AI Copilot Metrics

AI Copilot metrics measure how developers interact with AI coding assistants, adoption rates, and the impact on productivity across your organization.

Available Metrics

Adoption & Usage

ai-adoption-rate

string

Percentage of active developers using AI tools

adopted-users

string

Number of users who have adopted AI tools

adoption-rate

string

AI tool adoption rate

Suggestions & Acceptance

suggested-lines

string

Lines of code suggested by AI

accepted-lines

string

Lines of code accepted from AI suggestions

ai-acceptance-rate

string

Percentage of AI suggestions that were accepted

acceptance-rate

string

AI suggestion acceptance rate

total-accepts

string

Total number of accepted AI suggestions

total-rejects

string

Total number of rejected AI suggestions

Code Volume

ai-total-loc

string

Total lines of code written with AI assistance

ai-percent-ai-generated-code

string

Percentage of all code that was AI-generated

ai-code-ratio

string

Ratio of AI-generated code to total code

total-lines-added

string

Total lines added with AI assistance

total-lines-deleted

string

Total lines deleted with AI assistance

Requests by Type

chat-requests

string

Number of AI chat requests

composer-requests

string

Number of AI composer requests

agent-requests

string

Number of AI agent requests

cmd-k-usages

string

Number of inline (Cmd+K) AI usages

Quality & Cost

ai-complexity-score

string

Complexity score of AI-assisted code

ai-pr-count

string

Number of pull requests created with AI assistance

ai-total-token-usage

string

Total AI token usage

ai-total-cost

string

Total estimated cost of AI usage

Insights

ai-favourite-model

string

Most used AI model across the organization

most-used-programming-language

string

Most used programming language in AI interactions

Accepted vs Suggested Lines

Retrieve both accepted and suggested lines in a single request to calculate acceptance rate over time.

curl -X POST 'https://api.bilanc.co/metrics/get-multiple-metrics' \
  -H 'Authorization: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "filters": {
      "team": ["team123"],
      "start_date": "2024-01-01",
      "end_date": "2024-03-31"
    },
    "date_level": "month",
    "metrics": ["suggested-lines", "accepted-lines"],
    "date_fields": {
      "suggested-lines": "date",
      "accepted-lines": "date"
    },
    "aggregations": {
      "suggested-lines": "SUM",
      "accepted-lines": "SUM"
    }
  }'

AI Adoption Rate

Track the percentage of active developers using AI tools over time.

curl -X POST 'https://api.bilanc.co/metrics/ai-adoption-rate' \
  -H 'Authorization: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "filters": {
      "start_date": "2024-01-01",
      "end_date": "2024-03-31"
    },
    "group_by": ["team_name"],
    "date_level": "month",
    "date_field": "date"
  }'

Most Used AI Model

Retrieve the most commonly used AI model across the organization.

curl -X POST 'https://api.bilanc.co/metrics/ai-favourite-model' \
  -H 'Authorization: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "filters": {
      "start_date": "2024-01-01",
      "end_date": "2024-03-31"
    },
    "group_by": ["team_name"],
    "date_field": "date"
  }'

Parameter Notes

date_field

string

For AI copilot metrics the available date field is:

date: The date of the AI interaction

aggregation

string

For line counts and totals: SUM
For rate metrics (ai-adoption-rate, ai-acceptance-rate, acceptance-rate, adoption-rate, ai-code-ratio, ai-percent-ai-generated-code): no aggregation needed — values are pre-computed percentages/ratios
For cost and token usage: SUM or AVG

Common Use Cases

Adoption Monitoring: Track AI tool adoption across teams over time
ROI Analysis: Compare ai-total-cost against productivity gains
Acceptance Quality: Monitor acceptance-rate to gauge suggestion relevance
Language Insights: Use most-used-programming-language to focus AI training
Usage Breakdown: Compare chat-requests, composer-requests, and agent-requests to understand how developers use AI

Documentation Index