Antenna

The control plane for AI transformation

AI spend is growing fast. Get control and maximize returns. Antenna gives you the visibility and controls to scale AI with confidence.

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Every tool brings new controls and new data. The result is an uneven picture of spend, usage, and impact at a time when AI is becoming a critical investment.

Antenna connects the AI stack and turns fragmented activity into evidence leaders can act on.

  • AI Tools

  • AI Gateways

  • Managed Clouds

  • Claude
  • OpenAI
  • GitHub Copilot
  • Cursor
  • Amazon Q
  • Gemini
  • Windsurf
  • Kiro
  • Devin
  • OpenRouter
  • Codex
  • LiteLLM

[ Control surface ]

One unified view of AI across the company

Antenna brings governance, financial controls, and delivery intelligence into one operating layer.

  • Governance

    Use data to direct AI adoption, investment, and scale.

  • Controls

    Apply consistent guardrails from one operating layer.

  • Optimization

    Direct AI spend toward the right tools and workflows.

  • Budget

    Manage AI budgets to deliver the most value.

  • FinOps

    Connect consumption and returns across the AI stack.

  • ROI

    Connect token and tool spend to delivery performance.

  • Forecasting

    Project spend and usage as adoption increases.

  • Capacity Planning

    Model delivery, headcount, and AI-budget tradeoffs.

  • Productivity

    Measure how AI changes throughput and quality.

  • Benchmarks

    Compare performance across teams, tools, and time.

[ AI spend management ]

Track and forecast AI spend at every level

See, understand, and control every dollar your company spends on AI. Forecast budgets as usage, models, and team size change.

Total AI Spend

Investment by business function · trailing 7 months

$388K

+10.5%

Engineering
Product
Sales
Operations

[ Spend optimization ]

Spend with intent and scale what works

Turn a complete view of AI spend into better budget, tooling, and model decisions.

  1. Set budgets

    Compare usage, cost, and outcomes to invest where AI delivers the most value.

  2. Choose tools

    Compare agents, harnesses, and providers based on their impact on performance.

  3. Select models

    Find the right balance of capability, speed, reasoning, and cost for each workload.

[ Maturity quadrant ]

Connect AI usage to business outcomes

See which teams get the highest lift per dollar of AI spend, identify what drives their results, and replicate it. Flag teams burning through tokens without the productivity lift.

[ Use Cases ]

See all your AI data

Turn AI data into insights for every department. Give each team a clear view to plan, optimize, and act.

Product

Track roadmap progress, delivery bottlenecks, and AI investment by product area.

Explore data for product teams

Finance

Connect software investment, resource allocation, and product plans to financial outcomes.

Explore data for finance teams

Engineering

What is preventing AI from translating into delivery? Find performance gaps and bottlenecks that limit leverage.

Unlock engineering productivity
[ Constraint ]

Code review bottleneck

Pull requests are waiting longer for review, slowing delivery across high-volume teams.

Signal

2.4× review queue

Recommended action

Rebalance reviewer capacity

[ Delivery ]

Spend rising without output

AI costs are climbing faster than delivery, revealing workflows that need intervention.

Signal

+31% spend · +2% delivery

Recommended action

Set team budgets

[ Efficiency ]

High frontier model usage

Premium models are likely handling routine work that can run effectively on lower-cost models.

Signal

68% premium-model calls

Recommended action

Switch to most cost-efficient model

[ Tools ]

Unused licenses increasing

Paid seats are going idle while active teams wait for access to the tools they need.

Signal

$42K idle annualized

Recommended action

Remove or reassign inactive seats

[ Agents ]

Low agent review adoption

Most pull requests are reviewed without agents before merging to the main branch.

Signal

37% of PRs agent-reviewed

Recommended action

Expand agentic reviews

[ Quality ]

High deployment failure rate

Recent releases are failing more often, with agent-authored changes overrepresented.

Signal

14.8% failure rate

Recommended action

Add testing skill for agents

See the forest and the trees

AI is redefining what your organization can accomplish. Make the most of it across every tool, team, and workflow.

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