KPI Reference Guide

Understanding KPIs

What is a KPI?

A Key Performance Indicator (KPI) is a measurable value that demonstrates how effectively your organization is using AI. Each KPI card in the dashboard shows:

• Current value — The metric for the selected period

• Trend badge — Comparison to the previous equivalent period

• Visual indicator — Color-coded to show positive/negative trends

How Trends Are Calculated

Trends compare the current period to the previous equivalent period:

Trend formula: ((Current - Previous) / Previous) × 100

Overview Dashboard KPIs

Active Users

Definition: The number of unique organization members who sent at least one message during the selected period.

Formula:

COUNT(DISTINCT user_id WHERE messages_sent >= 1)

What it measures:

• Platform adoption

• User engagement

• Active user base size

Good trend: 🟢 Increasing (more users adopting AI)

Example:

• Period: Last 30 days

• Active Users: 45

• Trend: +12% (4 more users than previous 30 days)

Usage Intensity

Definition: The average number of messages sent per active user.

Formula:

Total messages / Active users

What it measures:

• How deeply users engage with AI

• Average workload per user

• Platform stickiness

Good trend: 🟢 Increasing (users are engaging more deeply)

Example:

• Total messages: 1,350

• Active users: 45

• Usage Intensity: 30 messages/user

Interpretation:

• <10: Light usage

• 10-30: Moderate usage

• 30-50: Heavy usage

• 50: Power user behavior

Mates per User

Definition: The average number of different Mates used per active user.

Formula:

COUNT(DISTINCT mate_id) / COUNT(DISTINCT user_id)

What it measures:

• Diversity of AI usage

• Mate discovery

• Platform exploration

Good trend: 🟢 Increasing (users exploring more Mates)

Example:

• Distinct Mates used: 135

• Active users: 45

• Mates per User: 3.0

Interpretation:

• 1-2: Users stick to familiar Mates

• 3-5: Good exploration

• 5: High diversity, users leveraging specialized Mates

Growth

Definition: The percentage change in active users compared to the previous period.

Formula:

((Current active users - Previous active users) / Previous active users) × 100

What it measures:

• Adoption velocity

• Platform momentum

• User acquisition success

Good trend: 🟢 Positive growth

Example:

• Current period: 45 active users

• Previous period: 40 active users

• Growth: +12.5%

Engagement Dashboard KPIs

Conversations / User

Definition: The average number of conversation sessions per active user.

Formula:

Total conversations / Active users

What it measures:

• Session frequency

• How often users return to the platform

• Engagement depth

Good trend: 🟢 Increasing (users having more sessions)

Example:

• Total conversations: 225

• Active users: 45

• Conversations / User: 5.0

Interpretation:

• 1-3: Occasional use

• 4-7: Regular use

• 7: Frequent, habitual use

Messages / User

Definition: The average number of messages sent per active user.

Formula:

Total messages / Active users

What it measures:

• Overall engagement level

• Platform usage intensity

• User activity

Good trend: 🟢 Increasing (users engaging more)

Example:

• Total messages: 1,350

• Active users: 45

• Messages / User: 30

Mates Explored / User

Definition: The average number of distinct Mates used per active user.

Formula:

COUNT(DISTINCT mate_id per user) / COUNT(DISTINCT user_id)

What it measures:

• Mate discovery

• Usage diversity

• Platform exploration

Good trend: 🟢 Increasing (users trying more Mates)

Example:

• User A used 3 Mates

• User B used 5 Mates

• User C used 2 Mates

• Average: (3+5+2)/3 = 3.3 Mates/User

Activation Rate

Definition: The percentage of organization members who are actively using the platform.

Formula:

(Active users / Total organization members) × 100

What it measures:

• Platform adoption

• Onboarding success

• User activation

Good trend: 🟢 Increasing (more members becoming active)

Example:

• Active users: 45

• Total members: 60

• Activation Rate: 75%

Interpretation:

• <30%: Low adoption — needs attention

• 30-60%: Moderate adoption

• 60-80%: Good adoption

• 80%: Excellent adoption

Mates Dashboard KPIs

Active Mates

Definition: The number of Mates that received at least one request during the period.

Formula:

COUNT(DISTINCT mate_id WHERE requests >= 1)

What it measures:

• Mate utilization

• Platform diversity

• Mate portfolio health

Good trend: 🟢 Increasing (more Mates being used)

Example:

• Total Mates in organization: 20

• Active Mates: 12

• Utilization: 60%

Requests

Definition: The total number of messages sent to Mates (Mate invocations).

Formula:

COUNT(messages WHERE recipient_type = 'mate')

What it measures:

• Total AI workload

• Mate demand

• Platform usage volume

Good trend: 🟢 Increasing (more AI usage)

Example:

• Requests: 1,125

• This represents all messages directed to Mates

Users (Mates Dashboard)

Definition: The number of unique users who interacted with at least one Mate.

Formula:

COUNT(DISTINCT user_id WHERE mate_requests >= 1)

What it measures:

• Mate adoption

• User reach

• Platform penetration

Good trend: 🟢 Increasing (more users using Mates)

Tokens / Response

Definition: The average number of tokens consumed per AI response.

Formula:

Total tokens / Total AI responses

What it measures:

• Response efficiency

• Token optimization

• Cost per response

Good trend: 🟢 Decreasing (more efficient responses) or stable

Example:

• Total tokens: 2,250,000

• AI responses: 1,125

• Tokens / Response: 2,000

Interpretation:

• <1,000: Very concise responses

• 1,000-3,000: Normal responses

• 3,000-5,000: Detailed responses

• 5,000: Very verbose responses (may need optimization)

Usage Dashboard KPIs

Total Tokens

Definition: The sum of all input and output tokens consumed during the period.

Formula:

SUM(input_tokens + output_tokens)

What it measures:

• Total AI consumption

• Platform usage volume

• Cost driver

Good trend: Depends on context

• 🟢 Increasing = more usage (good for adoption)

• 🔴 Increasing = higher costs (may need optimization)

Example:

• Input tokens: 900,000

• Output tokens: 1,350,000

• Total Tokens: 2,250,000

Estimated Cost

Definition: The Allmates API cost (operational COGS) in USD of the tokens consumed. It is computed from up-to-date, historized model prices, and is replaced by the actual provider-billed cost when the message runs through an allmates-managed connection. A pricing fallback (model family → provider → average) ensures the value is never zero.

Formula:

SUM(tokens × model_price_per_token)

What it measures:

• AI spending

• Budget consumption

• Cost trends

Good trend: 🟢 Stable or decreasing per user

Example:

• Total tokens: 2,250,000

• Average price: $0.002 per 1K tokens

• Estimated Cost: $4.50

Important notes:

• For messages metered through an allmates-managed connection, the real billed cost is used; otherwise an estimate from historized model prices is shown.

• For your own (BYOK) connections, the figure is an estimate and may differ from your provider invoice — volume discounts and custom pricing agreements are not reflected.

Tokens / Message

Definition: The average number of tokens consumed per message (human + AI).

Formula:

Total tokens / Total messages

What it measures:

• Message efficiency

• Context size

• Optimization opportunity

Good trend: 🟢 Stable or decreasing (more efficient)

Example:

• Total tokens: 2,250,000

• Total messages: 1,350

• Tokens / Message: 1,667

Interpretation:

• <1,000: Very efficient

• 1,000-2,500: Normal

• 2,500-5,000: High context (long conversations or large prompts)

• 5,000: Very high (may need optimization)

Cost / User

Definition: The average cost per active user, attributed to the human who triggered each message.

Formula:

Total cost / Active users

What it measures:

• Per-user spending

• Cost efficiency

• Budget planning

Good trend: 🟢 Stable or decreasing

Example:

• Estimated cost: $4.50

• Active users: 45

• Cost / User: $0.10

Interpretation:

• <$0.50/user: Very cost-efficient

• $0.50-$2/user: Normal

• $2-$5/user: High usage or expensive models

• $5/user: Very high (review usage patterns)

Tools Dashboard KPIs

Tool Calls

Definition: The total number of tool invocations during the period.

Formula:

COUNT(tool_calls)

What it measures:

• Tool usage volume

• External integration activity

• Mate capabilities utilization

Good trend: 🟢 Increasing (more tool usage = more advanced workflows)

Success Rate

Definition: The percentage of tool calls that completed successfully.

Formula:

(Successful calls / Total calls) × 100

What it measures:

• Tool reliability

• Integration health

• User experience quality

Good trend: 🟢 High and stable (>95%)

Example:

• Total calls: 500

• Successful calls: 475

• Success Rate: 95%

Interpretation:

• 95%: Excellent reliability

• 90-95%: Good (monitor for issues)

• 80-90%: Moderate (investigate failures)

• <80%: Poor (immediate action needed)

Average Duration

Definition: The average response time for tool calls in seconds.

Formula:

SUM(tool_call_duration) / COUNT(tool_calls)

What it measures:

• Tool performance

• User experience

• API responsiveness

Good trend: 🟢 Low and stable (<2s)

Example:

• Total duration: 1,250 seconds

• Total calls: 500

• Average Duration: 2.5s

Interpretation:

• <0.5s: Instant (excellent UX)

• 0.5-2s: Fast (good UX)

• 2-5s: Medium (acceptable)

• 5-10s: Slow (optimization recommended)

• 10s: Very slow (poor UX, needs attention)

Total Cost (Tools)

Definition: The total cost of tool calls in USD.

Formula:

SUM(tool_call_cost)

What it measures:

• Tool spending

• External API costs

• Budget consumption

Good trend: 🟢 Stable or decreasing per call

Tools / Message

Definition: The average number of tool calls per agent message.

Formula:

Total tool calls / Total agent messages

What it measures:

• Tool dependency

• Workflow complexity

• Automation level

Example:

• Tool calls: 500

• Agent messages: 1,125

• Tools / Message: 0.44

Interpretation:

• <0.3: Low tool usage (mostly conversational)

• 0.3-0.7: Moderate tool usage (balanced)

• 0.7-1.5: High tool usage (tool-heavy workflows)

• 1.5: Very high (multiple tools per response)

Errors Dashboard KPIs

Message Errors

Definition: The number of messages that encountered an error during processing.

Formula:

COUNT(messages WHERE status = 'error')

What it measures:

• LLM reliability

• Message processing health

• User experience issues

Good trend: 🟢 Low and decreasing

Tool Errors

Definition: The number of tool calls that failed.

Formula:

COUNT(tool_calls WHERE status = 'error')

What it measures:

• Tool reliability

• Integration health

• External API issues

Good trend: 🟢 Low and decreasing

Global Error Rate

Definition: The percentage of all operations (messages + tool calls) that resulted in an error.

Formula:

((Message errors + Tool errors) / (Total messages + Total tool calls)) × 100

What it measures:

• Overall system reliability

• User experience quality

• Platform health

Good trend: 🟢 Low (<5%)

Example:

• Message errors: 15

• Tool errors: 25

• Total messages: 1,350

• Total tool calls: 500

• Global Error Rate: (40 / 1,850) × 100 = 2.16%

Interpretation:

• <5%: Excellent reliability

• 5-10%: Moderate (monitor closely)

• 10%: Critical (immediate action needed)

Impacted Users

Definition: The number of unique users who encountered at least one error.

Formula:

COUNT(DISTINCT user_id WHERE errors >= 1)

What it measures:

• Error reach

• User experience impact

• Support workload

Good trend: 🟢 Low and decreasing

Credits Dashboard KPIs

Credits Consumed

Definition: The total Polar units consumed across all meters.

Formula:

SUM(consumed_credits per meter)

What it measures:

• Quota consumption

• Billing usage

• Resource utilization

Good trend: 🟢 Within allocated limits

Credits Remaining

Definition: The total Polar units still available across all meters.

Formula:

SUM(allocated_credits - consumed_credits per meter)

What it measures:

• Available quota

• Buffer before limit

• Planning headroom

Good trend: 🟢 Sufficient buffer (>20%)

LLM Credits

Definition: The Polar credits consumed by LLM token usage over the period — any connection that bills through Polar metering (including BYOK that transits Polar, e.g. Anthropic on Allmates Premium).

Formula:

SUM(polar_tokens_total_cost_credit)

Sub-info: the card shows the credit value (credits × $0.0002) and the Allmates API cost — the operational cost (COGS) Allmates pays the LLM providers. These are two different reference frames and do not add up.

What it measures:

• LLM credit consumption

• Token usage billed via Polar

Tool Credits

Definition: The Polar credits consumed by tool calls over the period.

Formula:

SUM(polar_total_tools_cost_credit)

What it measures:

• Tool credit consumption

• Tool usage billed via Polar

Credit Value

Definition: The monetary value of the credits consumed, valued at the organisation's daily weighted-average cost (WAC) per credit. It reflects the economic value of usage — not the exact Polar invoice.

Formula:

SUM(polar_global_credit_cost × daily_WAC_usd_per_credit)

Pricing note: until Polar purchase prices are ingested, every credit is valued at a unified reference rate of $0.20 per 1,000 credits ($0.0002 / credit); the WAC then refines per organisation as real purchase prices arrive.

What it measures:

• Economic value of consumption

• Customer-facing credit spend

Attachments Dashboard KPIs

Files Uploaded

Definition: The total number of files uploaded during the period.

Formula:

COUNT(file_uploads)

What it measures:

• File usage volume

• Content-based workflows

• Storage demand

Good trend: 🟢 Increasing (more file-based work)

Total Storage

Definition: The total storage consumed by uploaded files (in GB or MB).

Formula:

SUM(file_size)

What it measures:

• Storage consumption

• Infrastructure cost

• Data volume

Good trend: 🟢 Stable or growing predictably

Tokens Extracted

Definition: The total number of tokens parsed from files via RAG (Retrieval-Augmented Generation).

Formula:

SUM(tokens_extracted_from_files)

What it measures:

• Content extraction volume

• RAG usage

• File processing workload

Good trend: 🟢 Increasing (more content being processed)

Error Rate (Attachments)

Definition: The percentage of files that encountered processing errors.

Formula:

(Files with errors / Total files) × 100

What it measures:

• File processing reliability

• Format compatibility

• Processing pipeline health

Good trend: 🟢 Low (<5%)

Interpretation:

• <5%: Excellent processing

• 5-10%: Moderate (check unsupported formats)

• 10%: High (investigate processing issues)