Efficiency Model

Where is your AI output actually going?

Most organizations can tell you what they spend on AI. Far fewer can tell you which teams, agents, and workflows that spend went to — or how many engineering hours disappear into reviewing sessions and assembling reports. Put your own numbers in and see the shape of it.

Your numbers

Tell us about your organization.

The calculation runs entirely in your browser. Nothing is sent anywhere unless you request the business case below.

Engineers & analysts using AI 40

Current AI & token spend — monthly $60,000/mo

Enter what you spend per month. Every result is annualized (× 12) — that comes to $720,000 a year at this setting.

Agents running in production 25

AI-ops hours per person, per week 4 hrs/wk

Time spent reviewing agent sessions, chasing down cost questions, and assembling reports by hand.

Blended fully-loaded rate $95/hr

Model assumptions — change any of these

AI spend currently unattributed 60%

Of that, share recoverable once visible 20%

AI-ops toil removable by automation 50%

AgentOS engineering output multiplier 5×

Drives the engineering-output figure only — it is deliberately kept out of the dollar total above, which covers recoverable spend and returned hours. Set it to 1× for no multiplication.

These defaults are our starting hypotheses, not measured results — Daiahn is pre-launch. Set them to whatever you believe about your own environment, including zero (or 1× for the multiplier). The model is arithmetic on your inputs; there is no hidden multiplier.

Modeled efficiency gain · per year $0

Annual figure — about $0 per month. It combines recoverable AI spend with the value of engineering hours returned to your team, based on the numbers you entered.

$0 Recoverable AI spend / yr

0 Engineering hours returned / yr

0 Equivalent full-time engineers

$0 Spend you can't currently attribute

$0 Annual spend per production agent

5 days Engineering work delivered per day, minimum

For the budget conversation

The version your CFO will read.

We'll turn the scenario you just built into a one-page business case — your inputs, the math in full, and every assumption on the record. Written to survive scrutiny, and yours to forward without edits.

First name

Last name

Work email

// Illustrative model, not a forecast or a guarantee. Daiahn is pre-launch, so the three default assumptions are stated hypotheses rather than measured customer outcomes — every one is editable above. Your results are computed locally in your browser. Nothing is transmitted unless you request the written business case, which shares the figures you modelled so it can be written around your scenario.

Closing the gap

What actually turns those numbers around.

The model above is only as interesting as the mechanism behind it. Three things move it.

01

Attribution before optimization

You can't right-size spend you can't trace. Agent Intelligence attributes every dollar to a team, workflow, agent, and model — which is what makes the recoverable share actionable rather than theoretical.

02

Rollups instead of hand-assembly

Most AI-ops toil is people rebuilding the same report. Cross-session rollups render automatically as standard notebooks, so the weekly reporting scramble stops being a person's job.

03

Deterministic, not billable, analytics

Most Daiahn analytics are pure SQL over a lossless event store — reproducible, auditable, and free per query. You aren't paying inference costs to find out what your inference costs were.

04

Output multiplied, not just measured

Measurement shows you the gap; AgentOS Engineering closes it by embedding developer agent teams inside your existing repos and workflows to clear backlog without adding headcount.

See it on your own traces

Run this against your real data.

A founder-led walkthrough using the kind of sessions your teams actually run — where the modeled numbers above get replaced by measured ones.

Request a demo Schedule an executive briefing