The platform for your AI workforce Enterprise

A better way to run your AI workforce.

Your agents already ship real work. airis gives every run an owner, a goal and a record: what it cost, whether it met the acceptance criteria, and what to change before the next run.

Book a demo See the platform

01 Lower cost
02 Higher quality
03 Faster delivery

A connected view of agent work airis, running on agentacct · Sample data

airis Engineering 3 teams · 24 agents · 252 sessions

Engineering overview

Move your team’s goals forward.

August spend
$26,748.48

GoalIn progress

Ship changes faster and safely

CI candidate awaits review; release automation is adopted.

3 workstreams · $7,568.94

GoalAt risk

Ready for enterprise customers

Billing correctness is the critical constraint.

2 workstreams · $11,981.64

GoalAt risk

Make the product fast and reliable

Search latency needs work; the canary is still under review.

1 workstream · $7,197.90

What needs to move next

Billing has a reproducible failure. Give the next agent a recovery plan · Alex owns it

Two goals need a better path to done2 at risk

Your agent workspace

Recorded sessions252

Named agents24

Every run adds contextRecorded

Shared team records · source access by policy Sample data · September snapshot

Captures work from

Claude Code
Codex
OpenCode
One usage basis across the tools your team runs. Coverage differs by client; missing evidence stays visible.

01 / 09 Why airis

You can see what your agents cost. Not what they delivered.

Provider bills stop at the total. airis connects that spend to the goals, the work and the people behind it, and keeps the accepted result next to the cost, so a cheaper failure never reads as a win.

What a provider bill shows

One line · August $26,748.48 No goals, no owners, no outcome.

What airis adds

Spend attributed to goals, teams, people and agents
Accepted work counted separately from reported work
Repeated effort surfaced as a cohort you can act on
Cost per accepted task, not cost per token

GoalSpendAcceptedPer accepted task

Ready for enterprise customers Billing parity · Alex

$11,981.64

14 / 19 $855.83

Ship changes faster and safely Faster CI/CD · Frank

$7,568.94

21 / 23 $360.43

Make the product fast and reliable Search quality · Sam

$7,197.90

9 / 16 $799.77

Repeated repository discovery 62 sessions, mostly Faster CI/CD · Skill opportunity

$4,045.58

Modeled Not realized

Sample data · Usage estimate, not an invoice · Acceptance from recorded checks and review

02 / 09 The proof

The same tasks. A stronger plan.

We show, we don’t tell. Take one repeated failure, turn it into a reviewed change, and run it against the exact same tasks. Here is a sample cohort of 12 paired CI tasks, with unfinished trials kept in the count.

Reference sandbox · 12 paired CI tasks Sample

Prepared change vs. current practice

Same inputs · same acceptance

Tasks accepted

7 10 / 12

+3 on the same inputs

Time to decision

2h 29m 1h 44m

30% less, 3h cap

Cost per accepted task

$48.12 $26.96

44% lower, retries included

Input, revision, environment and acceptance held constant. Unfinished trials and four held-out cases stay in.

See the plan behind it

03 / 09 Three outcomes

Cost, quality and speed, measured on the same work.

One workstream, Faster CI/CD at airis, read three ways. The same tasks, the same acceptance criteria, the same environment.

01 Lower cost

Cut the cost of wasted work.

See where the budget goes, down to the team, task and agent. Find repeated effort and expensive detours, then turn those patterns into changes you can evaluate before you rely on them.

Cost intelligence

Cost by goal · August Sample

$26,748.48 Engineering · 3 goals

Ready for enterprise customers $11,981.64

Ship changes faster and safely $7,568.94

Make the product fast and reliable $7,197.90

Reusable repository context 62 eligible sessions · same acceptance criteria

$4,045.58/mo Modeled

02 Higher quality

Make the goal the measure of good work.

Give agents a clear definition of success. Connect each task to acceptance criteria, recorded checks and human review, so work that misses the mark is visible before it ships, and the next run starts from a better brief.

Execution quality

Faster CI/CD · Acceptance In review

Agent report “Complete”

Recorded evidence 2 checks · 1 pending review

7m 42s median CI run Target under 8 minutes · scoped to the candidate

Recorded

248 / 248 suites retained exit 0 · final suite remains required

Checked

Peer review & rollout William reviews next · a person decides this row

Pending

A passing check supports its stated scope. It is not proof that all work is correct.

03 Faster delivery

Less waiting. More work moving forward.

Repeated setup, duplicated context and incomplete handoffs slow work down. airis shows where the time goes and lets you prepare a better sequence: reuse verified context, run independent checks in parallel, hand over a complete review packet.

Delivery speed

Implement dependency caching · Timeline Sample

Current 4h 05m

Discover context

Implement

Validate (full suite)

Wait for review

Proposed 2h 20m Modeled

Reuse verified context

Implement

Independent checks (parallel)

Complete review packet

Same acceptance conditions in both arrangements · a planning scenario, not measured savings

04 / 09 How airis works

One record, from captured run to reusable lesson.

Usage, work and recorded checks are captured from each agent session, attached to the people, tasks and goals behind them, read as cost, quality and speed, and turned into playbooks the next run can reuse.

Sources

Claude Code
Codex
OpenCode
CI & checks
Repositories

Capture

Usage records
Work sections
Recorded checks
Capture source kept

Work record

People & agents
Tasks
Workstreams
Goals

Outcomes

Cost, attributed
Quality, with evidence
Speed, end to end

Improve

Skills
Workflows
Hooks
Team playbooks

Reviewed changes apply to future runs, then get compared on the same criteria

What a green check means here, and what it does not.

airis is built on discipline about evidence, so the vocabulary is strict. Six distinctions the product never blurs, on any screen.

01A report is not a check.: An agent saying “done” is a report. A recorded command with its exit code and capture source is a check. Both are stored, neither is confused for the other.
02A check is not a review.: A passing check supports its stated scope. A person accepting the work is a review, and the record keeps that decision separate.
03An estimate is not an invoice.: Usage comes from client records with its pricing basis attached. When a link or a number is missing, the gap stays visible instead of being filled in.
04Modeled is not realized.: Optimization potential is a cohort and a proposal. Nothing counts as a saving until the change is applied and compared on the same tasks.
05Visibility is not sharing.: Organization visibility does not mean every conversation is shared. Source access is a policy, and it is shown next to the work it governs.
06A proposal is not a change.: Generated skills, workflows and hooks change no runner until a person reviews, applies and compares them.

05 / 09 The improvement loop

Improvement is a reviewed change, not a setting.

This is the method behind the proof. Find the repeated pattern, prepare a scoped skill, workflow or hook, compare it with current practice on equal terms, and apply what holds up. Nothing changes a runner until a person applies it.

01 / Understand
Find the pattern.

The same repository is explored again and again across 62 sessions in the CI workstream.

Repeated effort · $4,045.58/mo modeled
02 / Prepare
Make it a scoped change.

An acceptance pack, a pre-handoff hook and a review workflow, each with an owner and a version.

Skill · Draft v1 · Owner Frank
03 / Compare
Keep the quality bar.

Same input, revision, environment and criteria. Unfinished tasks and held-out cases stay in.

12 paired tasks · 4 held out
04 / Apply & learn
Roll out with control.

Apply to future runs the person owns, keep comparing, and retain what proves useful as a versioned playbook.

Controlled rollout · Versioned scope

06 / 09 Built for the whole team

The same record. Two seats.

One task, “Implement dependency caching”, seen by the lead who owns the goal and by the engineer who owns the task. Same evidence, different focus.

Mike · Engineering lead

See the whole picture. Help the team move.

Goals, budgets and progress in one view. Where a workstream needs support, who is on it, and what the next decision is.

Progress toward shared goals
Spend across teams, people and agents
Opportunities to improve together

Goals & work

Team view · Faster CI/CD Sample

Goal Ship changes faster and safely

3 workstreams$7,568.94 estimated21 / 23 accepted

Implement dependency caching Frank · 2 agents · $1,058.86 estimated

Next: William reviews

In review

Repeated repository discovery across the workstream 62 sessions · skill compared, not applied

Frank · Platform engineer

Own your work. Improve your next run.

Your tasks, the agents that contributed, the evidence behind the result and the context worth reusing next time.

Your tasks and contributing agents
Evidence in context, before you accept a result
Skills and workflows close at hand

Team playbooks

Your work · Implement dependency caching Sample

Your current task Implement dependency caching

6 sessions$1,058.86 estimatedClaude Code · Codex

248 / 248 suites retainedexit 0 · recorded from CI

Checked

A better starting point for your next task Repository context skill · Draft v1 · compared, awaiting decision

07 / 09 The platform

agentacct, the platform underneath airis.

Three outcomes to improve every run. Two features that bring the work together. And an open-source app you can run on your own machine today.

00 agentacct One place to understand and improve agent work. 01 Cost intelligence Attribute spend, find repetition and compare cost per accepted task. 02 Execution quality Define acceptance, inspect the evidence and compare on equal terms. 03 Delivery speed Reuse context, prepare independent checks and hand over complete packets. 04 Goals & work Connect goals, workstreams, people and agents in one shared direction. 05 Team playbooks Turn a useful lesson into a scoped, reviewed, reusable practice. OSS agentacct on GitHub The local-first open-source app. Your machine, your records, no account.

Available today

Open source, on your machine.

The agentacct app records your own agent work locally: usage, work sections and recorded checks from Claude Code, Codex and OpenCode, with a receipt for each task. No account, no cloud sync.

agentacct on GitHub

The airis platform

The connected workspace for a whole company.

Everything that spans a team, from shared goals to cost attribution and reviewed playbooks, lives in the hosted airis platform. Book a demo to see it with your teams and tools in mind.

What a demo covers

08 / 09 Pricing

Priced per person. Agents are not seats.

Start with your own agents, bring the team together, then run the workforce across the company. A seat is a human; agents do not consume one. Model and provider fees stay with your provider.

Free Coming soon Your own agents, usage and recorded checks. Team Coming soon Shared goals, workstreams and cost by agent, person and task. Business Coming soon Cross-team views, comparisons, roles and source access. Enterprise Coming soon Rollout planning, data and access requirements, support.

Compare plans Plans coming soon · Agents do not consume seats

09 / 09 Questions

The questions engineering leaders ask first.

Short answers with the boundaries stated. Everything else, we cover on a call with your team.

Book a demo

01 Which agents does it capture work from?

The main receipt path covers Claude Code, Codex and OpenCode, with narrower or scoped coverage for other clients. Coverage differs by client and is documented with the open-source app. Where evidence is missing, airis says so rather than filling the gap.

02 Where does the data live?

The open-source agentacct app runs on one machine and keeps its records there. The hosted airis platform holds the shared, multi-team workspace. We cover data residency, access and retention for your organization on a demo call.

03 What is the relationship between airis and agentacct?

airis is the company. agentacct is the platform it builds. The open-source app records your own agent work; the hosted platform connects that kind of record to people, goals and budgets across a company.

04 Are the numbers on this site real?

No. Every workspace, goal, person, agent and figure shown is sample data. Modeled optimization potential is not realized savings, time comparisons are planning scenarios, and usage figures are estimates with a stated basis, never invoices.

05 Does a passing check mean the work is correct?

It means the recorded command passed within its stated scope. airis keeps the agent’s report, the recorded check and the human review as separate facts, so a reviewer can see what is proven and what is still pending.

06 Do agents count as seats?

No. A seat is a human workspace member. Agents do not consume seats, and model or provider fees are separate from airis pricing.

07 What happens on a demo?

A walkthrough of the workspace, a closer look at cost, quality and delivery for a sample cohort, and a conversation about your teams, tools and what a rollout would need.