# Wire Framework, AI & Agentic Analytics Delivery Blueprints | Rittman Analytics URL: https://rittmananalytics.com/services/wire-framework Description: The Wire Framework is Rittman Analytics' opinionated, production-ready dbt and Dagster blueprint for building modern, AI-ready data platforms, faster, with fewer mistakes. --- New · v3.4.20 AI-accelerated delivery, from SOW to dashboards. # Build data platforms in days, not weeks. Wire is the delivery framework that turns Claude Code and Gemini CLI into a senior analytics engineer that already knows your conventions. Every artifact — from requirements to LookML — generated, validated and reviewed against the same playbook we’ve refined over 20+ years. [Read the Docs](https://docs.rittmananalytics.com/) [Read our Case Study](https://rittmananalytics.com/case-studies/barton-peveril) 10 Release types 15 Artifact types ~25min Autopilot SOW → run acme-platform · 01-data-foundation $ /wire:requirements-generate 01-data-foundation → reading engagement/context.md + sow.md → applying spec workflows/requirements/generate.md ✓ generated requirements\_specification.md · 13 sections $ /wire:requirements-validate 01-data-foundation ✓ 13/13 checks passed $ /wire:requirements-review 01-data-foundation ⌛ awaiting sponsor approval requirements conceptual\_model data\_model dbt semantic\_layer dashboards Built on the stack you already trust ![Claude](https://rittmananalytics.com/wire-framework/assets/logo-claude.png) ![Gemini](https://rittmananalytics.com/wire-framework/assets/logo-gemini.png) ![dbt](https://rittmananalytics.com/wire-framework/assets/logo-dbt.png) ![BigQuery](https://rittmananalytics.com/wire-framework/assets/logo-bigquery.jpg) ![Looker](https://rittmananalytics.com/wire-framework/assets/logo-looker.png) ![Fivetran](https://rittmananalytics.com/wire-framework/assets/logo-fivetran.webp) ![Dagster](https://rittmananalytics.com/wire-framework/assets/logo-dagster.svg) The challenge ## The Data Team's Dilemma: Speed vs. Trust Traditional analytics delivery is broken. Business leaders want answers yesterday, but the process is slow and fraught with risk. ### Your Challenges: - **Moving too fast** leads to inconsistent metrics, broken dashboards, and a gradual erosion of trust from the very stakeholders you're trying to serve. - **Moving too slowly** means missed opportunities, a frustrated business, and a data team that is perpetually seen as a bottleneck, not a strategic partner. - **AI is not a magic bullet**, used incorrectly, it just creates higher volumes of low-quality work. Used strategically, it can transform your team's entire operating model. ### Our Solution: AI-Augmented Process, Human-Led Judgment Our approach uses AI to automate repetitive tasks and accelerate feedback loops, freeing your team to focus on what matters most: complex business logic and stakeholder collaboration. #### Proof in Practice: See how we partnered with Barton Peveril College to deliver an AI-accelerated MVP analytics platform in just 15 working days, pioneering a new approach for UK education. [Read More →](https://rittmananalytics.com/case-studies/barton-peveril) ![Barton Peveril College students](https://rittmananalytics.com/lovable-uploads/barton-peveril-science-lab.png) Barton Peveril The problem we’re solving ## AI code generation gets the SQL right. It gets the methodology *wrong*. Drop an AI into a dbt project with no scaffolding and you get models that compile, then naming chaos, missing tests, drifting grain, all the things a senior analytics engineer wouldn’t miss. Without Wire ### Improvisation at every step The model knows the conventions. It just isn’t held to them. The result is technically valid SQL that, over 15 models, drifts in ways that erode the value proposition entirely. - Inconsistent naming across `stg_`, `int_`, `wh_` layers - Missing surrogate-key patterns and grain management - FK relationship tests skipped on half the models - Requirements untraceable to warehouse columns - Each engagement is a one-off with no compounding With Wire ### Generation, constrained. Each command reads its workflow specification before generating. The spec dictates upstream inputs, naming, structure, validation checks, and how state is updated. Every artifact looks like the team has been on the project for months. - Encoded conventions for dbt, LookML, pipelines and docs - Automatic prerequisite enforcement between artifacts - Validation is mechanical: pass / fail with named failures - Research findings persist across sessions and releases - Every engagement compounds onto the same framework Release types ## Ten shapes of engagement. One framework. Discovery to scope it. Ten delivery types for everything from a one-week dbt sprint to a full platform build. Agentic Commerce for AI-powered storefronts. Pick the type; the framework instantiates the right artifact queue. #### Discovery Shape Up planning: problem → pitch → release brief → sprint plan. 1–2 wk**4 artifacts** #### Full Platform SOW → production dashboards + trained users. End-to-end. 2–3 wk**15 artifacts** #### Dashboard-First Interactive mocks drive the data model; seed data enables immediate dbt. 1–2 wk**14 artifacts** #### Pipeline + dbt New pipeline plus dbt transformation layer; BI out of scope. 1–2 wk**7 artifacts** #### dbt Development Analytics engineering on existing pipeline infrastructure. 1 wk**5 artifacts** #### Dashboard Extension New dashboards on top of an existing, populated semantic layer. 3–5 d**4 artifacts** #### Enablement Training and documentation for an existing platform. 2–3 d**2 artifacts** #### Agentic Commerce AI-powered ecommerce: Lovable + 9 AI features (search, try-on, assistant). 1–4 wk**9 artifacts** #### Data Migration Lift, validate and cut over legacy warehouses, pipelines and dashboards with rollback coverage. 1–3 wk**8 artifacts** #### Platform Health Check Rapid audit of dbt, pipelines and dashboards with a prioritised backlog of fixes and quick wins. 2–3 d**3 artifacts** How it works ## How AI-Augmented Delivery Works Our methodology embeds AI into every stage of the analytics lifecycle, while keeping your expert team in complete control. ![AI-generated dashboard mockup](https://rittmananalytics.com/lovable-uploads/ai-accelerated-prototyping.png) ### Accelerated Prototyping Use AI to generate initial data models and dashboard mockups directly from your requirements, making stakeholder conversations concrete and productive from day one. ![Governance and code review process](https://rittmananalytics.com/lovable-uploads/ai-governance-speed.png) ### Governance at Speed Ensure every AI-generated asset passes through clear, human-led review gates. We maintain 100% explainability, semantic clarity, and trust in your final data products. ![Accelerated AI delivery timeline](https://rittmananalytics.com/lovable-uploads/ai-faster-iteration.png) ### Faster Iteration Instantly generate boilerplate code, documentation, and tests, allowing your engineers to focus on solving unique business problems, not on repetitive, low-value tasks. ![Collaborative review and validation](https://rittmananalytics.com/lovable-uploads/ai-deeper-collaboration.png) ### Deeper Collaboration Free your team from waiting on slow builds and manual processes. More time is spent with stakeholders validating insights and less time debugging pipelines. Why Wire ## Three gates, every artifact. Every artifact follows the same three-gate pattern. Substitute `` with `requirements`, `conceptual_model`, `dbt`, anything in the queue. The next gate doesn’t fire until the previous one passes, phase discipline is structural, not a checklist someone might skip. Gate 1 ### Generate AI reads the spec and upstream inputs, produces the artifact. Files land in the release folder. Gate 2 ### Validate Automated checks against conventions. **PASS** or **FAIL**, with the specific failures listed. Gate 3 ### Review You or the stakeholder approves. Status moves to `approved` or `changes_requested`. Each gate updates `status.md` and appends to `execution_log.md`. Nothing happens off the books, so a new team member picks up an engagement and is productive within an hour. VS Code extension ## Wire, right inside your editor. The Wire VS Code extension surfaces every `/wire:*` command, every artifact gate state, and the running execution log in the editor you already live in. It shares state with the CLI, so you can switch between them mid-engagement without a beat. ##### Status sidebar Every artifact’s gate state visible in a dedicated side-bar pane, one click to open the file. ##### Command palette `⌘⇧P` → type “Wire”. All 67 commands one keystroke away, with the release IDs auto-completed. ##### Inline terminal output Generate, validate and review run inside the editor terminal, with syntax-highlighted validation reports. ##### Live status.md The release’s `status.md` updates in real time as you run commands. Diff-friendly, git-friendly, review-friendly. Install Search *“Wire Framework”* in the VS Code Marketplace, or run `code --install-extension rittmananalytics.wire`. Requires VS Code 1.98+ and Claude Code. status.md · acme-platform Wire · 01-data-foundation requirements approved conceptual\_model approved data\_model running pipeline queued dbt semantic\_layer dashboards Files status.md execution\_log.md 1\--- 2release\_id: "01-data-foundation" 3release\_type: "full\_platform" 4artifacts: 5 requirements: 6 generate: complete 7 validate: pass 8 review: approved 9 conceptual\_model: 10 generate: complete 11 validate: pass 12 review: approved 13 data\_model: 14 generate: in\_progress 15\--- 17\# Release status 18 19Day 3 of 15. Requirements + conceptual 20model signed off by Priya on Monday. Wire Autopilot ## Hand it the SOW. Walk away. For standard engagement shapes, Autopilot takes a Statement of Work and runs the entire lifecycle, discovery sprint, then every downstream delivery release the discovery identifies, autonomously. - **Eight context questions** upfront, client name, repo mode, tracker, document store. No release-type guessing. - **Self-validates and self-approves** internal artifacts, with up-to-three retries on validation and self-review. - **Safety gates** before any phase that touches external systems, pipeline activation, data refactor, deployment. - **Resumable from checkpoint**, interruptions cost minutes, not a re-run. #### autopilot · acme-platform.sow.pdf Phase 1 · Discovery sprint · in progress ✓ problem\_definition10 sections · auto-approved Complete ✓ pitchAppetite: 3 wk · 3 downstream releases identified Complete ↻ release\_briefValidate (attempt 1 of 3) Running · sprint\_planQueued Queued ! pipeline · safety gateWill pause before activating Fivetran Gate Worked example · Barton Peveril ### Live pastoral analytics, end-to-end, in 8 working days. Barton Peveril Sixth Form College needed a live pastoral-risk dashboard on top of ProSolution MIS and Focus, with safeguarding-grade data handling and same-week training for SPAs and pastoral leads. One `full_platform` release. 15 artifacts. SOW signed on Monday; dashboards live and end-users trained by the following Wednesday. [Read the case study](https://rittmananalytics.com/#) 8 d Working days · SOW to live 9 dbt models · 47 tests 100% PK + FK test coverage £7.1k Budget · 35 hours Built-in integrations ## Sits inside the tools your team already uses. Wire ships as a plugin for Claude Code and an extension for Gemini CLI. State is plain markdown in git. Integrations are MCP-native — one command to authenticate, then state syncs automatically. ![Fivetran](https://rittmananalytics.com/wire-framework/assets/logo-fivetran.webp)![Dagster](https://rittmananalytics.com/wire-framework/assets/logo-dagster.svg) Ingestion + orchestration Fivetran · Dagster Pipeline design + cost analysis. Connectors verified before generate; assets-first Dagster. Issue tracking Jira · Linear Bidirectional sync from `status.md` to your tracker. Fail-graceful — never blocks delivery. Document store Confluence · Notion Every approved artifact auto-publishes to your client’s space. Discovery docs reviewable in their tooling. Meetings Fathom MCP `utils-meeting-context` pulls transcripts into the engagement before every call. ## Start your next engagement on Wire. Book a 30-minute walk-through and see Wire run end-to-end on one of your SOWs, or one of ours. Take the recording away whether or not we work together. [Book a 30-min walk-through](https://rittmananalytics.com/#) [Read the docs](https://docs.rittmananalytics.com/) #### What you’ll see - SOW → live dbt + LookML in under 30 minutes via Autopilot. - The exact validation rules a Rittman senior would apply. - How the same playbook runs across BigQuery and Snowflake. - How to roll Wire into your own delivery team.