Valistry

Find the AI changes worth making.

Pick the right model for each use case, backed by benchmarks and ranked by savings. Make the change yourself or let an agent, then verify what improved.

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  • Ranked by expected value and quality risk
  • Right model per use case, by benchmarks
  • Savings verified after the change
Scenarios
Scenarios: change the assumption, watch the forecast move

A recommendation is not a saving.

Every opportunity moves through one accountable path: identified, modeled, approved, implemented, and verified against the original baseline.

Identified

$17,093 a month found

Found in your own runs, with the evidence.

Modeled

$15,481 a month waiting on you

Expected value, confidence and quality risk.

Approved

2 · R. Mehta, J. Chen

A person decides. The assumption goes on the record.

Implemented

2 changes made

Counting starts the day a change goes live.

Verified

$1,314 a month

Measured after the change, on the same ledger.

  • Ranked by value

    Each opportunity with its evidence, confidence and quality risk.

  • Tried before it is live

    Model a change on your own history first.

  • Verified, not promised

    A saving counts once it is measured.

Dev productivity, measured, not scored.

Early signals

Whether AI helps each team: its pull requests against a person's, matched on repository, size and week.

  • Cycle time, review wait and rework
  • By team, AI tool, repository or PR size
  • Each change checked after
  • No score, only counts
How it is measured →
Early signals, team by team
Early signals: each team's AI-assisted pull requests against its own, like for like

Model choice and retries change AI cost.

The same task costs more or less by model, prompt and retry. Here are 46 ways to cut it, checked on your own runs.

Model choice

8

The right model for the work, at the best price for it.

  • Model on evidence
  • Same model, cheaper provider
  • and 6 more

Prompts and caching

9

Fewer tokens sent, fewer tokens returned. Pay once for what repeats.

  • Prompt size
  • Context growth
  • and 7 more

How calls run

9

Stop paying for retries, failures, and work that can wait.

  • Retries
  • Failed tasks
  • and 7 more

Seats and licenses

4

Pay only for seats people use.

  • Unused seats
  • Seat plan
  • and 2 more

Buying and limits

6

Use what you prepaid before it expires. Set a limit, and know before spend passes it.

  • Commitments and credits
  • List price at volume
  • and 4 more

Value

6

Cost measured against the work it did.

  • Cost per successful outcome
  • Found by your team
  • and 4 more

Infrastructure and data

4

Hosted models and the services around them. Vector stores, embeddings and the chunks a prompt reads.

  • Hosted models billed while idle
  • Fine-tuned models nobody calls
  • and 2 more

Opportunity to verified saving.

Savings ranked by what they are worthTo Jira or Slack
Opportunities: evidence-backed, with the decisions taken on them
Every known lever, and what each foundThe industry's name for each
Every lever: each lever in its group, with what it found on your own records
The same work on open models, and its costBacked by benchmarks
Open models: your own tokens re-priced on published-weight models, with benchmark evidence
AI cost per customer case resolvedFrom your help desk
Support: what one resolved case cost, and how many were reopened
Every decision appended, nothing editedMeasured after the change
Ledger: every decision appended, nothing claimed before it is measured
Opportunities, explained →Every Optimize screen, explained →

Cut what does not pay. Grow what does.

Connect one source and review your first opportunity.

No prompts or replies by default · Checked against your bills

Once a saving is verified, the next questions are how to keep it, and who may use what.

04Next: Govern →