Opportunities › AI code assistant usage optimization and analytics › Proposed app
Steadyhand
See how your team uses AI coding tools and coach cheaper, better sessions
- Score
- 36
- confidence 52
- Evidence
- 4
- items on the page
- Rivals
- 2
- named in sources
- First version
- 35 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2025
- New market · AI estimate
ScreensConcept
Desktop app: Managers review detailed usage analysis, session-level data, and coaching opportunities in dashboards; engineers receive coaching on dense performance reports. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Team overview
Sample data31%
About this appAI proposal
Steadyhand reads usage logs from AI coding assistants and shows engineering managers how the tools are really used: which sessions finish the job, which loop and burn tokens, and where habits differ across the team. It goes past monthly spend and seat counts.
Each engineer gets a private coaching view with concrete suggestions, such as trimming oversized context or splitting a long session. Managers see team-level patterns, not a ranking of individual prompts. A local audit tool lets one engineer try it before the team signs up.
It is built for managers at companies that already track AI tool spend and want to know what that spend returns. It is meant to work with Claude Code, Codex and similar assistants.
- Tagline
- Know how your team uses AI, then get better at it.
- Voice
- CalmPlainPractical
- Why this name
- A steady hand suggests careful, controlled use of a powerful tool, and coaching rather than surveillance.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Session-level usage analysis
Breaks usage into sessions and shows length, retries and token use, so you see patterns a spend total hides.
No member yet
Private coaching for engineers
Each engineer gets tips on context size, repeated retries and session scope, with the tokens each change could save.
No member yet
Team workflow view
Aggregate charts for managers: adoption, typical session shape and where teams differ. No prompt text is shown.
No member yet
Spend and seat context
Import monthly spend and seats, then compare them with usage to find idle seats and costly habits.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#1F4D3FPine · primary#F6F3EAPaper · surface#1F2A24Ink · text#E0A030Amber · accent#B5533CClay · warning / data
Typography
Aa
Steadyhand
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Know how your team uses AI, then get better at it.
PricingAI proposal
The material gives no competitor prices. These tiers are a starting point to test, based on the idea of per-engineer team plans and a free local tool for individuals.
Solo audit
Free
- Local audit of your own logs
- Personal coaching tips
- No account data shared
Team
$12 a month
- Per engineer, per month
- Team workflow view
- Spend and seat import
- Private coaching for each engineer
Enterprise
Contact us
- SSO and audit controls
- Multi-team reporting
- Custom data retention
Would you pay Team · $12 a month?
Your answer tells PWAS which prices people accept. It changes no score.
No answers yet.
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Shared outputs. Engineers share a summary of their local audit, and managers send team reports to leadership. Recipients see the product name on the report.
- Do they become customers?
- Report readers are often managers and engineers who could pay. What to test: whether shared audit summaries lead to team signups.
- First customers
- Engineering managers at software companies of 30 to 200 engineers that already pay for Claude Code or Codex seats and track spend.
- Where to reach them
- Hacker News: Share the free local audit tool and invite comments from people discussing AI coding spend.
- Engineering manager communities: Offer free team audits to managers in forums and Slack groups.
- Direct outreach: Message managers who have posted about building internal dashboards for AI tool spend.
- First milestone
- Five teams of at least 20 engineers each pay for the Team plan after a free audit.
Why people already pay for this
- VerifiedHacker News post received 21 points and 22 comments
- Public claimMultiple companies have built their own dashboards tracking Claude and Codex spend and seats per month
- Public claimExisting dashboards do not analyze how engineers use the tools or identify improvement opportunities
- Public claimThere is demand for team-based solutions in this space
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Local audit CLISmall cross-platform command-line toolLets engineers analyze their own logs on-device before any data is shared.
- This app · Log ingestionParsers plus background job queueAssistant logs and usage exports need parsing into one session format.
RisksAI inference
- PlatformAssistant vendors may change log formats or ship their own usage analytics, which would break parsers or overlap with this product.
- CommercialEngineers may see usage analysis as surveillance and refuse to install it, so private coaching and aggregate-only manager views must be convincing.
- DataSession logs can contain source code and secrets. Local redaction and strict retention are needed before any upload.
- CommercialMany companies have built their own spend dashboards, so buyers may not pay for something their internal team can extend.
- AICoaching suggestions may be wrong or generic if the outcome of a session cannot be judged reliably from logs alone.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.