Astrodata / [Client]
Confidential proposal
Strategic data and AI advisory, with a path to build.
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, and comprehensive data layer.
Astrodata is a partner who has already shipped the same architecture in production, who can compress the timeline from “directionally right” to “in market,” and who can stay long enough to make sure the build matches the vision.
We are a 30-person data + AI consultancy with a senior-to-principal team and active partnerships with Snowflake, Omni Analytics, and Anthropic. We have built the patterns [Client] is going to need:
A unified analytics platform powering agentic and conversational workloads at Teladoc Health, the largest virtual care provider in the United States.
An agentic search experience at Kyruus Health that lets 150 million health plan members find providers by asking questions in natural language, and gets answers back and powers appointment scheduling.
An agent at Decision Resources that lets business process managers explore ERP data, enrich it with external context, and take action on forecasts and recommendations.
Astrodata proposes to integrate [Client]’s transactional systems across every key domain (sales, marketing, operations, etc.) in a cloud data warehouse to establish a Customer 360 and Enterprise 360 vantage point. A semantic layer will define contextual meaning and relationships between data elements, which is essential for both business and AI users to engage core datasets. Conversational and agentic AI will comprise the control plane allowing [Client] staff to analyze business opportunities and take action in transactional systems. Activation will be facilitated by a reverse ETL solution that allows [Client] staff, with AI support, to define data extracts (like customer segments or room pricing tables) and automate data movement back into transactional systems.
We are proposing an ongoing advisory partnership alongside a flexible build capability that activates when [Client] is ready. The advisory relationship is the constant, and build work happens in parallel, scoped to what the business needs and when it needs it.
A consistent Astrodata presence in [Client]’s working sessions, architecture decisions, and vendor conversations. We stay close enough to the business to know when the data is ready, when the organizational conditions are right, and when it’s time to move.
guest_uuid: which entities resolve to it,
which source systems feed it, which downstream agents and tools
read from it. Delivered as an entity relationship diagram plus a
written specification and implementation recommendations,
maintained as the business evolves.
Weekly working sessions with operational owners. Async working documents shared in real time. Periodic on-site visits in Mexico City or at the property. A standing readout cadence with the leadership team so strategy stays connected to execution.
$XXXXX per month. Either party can close the engagement with 30 days’ notice. Travel billed as reimbursable expenses if approved in writing.
Advisory work surfaces the right moments to build. When the data is ready, the use case is clear, and the business priority is aligned, we propose a build engagement. Build work is scoped and contracted separately, in parallel with the advisory relationship, and sized to what [Client] needs at that point in time.
The work below represents some of the initial projects we expect to build together, activated in the order that makes sense for the business.
guest_uuid resolution layer. Replace Power BI silos
with the unified semantic model. The first deliverable is a live
dashboard the Revenue Manager actually opens every morning.
Each build engagement is sized and contracted when we get there, informed by the advisory work and the conditions on the ground. Investment is scoped more accurately as each engagement is activated.
Astrodata staff may include senior or principal level resources in the following roles:
Most Astrodata clients start in one engagement model and end up using two or three across a year. That’s by design. The four models below are levers, not lanes, and they’re combinable. For [Client], we anticipate beginning with Strategic Advisory and layering in Project or Retainer work as build engagements come into focus.
Discovery, architecture audits, vendor evaluations, data model design, ROI scoping sprints.
Data foundation build, semantic layer, one agent shipped in human-in-loop mode with measurable lift against control.
Multi-agent revenue intelligence platform with reverse ETL, governed automation, and the commercial scoreboard live.
Combinable. The expected [Client] path is to start with Strategic Advisory, layer in a Fixed Bid Foundation project when the architecture is settled, then dial back to Advisory once execution is steady. Recommended sequencing for 2026 is in §06 below.
How we set these ranges. The four models are how Astrodata structures every client engagement, not custom built for [Client]. Hourly rates and retainer brackets reflect what comparable hospitality and SaaS clients have paid for similar work in the last 18 months. Project sizing brackets are calibrated against actual engagement scope: small is one or two senior architects for a discovery sprint, large is a multi-pod build with reverse ETL and an agent runtime in production. The board is welcome to push on any number here.
The shape of 2026 is three quarters of compounding work. Q2 is for architecture and the first measurable agent. Q3 is for the data foundation and the Revenue Agent in human-in-loop. Q4 is for guest-facing agents and the operational scoreboard. The pace is dictated by data readiness, not optimism.
By the end of year two, every revenue and guest decision flows through a unified data layer with a control plane built for the people making them. The stack moves from recommend-and-approve to governed-automation-within-policy for the patterns that have proven safe. By year three, [Client] has the architecture to absorb 10+ properties without re-platforming.
How we sequenced this. The quarters unlock each other on data readiness, not calendar. Architecture has to be settled before the foundation can land, the foundation has to be live before the Revenue Agent can run, and the Revenue Agent has to prove itself in human-in-loop before guest-facing agents inherit the same governance. The medium term assumes [Client] adds properties at the cadence stated in your 2026 technology strategy. If the cadence changes, we re-sequence.
Joshua asked the board-facing question directly: what does a successful engagement look like? Below, the focus areas from §03 paired with the KPIs we would watch and the directional impact ranges we would target. These are informed guesses based on patterns we have seen in adjacent industries. We will refine them against [Client]’s actual baselines in the first 30 days of the advisory engagement.
Daily revenue briefings, governed rate moves, and segmentation pushed back into Duetto. The Revenue Agent compounds across thousands of micro-decisions per week.
Replace Zapier sprawl with consolidated orchestration. Replace manual report-pulling with self-service. The same operations team runs a materially larger business.
Pre-Arrival Agent, WhatsApp co-pilot, and Voice Reservations bring the guest into a personalized conversation before, during, and after the stay, without growing front-office headcount.
With unified guest data and proper attribution, ad spend can be targeted against lifetime value instead of last click. JB’s prior work leading data at Better is the playbook.
How we got to these numbers. The KPIs are drawn from JB’s playbook leading data at Better and from the agentic analytics work in flight at Teladoc Health, Kyruus Health, and Decision Resources. The directional ranges are conservative midpoints from comparable deployments, weighted toward the lower end where [Client]’s data foundation is not yet in place. They are guesses today. They become specific [Client] targets in the first 30 days, against actual baselines.
How we validate. Each build engagement is gated on its measurable contribution to the scoreboard. ROI for the platform is computed against cumulative lift, not against any single project, and reviewed quarterly with the leadership team.
The technology decisions [Client] makes in the next 90 days will determine the cost and capability of the platform for the next five years. Picking the wrong warehouse, the wrong agent runtime, or the wrong reverse ETL approach is a six-figure mistake that compounds. The advisory relationship exists to make those decisions deliberately, with vendor-neutral analysis, before any code is written.
A scoped, sequenced plan with named dependencies cuts the discovery overhead out of the build.
With any build plans Astrodata delivers, [Client] owns a costed plan and can take it to any builder, including us. We earn the build work by delivering the advisory well.
LLMs are good at writing code but bad at creating system architecture. Define a plan and project roadmap into smaller, integrated components to enable AI-accelerated delivery of a cohesive and scalable system.
| Week | Milestone |
|---|---|
| 0 | Countersignature and kickoff |
| 1 to 3 | Discovery, system access, stakeholder interviews, on-property visit (optional) |
| 4 to 6 | Architecture options analysis, data model first draft |
| 7 to 8 | Agent roadmap, commercial scoreboard, on-property visit (optional) |
| 9 | First build engagement scoping |
| 10 | Leadership readout and advisory cadence established |
The largest virtual care provider in the United States. Astrodata embedded with Teladoc Health’s data and analytics teams to deliver a modern data platform purpose-built to support agentic workflows and conversational analytics on top. Snowflake architecture, dbt modeling of clinical, member, and revenue domains, Fivetran ingestion, and Omni Analytics enablement, all designed so that natural-language interfaces and AI agents read from the same governed data the BI team uses.
Snowflake · dbt · Fivetran · Omni Analytics
Connects 425,000 providers and 150 million health plan members. Astrodata partnered with Kyruus Health to build an agentic provider search experience. Members ask questions in natural language (“I need an endocrinologist who treats Type 1 diabetes, takes my plan, and has evening availability near me”) and the agent returns matched recommendations grounded in unified provider data. Provider, specialty, condition, plan, and availability data indexed in Elasticsearch as the retrieval layer; Google Gemini doing the reasoning; React for the member-facing experience.
Elasticsearch · React · Google Gemini
40+ years in business, 500+ manufacturing clients, top US Infor CloudSuite Industrial (SyteLine) ERP partner. Astrodata is building a conversational analytics experience that lets process managers explore SyteLine data, enrich it with context from web sources, and take action on forecasts and operational recommendations. Airbyte ingestion into Snowflake, dbt models for orders, inventory, production, supplier, and customer entities, custom agents reasoning over the modeled layer with grounded retrieval.
Snowflake · dbt · Airbyte · Snowflake Cortex · Omni Analytics · React