Executive briefing

Get the data out of the source systems, into one place the client owns, and back out as answers.

A reference data platform engagement: pull data out of the source systems, land it in a warehouse the client owns, model it bronze → silver → gold against the measurable outcomes, and serve it back through dashboards and an AI query layer.

Source systems

Data sources mapped

Semantic models

Tracked metrics

Client

Multi-Site Operating Company

Multi-site portfolio (region withheld)

Owner / PrincipalEconomic buyer — outcome and budget owner
Finance LeadSystem admin — budgets, GL, reporting
Site ManagersReport consumers — per-site operators

Objectives

One consolidated view of budget vs actuals across every site.
Per-unit expense benchmarking by category to expose cost outliers.
Vendor rate comparison to drive renegotiation and bulk contracts.
Self-serve, read-only reporting for site managers.

Context

“We don't touch code until we understand the measurable outcome, then we reverse-engineer it back to the source.”

Architecture scales to N sites with no structural change

Providers: Managed IT provider (infrastructure), data engineering partner (platform)

1. Map

One-time consultative fee

No code is written until the measurable outcomes are agreed and reverse-engineered back to the technology landscape. Map produces this document.

Technology landscapeOutcome definitionsSource feasibilitySemantic model draft

2. Serve

Fixed-scope project

Run the lines: connect the sources to the warehouse, model the data, and finish with a working dashboard and report set the team can consume.

PipelinesWarehouseBronze / silver / gold modelsDashboards & reports

3. Manage

Optional recurring retainer

Keep the water flowing. Version-controlled pipelines, monitored source API changes, triaged breakages, and model changes on request.

Pipeline monitoringSchema-change triageModel extensionsEmbedded data engineering

What gets installed

Plumbing · Modeling · Reporting · AI

Plumbing — ingestion pipelines

Version-controlled connectors that extract data out of each source system on a schedule. Because the pipeline is owned in code, a source schema change is detected and triaged rather than silently breaking a report.

Modeling — warehouse & semantic layer

Everything lands in one relational warehouse, then moves bronze (raw) → silver (conformed) → gold (business-ready). This is the slowest stage by design: the end goal is defined first and reverse-engineered back to the source.

Reporting — dashboard layer

An open-source dashboard tool is included with the platform — not a per-seat SaaS subscription. Read-only logins, scheduled delivery, and export are part of the build.

AI readiness — query layer

Because the warehouse is a widely supported open database, an LLM can query it through a standard model-context protocol. Answer quality depends on the semantic layer: without defined business nouns, a model guesses.

Managed cloud — Fastest to stand up; partner-operated, client-owned data.Client cloud tenancy — Deployed into the client's own cloud account and billing.On-premise / colocation — For clients who require physically owned hardware.

Architecture — source systems to decisions

Alpha to omega

Core problem

The data is reachable one entity at a time, so nothing can be compared. Planning cycles run for months, managers can't read their own numbers, and there is no cross-site visibility for cost control.

Key constraint

The economic buyer is a “show me” decision maker: a working deliverable comes before any ongoing commitment, and demonstrated value has to clearly exceed the investment.