AI app for it and development · no coding needed
AI usage and cost evidence workbench
Reduce unverified AI spend while keeping a defensible record of usage and cost.
Made for: Engineering and finance teams accountable for AI model and coding-tool spend
What it does for you
The problem
AI usage and cost are spread across providers and tools, so teams cannot attribute spend, spot anomalies or reconcile invoices.
What it gives you
Reviewed AI usage and cost evidence linked to owners and periods
What you give it
Provider usage recordstool seat datarequest logsfinance exports
Build your own version of Siloam AI (alpha), Edgee and more
One app with what these 9 AI tools do, yours to keep and change: Siloam AI (alpha), Edgee, whoburnedmore, TensorZero, Editor Usage for Cursor, Tokenwise, GPT Analytics, DepthData, Langfuse Custom Dashboards.
Everything these tools do, in one app
- Usage and cost tracking Shows how much AI usage is happening and what it costs.Found in Siloam AI (alpha), Edgee, whoburnedmore and 4 more
- Multi-provider support Works across multiple AI model providers or AI tools in one place.Found in Siloam AI (alpha), Edgee, whoburnedmore and 2 more
- Usage dashboards Displays usage and cost metrics in a visual dashboard.Found in Edgee, whoburnedmore, TensorZero and 2 more
- Cost attribution Attributes AI spend to specific features, teams, or people.Found in Edgee, Tokenwise, DepthData
- Request-level logging Captures details of each AI request, such as tokens, latency, status, and errors.Found in Tokenwise, TensorZero, Langfuse Custom Dashboards
- Anomaly detection Flags unusual or unexpected AI behavior.Found in Siloam AI (alpha)
- Hallucination detection Flags unreliable or incorrect model outputs.Found in Siloam AI (alpha)
- Token compression Shortens prompts before they reach the model to reduce token usage.Found in Edgee
- Optimization recommendations Suggests fixes like cheaper models, caching, or prompt trimming to reduce cost.Found in Tokenwise, TensorZero
- A/B testing Lets you test changes on live traffic and compare quality, latency, and cost.Found in Tokenwise, TensorZero
- Evaluation tools Benchmarks model outputs using heuristics or LLM-based judges.Found in TensorZero, Langfuse Custom Dashboards
- Prompt management Supports versioning, collaboration, and deployment of prompts.Found in Langfuse Custom Dashboards
- Public leaderboard Shows usage totals alongside other developers' stats.Found in whoburnedmore
- Seat and adoption tracking Tracks who has active seats and where tools overlap.Found in DepthData
- Expense reconciliation Matches finance CSV vendor names against connected tools to find unmatched spend.Found in DepthData
- Data source labeling Labels each figure by how it was obtained (e.g., measured, allocated, modeled).Found in DepthData
- Cursor movement analytics Tracks and visualizes cursor activity in text editors.Found in Editor Usage for Cursor
- Predictive analytics Uses machine learning to forecast trends from data.Found in GPT Analytics
How it works, step by step
- Ingest usage and cost records from multiple AI providers and coding tools
- Show usage and cost metrics in a visual dashboard
- Attribute spend to features, teams and named people
- Capture request-level tokens, latency, status and errors
- Flag unusual usage or cost behavior
- Flag unreliable or incorrect model outputs
- Shorten prompts before they reach the model to reduce token usage
- Recommend cheaper models, caching or prompt trimming
- Run A/B tests on live traffic and compare quality, latency and cost
- Benchmark model outputs with heuristics or LLM-based judges
- Version, review and deploy prompts with named owners
- Show usage totals beside other developers' stats
- Track active seats and overlapping tool adoption
- Match finance CSV vendor names against connected tools to find unmatched spend
- Label each figure as measured, allocated or modeled
- Track cursor activity in text editors
- Forecast usage and cost trends from historical data
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export versioned reviewed AI usage and cost evidence linked to owners and periods 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 usage and cost evidence workbench 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 usage and cost evidence workbench 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 links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 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 unverified AI spend while keeping a defensible record of usage and cost. For engineering and finance teams accountable for AI model and coding-tool spend, convert provider usage records, tool seat data, request logs and finance exports into reviewed AI usage and cost evidence linked to owners and periods. The benefit is a testable hypothesis, measured through reconciled AI spend per period and unexplained cost variance; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect provider usage records, tool seat data, request logs and finance exports, then follow this sequence: 1. Ingest usage and cost records from multiple AI providers and coding tools. 2. Show usage and cost metrics in a visual dashboard. 3. Attribute spend to features, teams and named people. Resolve uncertain cases with qualified reviewers, approve reviewed AI usage and cost evidence linked to owners and periods, and measure reconciled AI spend per period and unexplained cost variance against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed reporting period and approved provider set; final cost attribution and reconciliation checks remain finance and engineering. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, cost accuracy and usage permissions. Finance and engineering owners approve substantive changes and reporting scope. One fixed reporting period and approved provider set; final cost attribution and reconciliation checks remain finance and engineering. 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 fixed reporting period and approved provider set; final cost attribution and reconciliation checks remain finance and engineering. Implement one approved input format, a bounded representative case set and the first two task modules: ingest usage and cost records from multiple AI providers and coding tools; show usage and cost metrics in a visual dashboard. Support the third module with operator review: attribute spend to features, teams and named people. 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
Provider billing APIs, coding-tool seat exports, request log pipelines and finance CSV exports. Cloud storage, identity provider for reviewer access and BI 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: Source and connection setup, Editable usage and cost workspace, Review and export. Use a thumbnail gallery for reporting periods, a large central dashboard canvas, and a right-hand panel for sources, labels and comments. Let users compare periods and providers side by side. Display draft, changes requested and approved states. Provide a reviewer link with comments anchored to the relevant figure. Make the task-specific outcome reviewed AI usage and cost evidence linked to owners and periods visible beside its evidence, review state and value baseline.
