AI app for it and development · no coding needed
AI coding assistant usage and cost analysis workspace
Reduce manual reconciliation of AI coding assistant usage and cost while keeping session data under the buyer's control.
Made for: Engineering leaders and platform teams tracking AI coding assistant usage, cost and session activity
What it does for you
The problem
AI coding assistant spend and session activity sit across several vendor dashboards and local logs, so cost per task, model, project or pull request and the workflow signals behind it cannot be reviewed in one place.
What it gives you
Reviewed usage and cost analysis linked to named owners
What you give it
Permitted session logsvendor usage recordsrepository metadatateam mappings
Build your own version of CodeBurn, AI Spend Console by Rippling and more
One app with what these 4 AI tools do, yours to keep and change: CodeBurn, AI Spend Console by Rippling, ClawMetry for OpenClaw, Claude Code & Codex Usage Trading Cards by Rudel.
Everything these tools do, in one app
- Session usage analytics Aggregates token counts, model mix, and session details from AI coding sessions.Found in CodeBurn, Claude Code & Codex Usage Trading Cards by Rudel
- Cost breakdown by dimension Breaks down AI spend by task, model, project, vendor, employee, or pull request.Found in CodeBurn, AI Spend Console by Rippling, ClawMetry for OpenClaw
- Local data processing Runs entirely on the user's machine with no account or uploads required.Found in CodeBurn, Claude Code & Codex Usage Trading Cards by Rudel
- Open source Distributed as free, open-source software.Found in CodeBurn, ClawMetry for OpenClaw, Claude Code & Codex Usage Trading Cards by Rudel
- Session browser Displays session titles, timing, and costing information to find specific work sessions.Found in CodeBurn
- Workflow insights Shows how often the AI is corrected, time to first edit, and files reworked.Found in CodeBurn
- Multiple interfaces Provides CLI, desktop apps, menu bar widget, self-hosted web dashboard, and GNOME panel extension.Found in CodeBurn
- Waste optimization Identifies waste such as cache bloat or retry tax, applies fixes, and tracks savings.Found in CodeBurn
- GitHub output mapping Maps AI spend to GitHub metrics like pull request volume, code revisions, and code rework.Found in AI Spend Console by Rippling
- Custom dashboard Connects AI vendors, GitHub, and employee data to generate a custom dashboard.Found in AI Spend Console by Rippling
- Natural language queries Allows asking follow-up questions in natural language to drill into spend and usage patterns.Found in AI Spend Console by Rippling
- Shareable dashboards Enables sharing dashboards with anyone in the company with permissioning.Found in AI Spend Console by Rippling
- Time-based spend tracking Tracks AI spend over time with views like 'AI spend by vendor over time'.Found in AI Spend Console by Rippling
- Real-time agent dashboard Shows sub-agent activity, tool calls, and session timelines in real time.Found in ClawMetry for OpenClaw
- Context tracing Provides full context tracing for chained agent interactions, including logs of what each agent saw and decided.Found in ClawMetry for OpenClaw
- Zero-configuration install Quick integration with existing setup with minimal configuration.Found in ClawMetry for OpenClaw
- Behavioral classifier Assigns archetypes based on usage patterns from 20k+ sessions.Found in Claude Code & Codex Usage Trading Cards by Rudel
- Error and quality signals Highlights where sessions fail or produce low-quality outputs.Found in Claude Code & Codex Usage Trading Cards by Rudel
- Trading-card presentation Provides a compact, shareable summary that makes patterns easy to scan.Found in Claude Code & Codex Usage Trading Cards by Rudel
How it works, step by step
- Aggregate token counts, model mix and session details from permitted AI coding sessions
- Break down spend by task, model, project, vendor, employee and pull request
- Process session data locally with no account or upload required
- Run as open-source software the buyer can inspect and self-host
- Browse session titles, timing and costing to find specific work sessions
- Show correction frequency, time to first edit and files reworked
- Expose the same data through CLI, desktop app, menu bar widget, self-hosted web dashboard and panel extension
- Identify waste such as cache bloat or retry tax, apply fixes and track savings
- Map AI spend to repository metrics such as pull request volume, code revisions and code rework
- Connect AI vendors, repository and employee data into a custom dashboard
- Answer follow-up questions in natural language to drill into spend and usage patterns
- Share dashboards with named colleagues under permissioning
- Track spend over time with views such as spend by vendor over time
- Show sub-agent activity, tool calls and session timelines in real time
- Trace full context for chained agent interactions, including what each agent saw and decided
- Install with minimal configuration against the existing setup
- Assign usage archetypes from observed session patterns
- Highlight sessions that fail or produce low-quality outputs
- Present a compact, shareable summary that makes patterns easy to scan
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed usage and cost analysis linked to named owners with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for AI coding assistant usage and cost analysis workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds AI coding assistant usage and cost analysis workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links5 KB
- questions.mdQuestions to answer before you build3 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare28 KB
- prompt-vps.mdThe same build on your own server (Docker)28 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria14 KB
- demo/index.htmlThe working demo on sample data200 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce manual reconciliation of AI coding assistant usage and cost while keeping session data under the buyer's control. For engineering leaders and platform teams tracking AI coding assistant usage, cost and session activity, convert permitted session logs, vendor usage records, repository metadata and team mappings into a reviewed usage and cost analysis linked to named owners. The benefit is a testable hypothesis, measured through reconciled spend per accepted pull request and reviewer hours per reporting cycle; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect permitted session logs, vendor usage records, repository metadata and team mappings, then follow this sequence: 1. Aggregate token counts, model mix and session details from permitted AI coding sessions. 2. Break down spend by task, model, project, vendor, employee and pull request. 3. Show correction frequency, time to first edit and files reworked. Resolve uncertain cases with qualified reviewers, approve reviewed usage and cost analysis linked to named owners, and measure reconciled spend per accepted pull request and reviewer hours per reporting cycle against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Session data stays on the buyer's machine; cost attribution and quality judgments remain with the named owner. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, session privacy and usage permissions. Named owners approve attribution rules and reporting scope. One repository and one AI vendor; cost attribution and quality judgments remain with the named owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One repository and one AI vendor; cost attribution and quality judgments remain with the named owner. Implement one approved input format, a bounded representative case set and the first two task modules: aggregate token counts, model mix and session details; break down spend by task, model, project, vendor, employee and pull request. Support the third module with operator review: show correction frequency, time to first edit and files reworked. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Buyer-owned session logs, vendor usage exports, repository metadata and team directories. Cloud storage, repository import/export and reporting destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Data sources and permissions, Editable analysis workspace, Client report and delivery. Use a thumbnail gallery for reporting periods, a large central analysis canvas, and a right-hand panel for sources, constraints and comments. Let users compare periods and dimensions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant chart or session. Make the task-specific outcome reviewed usage and cost analysis linked to named owners visible beside its evidence, review state and value baseline.
