strategic advisory
- AI & agent roadmap. Sequenced, gated on data readiness.
- Architecture guidance. Warehouse, ingestion, reverse ETL, agent runtime. TCO modeled across three years.
- Unified guest profile. Semantic model for guest_uuid, written specification, maintained as the business evolves.
- Commercial scoreboard. Metrics framework that says whether the platform is paying for itself.
Scoped and contracted as the business is ready
- Foundation. Warehouse and ingestion layer stood up, guest_uuid resolution, dashboard the Revenue Manager opens daily.
- Revenue intelligence. LTV, segments, and tags pushed back into Duetto, dailypoint, WhatsApp. Revenue Agent in human-in-loop.
- Guest-facing agents. Pre-Arrival, WhatsApp co-pilot, Voice Reservations.
- B2B and autonomous operations. Expansion as patterns prove out.
Year 1, sequenced by impact.
Each phase unlocks the next. Revenue management comes first because it has the most direct top-line lever and the clearest feedback loop. Data infrastructure laid in each phase carries forward into the one that follows.
Revenue Management
Augment Duetto with signals it doesn't ingest. Recommendations first, write-back as trust is earned.
Marketing and Attribution
Server-side tagging and warehouse-based attribution. Media-mix modeling. A/B Testing. Foundation for any later spend optimization agent.
Reservations, B2C and B2B
Unified guest profile across PMS, CRS, and loyalty. Prerequisite for personalization and direct booking optimization downstream.
Sales, Group and Leisure
Pipeline visibility and TA/corporate attribution. Emphasis on measurement due to longer sales cycles.
Customer Service
Retention and recovery. Important for NPS, secondary to top-line in early phases.
The medium term, 1 to 3 years.
By year three, [Client] has the architecture to absorb 10+ properties without re-platforming.
- Year 1. Foundation, Revenue Agent, and guest-facing co-pilots in human-in-loop. Scoreboard live with baselines and lift.
- Year 2. Governed automation for proven patterns. B2B integrations (TA module, group scoring, homes pipeline). Second property onboarded without re-architecting the data layer.
- Year 3. The platform scales from one property to ten or more on the same architecture. Astrodata’s role shifts from builder to strategic partner.
Revenue management and marketing attribution.
Revenue management
KPIs · Direct booking lift · RevPAR uplift · Pace vs. control
Augment Duetto with signals it doesn't ingest. Recommendations first, write-back as trust is earned.
- RevPAR uplift: [3 to 7%] vs. control weeks
- Direct booking share: +[2 to 5] percentage points
Marketing and attribution
KPIs · ROAS · CPA by channel · Advisor-attributed revenue
Server-side tagging and warehouse-based attribution. Media-mix modeling. A/B testing. Foundation for any later spend optimization agent.
- ROAS: +[10 to 25%] from better targeting
- CPA: down [10 to 20%] on top channels
- Advisor-attributed revenue: measurable for the first time
Reservations and sales pipeline.
Reservations, B2C and B2B
KPIs · Direct booking share · Booking conversion · Guest profile match rate
Unified guest profile across PMS, CRS, and loyalty. Prerequisite for personalization and direct booking optimization downstream.
- Direct booking share: +[2 to 5] percentage points
- Booking conversion: +[5 to 10%] from profile-driven personalization
- Guest profile match rate: [75 to 90%] of stays to a resolved guest_uuid
Sales, Group and Leisure
KPIs · Group pipeline visibility · TA attribution
Pipeline visibility and TA/corporate attribution. Emphasis on measurement due to longer sales cycles.
- Group pipeline: full funnel visible vs. point-in-time snapshots
- TA/corporate attribution: measurable for the first time
Customer service and guest lifetime value.
Customer Service
KPIs · NPS · WhatsApp response time · Case resolution time · Repeat booking rate
Retention and recovery. Important for NPS, secondary to top-line in early phases.
- NPS: +[3 to 8] points from personalized recovery and proactive outreach
- WhatsApp first reply: hours to under a minute
- Repeat booking rate: +[1 to 3] percentage points
Guest Loyalty and Lifetime Value
KPIs · LTV by segment · Loyalty enrollment · Revenue per returning guest
Customer service data feeds the guest profile. Returning guests are better understood, better served, and more likely to book direct.
- LTV by segment: measurable for the first time
- Loyalty enrollment: tracked and attributed across channels
- Revenue per returning guest: vs. new acquisition baseline
Three reasons.
The 90-day window
The technology decisions [Client] makes in the next 90 days will set five years of cost and capability. Better to invest in getting them right than to undo them.
Better-defined builds
Advisory makes every subsequent build engagement cleaner, with clear goals and acceptance criteria. De-risks both sides.
AI quality follows data
AI deployed on a half-baked foundation is convincing 90% of the time and catastrophic in the 10%. Getting the data right first makes the agents trustworthy.
What happens next.
- Week 1. Countersign engagement letter. Schedule recurring weekly working session. Identify the operational owners on the [Client] side.
- Weeks 2 to 4. System access, baseline metric capture, vendor evaluation kickoff for the warehouse layer.
- Weeks 5 to 10. Architecture recommendation delivered. Guest profile data model first draft. Agent roadmap sequenced and gated. Foundation project scoped.
- Week 10+. Foundation build engagement begins (if approved). Advisory continues alongside.
The advisory partnership starts immediately on countersign. Build engagements are scoped and contracted separately, on the cadence [Client] is ready for.
A pricing framework, not a price tag.
Four models, combinable. Most clients start in one and end up using two or three across a year. For [Client], we anticipate starting with Strategic Advisory and layering in Project or Retainer work as build engagements come into focus.
Strategic Advisory
Architecture, vendor selection, data modeling, agent roadmap. Weekly working sessions.
Fixed Bid Projects
Greenfield builds, migrations, integrations. Indicative ranges next slide.
Time and Materials
Principal architect $XXXXX/hrSenior engineer $XXXXX/hrOffshore engineers $XXXXX/hr
Retainer Pods
Quarter-, half-, or full-time staff. $XXXXX–$XXXXX per member, monthly depending on composition.
Build project sizing, indicative ranges.
$XXXXX to $XXXXX
Discovery, architecture audits, vendor evaluations, data model design, ROI scoping sprints.
$XXXXX to $XXXXX
Data foundation build, semantic layer, one agent shipped in human-in-loop with measurable lift against control.
$XXXXX to $XXXXX
Multi-agent revenue intelligence platform with reverse ETL, governed automation, and the commercial scoreboard live.
Let’s build it right.
Measure twice. Cut once. Repeat.
Architecture that scales from one property to ten. Agents that earn trust before they earn autonomy.A scoreboard that says, every quarter, whether the platform is paying for itself.