Helm

Know what AI costs. See what you get back.

One place to understand your AI investment. Fund your people and agents, connect spending to the work and find where it can do more for your business.

AI across your businessIllustrative data · 30 days
People using AI184
Connected tools6
AI spend$8,420

Recorded sessions by tool · selected groups

Finance320
Claude: 130 sessions. OpenAI: 50 sessions. Microsoft 365 Copilot: 140 sessions. Vertex AI: 0 sessions. Cursor: 0 sessions. GitHub Copilot: 0 sessions.
Sales350
Claude: 70 sessions. OpenAI: 100 sessions. Microsoft 365 Copilot: 180 sessions. Vertex AI: 0 sessions. Cursor: 0 sessions. GitHub Copilot: 0 sessions.
Marketing360
Claude: 140 sessions. OpenAI: 110 sessions. Microsoft 365 Copilot: 20 sessions. Vertex AI: 90 sessions. Cursor: 0 sessions. GitHub Copilot: 0 sessions.
Legal260
Claude: 150 sessions. OpenAI: 20 sessions. Microsoft 365 Copilot: 90 sessions. Vertex AI: 0 sessions. Cursor: 0 sessions. GitHub Copilot: 0 sessions.
Operations260
Claude: 40 sessions. OpenAI: 100 sessions. Microsoft 365 Copilot: 80 sessions. Vertex AI: 40 sessions. Cursor: 0 sessions. GitHub Copilot: 0 sessions.
Engineering450
Claude: 90 sessions. OpenAI: 30 sessions. Microsoft 365 Copilot: 0 sessions. Vertex AI: 30 sessions. Cursor: 180 sessions. GitHub Copilot: 120 sessions.
Your AI operating system Costs · Budgets · Adoption · Value
01 / Spending

Catch rising costs before you overspend.

Bring subscription and usage costs from connected providers together. Follow them by person, team, project, model or provider.

Review changes over time, compare spending with the budget and see the forecast. Keep the basis for each figure clear, including costs that still need allocating.

Understand AI spending and value ↗
Customer support / September Forecast
£840 over budget at the current rate
£3,000 budget 0 replies 1,200 2,400 replies
2,400 replies this month
×
£1.60 per reply
=
£3,840 forecast spend

The cost per reply is driving the overspend.

Full-month comparison at the same reply volume. Quality review pending.
02 / A shared AI wallet

One budget. Across your AI tools.

Give a person or team a shared allowance across connected services. Fund project work from its own budget first, with rules for what happens when that funding runs out.

Allocate costs to departments, projects, funds or clients. Handle requests for more budget, alert the right owner and apply limits through supported connections.

Share a wallet across your tools ↗
Operations / Shared AI wallet This month
Budget £8,000
Used after charge £6,240
Remaining after charge £1,760
Claude £3,600 OpenAI £1,800 Vertex £840
Next charge / Claude · Reporting £48
Choose which budget pays
Reporting project pays £48

Project budget: £320 → £272 remaining. The team wallet stays at £1,760.

Fund the work

Choose which project, team or individual allowance pays. Keep the decision visible in the cost record.

Approve more budget

Give requests an owner and a decision, with the reason for the increase alongside them.

Respond before overspending

Set alerts and supported spending limits. Decide which work should continue and which needs approval.

Finance and technology colleagues discussing an investment decision.
Across your business

A shared view for a better decision.

Finance needs to explain the bill. People need access to the right tools, whether they work in sales, finance, marketing or engineering. Team leaders need to know whether the work is improving.

Helm brings those questions together, so the next investment starts with evidence.

03 / Adoption and waste

Keep useful access. Stop paying for the rest.

See who uses AI, where usage has lapsed and which tools teams rely on. Compare paid seats with observed use and find overlapping subscriptions.

Auto-suspend unused access under the rules you agree. Keep former employees, inactivity, renewals and plan changes in the same review.

Review tools, seats and renewals ↗

Help people get more from AI.

Usage tells you who’s active. F.AI helps assess capability, task performance and frustration so you can see where support or a different model could help.

Explore F.AI ↗
Unused access Ready for review
180 seats Former employees and people no longer using their access
Claude 120 unused seats · £20 per seat/month
£28,800 potential saving / year
ChatGPT 60 unused seats · £24 per seat/month
£17,280 potential saving / year
Potential annual saving £46,080
04 / Value

Compare the cost with the result.

Choose a task and agree the baseline. Measure preparation, review, corrections, output quality and running cost before and after AI support.

See whether the change creates capacity, reduces operating cost or improves delivery. Keep measured results separate from estimates and potential savings.

Create more capacity for your team ↗
Monthly reporting Before / With AI
Preparation 8h → 2h
Human review 1h → 1h
Total per cycle 9h → 3h
6h

Released for analysis and decisions each month.

£18 running cost per cycle 72 hours a year at 12 cycles
05 / Business activity

Follow AI use into the work it supports.

A provider bill doesn’t tell you which project benefited. Connect usage with project records, workflows and delivery evidence to understand the activity behind the cost.

Attribute AI use to business activity ↗
01 / AI activity

The tool, model and recorded use

Start with the usage and cost records available from each connection.

02 / Business context

The person, task and project

Use the records in your work systems to allocate cost and establish context.

03 / Delivery evidence

What reached the business

With Sail, connect recorded AI output to delivered work. For code, compare it with GitHub changes and keep edits and evidence gaps visible.

Trace AI contribution to code ↗
Engineering

See what AI contributes to the code you ship.

Follow AI assistance into pull requests. See which generated lines survive review, compare cycle time and inspect work that needed another pass.

Use the same approach across the company: connect AI activity to the work, then check the result.

Explore AI code contribution ↗
From prompt to pull request Illustrative data · Helm + Sail
60%

of merged code came from AI

budget-approval.ts
1 export async function approveBudget(request) { Human
2 const budget = await getBudget(request.teamId) AI
3 if (budget.remaining > request.amount) { Removed
4 if (!canApprove(request.user, budget)) return Human
5 await reserveFunds(budget.id, request.amount) AI
6 return { approved: true } Removed
7 return recordApproval(request, budget) Human
8 } Human
AI kept Human written AI removed
240 AI lines generated 96 kept
144 removed or replaced + 64 human lines
2.5:1 AI lines generated to kept
6 Prompts across 3 attempts
160 Lines in the merged change
See how AI use connects to delivered work ↗
Engineering / AI contributionIllustrative data · 30 days
PRs with AI assistance64 / 100

Merged PRs with linked AI evidence

Generated lines retained40%

96 of 240 generated lines made it into merged code

Cycle time comparison18h / 26h

Median open-to-merge: AI-assisted / no linked AI

Reopened PRs4 / 64

AI-assisted PRs reopened before merge

Repositories by AI usage

Linked coding sessions, split by tool

Customer portal96 sessions
Reporting service65 sessions
Internal tools52 sessions
Claude CodeCursorGitHub Copilot

Cycle time compares groups, not a proven causal effect. Reopened PRs flag work to inspect, not proof that AI caused a defect. Fixes after merge need linked follow-up changes. Unknown attribution stays separate.

06 / Controls and evidence

Set the rules. Keep the evidence.

Choose models for the task

Compare cost and quality for coding, analysis or content work. Configure model selection and routing where your connections support it.

Give the right people control

Set access around roles. Make budget ownership, approvals and exceptions clear.

Know where the figures came from

Trace costs to their sources, reconcile totals and distinguish measured spend from estimates.

Work with your privacy requirements

Agree what information the job needs, who can see it and how long it should be kept.

Workloads and agents

Follow the AI running inside your business.

Workloads show AI deployed inside applications. Agents show AI carrying out jobs, including flows built in Sail. Give each an owner, a budget and measures that fit the work.

Understand workloads and agents ↗

Measure the useful result

For an application, compare cost per successful result. For an agent, follow completed jobs, exceptions and human review. Keep both alongside your company’s wider AI investment.

Your connected providers

Start with the tools your business uses.

We agree the reporting, attribution and controls available for your accounts. You can use Helm on its own, or connect it with applications built in Sail.

Explore all integrations ↗

Make your next AI decision with a clearer view.

Talk through your AI investment ↗
Practical questions

What to know before you start.

Is Helm only for engineering teams?

No. Helm brings AI spending, budgets, adoption and activity together across the company. Finance, sales, legal, marketing, operations and engineering can each see the tools and work relevant to them.

Can Helm stop every provider from overspending?

Controls depend on the connection. Some support spending limits or routing, while others provide delayed usage reports. We agree which measures and controls are available before setup.

How do you distinguish savings from unused budget?

Unused budget is headroom. Avoiding another purchase is cost avoidance. A reduced bill is a realized saving. We keep those measures separate so an estimate is not presented as cash saved.