AI-powered data platform & self-serve analytics transformation

The Challenge

Summer Discovery ran two geographically separate entities on six disconnected systems spanning finance, marketing, sales, and operations, with no shared definition of what a metric meant across any of them.

Cross-entity reporting was done manually, reporting cycles lagged the decisions they were meant to inform, and finance and marketing had no way to get an answer without waiting on the data team’s queue.

Accordion delivered an AI-powered data platform: a governed Microsoft Fabric warehouse, built from the ground up so Anthropic’s Claude Opus 4.8 could reason over it accurately, with Claude integrated as the natural-language interface across finance, marketing, and sales.

Value Created

70 hrs saved

per month through Claude-powered, self-serve natural-language analytics.

8-week delivery

build accelerated end-to-end with Claude-powered coding agents across pipelines and testing.

3 teams self-serve

across finance, marketing, and sales, each with direct Claude Team access, no analyst queue.

AI-ready foundation

built on a documented semantic layer and skill files that let Claude reason accurately, extending to new entities.

“Summer Discovery leaned into AI early and eagerly, and that’s exactly the kind of partner Accordion is proud to build with.”

Our approach

A governed Microsoft Fabric warehouse, unifying six previously disconnected systems into a single, documented source of truth for Claude to reason over
Claude, Anthropic’s AI platform integrated as the natural-language interface across the entire data estate, not a dashboard bolted onto one
Claude-powered coding agents used to scaffold pipelines, transformation logic, and test cases, significantly compressing the build timeline
Skill files and instructions grounding Claude in the KPI matrix and Summer Discovery’s own business context, so Claude’s answers are accurate, not just fluent
A golden-set questionnaire, a curated set of representative business questions with known-correct answers, used to validate Claude’s accuracy before go-live
Roadmap in motion toward Claude-driven, agentic workflows that proactively flag anomalies rather than waiting to be asked

Phased roll-out

Phase 1:

Foundational AI & Claude-powered build

AI-ready data foundation and semantic layer built for Claude to reason over.

Claude-powered coding agents accelerate pipeline, transformation, and test development.

Phase 2:

Self-serve with Claude

Claude integrated for natural-language querying across finance, marketing, sales.

Close out:

Governed scale

Skill-file context layer built to extend Claude’s reach, with a roadmap toward Claude-driven, proactive workflows.