AI usage and cost evidence workbench

For

Engineering and finance teams accountable for AI model and coding-tool spend

Solves

AI usage and cost are spread across providers and tools, so teams cannot attribute spend, spot anomalies or reconcile invoices.

Delivers

Reviewed AI usage and cost evidence linked to owners and periods

Built in

about 4 weeks of creation time, MVP in 4 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce unverified AI spend while keeping a defensible record of usage and cost.

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.
4. Capture request-level tokens, latency, status and errors.
5. Flag unusual usage or cost behavior.
6. Flag unreliable or incorrect model outputs.
7. Shorten prompts before they reach the model to reduce token usage.
8. Recommend cheaper models, caching or prompt trimming.
9. Run A/B tests on live traffic and compare quality, latency and cost.
10. Benchmark model outputs with heuristics or LLM-based judges.
11. Version, review and deploy prompts with named owners.
12. Show usage totals beside other developers' stats.
13. Track active seats and overlapping tool adoption.
14. Match finance CSV vendor names against connected tools to find unmatched spend.
15. Label each figure as measured, allocated or modeled.
16. Track cursor activity in text editors.
17. Forecast usage and cost trends from historical data.
18. Compare the reviewed result with the recorded baseline and value assumptions.
19. Capture corrections and named-owner approval before consequential use.
20. Export versioned reviewed AI usage and cost evidence linked to owners and periods with source references and unresolved questions.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Provider usage records, tool seat data, request logs and finance exports

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect provider usage records

4. 3

   Tool seat data

5. 4

   Request logs and finance exports

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed AI usage and cost evidence linked to owners and periods

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, source versions, reviewer comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   4 days

   One buyer segment, one recurring use case; first modules: ingest usage and cost records from multiple AI providers and coding tools; show usage and cost metrics in a visual dashboard. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   10 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will engineering and finance teams accountable for AI model and coding-tool spend use it to solve "AI usage and cost are spread across providers and tools, so teams cannot attribute spend, spot anomalies or reconcile invoices"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Reconciled AI spend per period and unexplained cost variance.
4. Measure, then decide. Track reconciled AI spend per period and unexplained cost variance; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed AI usage and cost evidence linked to owners and periods. Retain the explicit scope boundary: One fixed reporting period and approved provider set; final cost attribution and reconciliation checks remain finance and engineering.

What the build depends on. Source upload and preview, asynchronous ingestion jobs, editable version history, reviewer access and tested export formats. High-fidelity reconciliation requires finance and engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed reporting period and approved provider set; final cost attribution and reconciliation checks remain finance and engineering.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined reporting period. Offer a monthly production allowance after repeat demand. Quote complex multi-provider or finance reconciliation separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed AI usage and cost evidence linked to owners and periods. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce unverified AI spend while keeping a defensible record of usage and cost. Demonstrate a concrete reviewed AI usage and cost evidence linked to owners and periods using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering and finance teams accountable for AI model and coding-tool spend professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed AI usage and cost evidence linked to owners and periods from a small authorized input set, with a transparent calculation of reconciled AI spend per period and unexplained cost variance and no promised savings.

The first 30 days

1. Week 1: interview five engineering and finance teams accountable for AI model and coding-tool spend and inspect a recent example of AI usage and cost spread across providers and tools, so teams cannot attribute spend, spot anomalies or reconcile invoices.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure reconciled AI spend per period and unexplained cost variance, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Reconciled AI spend per period and unexplained cost variance. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Reconciled AI spend per period and unexplained cost variance; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed AI usage and cost evidence linked to owners and periods. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved source mappings, attribution rules and review examples, together with reliable delivery for a narrow engineering and finance niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering and finance teams accountable for AI model and coding-tool spend. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Siloam AI (alpha), Edgee, whoburnedmore, TensorZero, Editor Usage for Cursor, Tokenwise, GPT Analytics, DepthData and Langfuse Custom Dashboards, plus spreadsheets and manual provider exports. Compare this product with the buyer's present method on reconciled AI spend per period and unexplained cost variance. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Provider API calls, log storage, reviewer hours, client revision rounds and licensed source data. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed AI usage and cost evidence linked to owners and periods. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.