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Interaction Summaries

Every interaction, instantly understood

Stop reading transcripts. Get a plain-language summary of every voice and chat interaction, automatically.

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Why this matters

Agents and supervisors spend minutes per call reading transcripts. At high volume, that time disappears. AI summaries give it back so your agents can work more efficiently and your interactions can move more smoothly.

Get the highlights

Every call covered

Every voice and chat interaction is summarized automatically.

Seamless handovers

Give context and personalization when handing over an issue

Seconds, not minutes

Supervisors go from flagged interaction to full context in seconds.

Capacity in numbers

20K+

Happy customers

1.5M+

Users who love us

36.3B+

Automated interactions

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Interaction Summaries

Understand every call fast

Every voice and chat interaction gets an automatic AI summary. No transcripts, no manual notes, no delay.

Automatic on every interaction
Summaries are generated on 100% of voice and chat interactions without any manual effort or configuration.

Structured, plain-language output
Each summary covers why the customer contacted support, what steps were taken and how it was resolved.

Review any interaction in seconds
Supervisors get full context on a flagged call without reading a single line of transcript.

Feeds directly into QA and coaching
Summaries are connected to Auto QA and the Capacity Coaching Hub. Context flows where it is needed most.

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Seamless Handover

Pick up any conversation, instantly

Every agent who touches a voice or chat interaction gets immediate context. No briefing required.

Instant context on AI-to-human transfer
When an AI agent escalates to a human, the interaction summary surfaces automatically in the agent’s view at the moment of transfer.

No repeated questions on warm transfers
Human-to-human transfers include the full interaction summary so the receiving agent starts informed, not from scratch.

Full picture before you say hello
Agents picking up a voice or chat interaction get

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AI Agents

Agent Assist

Post Interation Workflows

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Integrations

AI Knowledge Orchestration

Trusted by 20,000+ organizations

3M
Disney
Valvoline
LinkedIn
Aveda
Kraft Heinz
Nike
SSM Health
Titleist
Pearson
McGraw Hill
YMCA
DSW
American Express
Choice Hotels
Sony
NVIDIA
US Army
AAA
HomeAdvisor
VIP
211 Heartline
BankSouth
Banfield Pet Hospital
PRMG
Asembia
InnovaCare
Maryville
USA Mortgage
Bodylase
OrthoBanc

Frequently asked questions

What are interaction summaries?

Interaction summaries are automatically generated, plain-language accounts of what happened on a voice or chat interaction, why the customer contacted support, what steps the agent took and how it resolved. They are produced by AI on every eligible interaction without manual effort.

How do interaction summaries work?

After each voice or chat interaction, Capacity generates a structured summary covering why the customer called, what steps were taken and how it resolved. Summaries are available in Capacity’s Post-Interaction Suite alongside your AQA scores and coaching sessions.

Do interaction summaries replace transcripts?

Summaries complement transcripts rather than replace them. A summary gives supervisors the full picture of an interaction in seconds. The original transcript remains available for cases where a word-for-word record is needed.

How long does it take to set up?

Interaction summaries require no setup. They are generated automatically on every voice and chat interaction once the feature is enabled.

Does this work with my existing contact center platform?

Capacity connects to your existing contact center platform via direct integration. Production-ready integrations include Glia, 8×8, RingCentral, Five9, NiCE, Salesforce and more. Confirm your platform compatibility during scoping.

Is my interaction data sent to external AI providers?

No. LLMs used by Capacity for interaction summaries are internally hosted in Capacity-controlled environments. Customer interaction data is not sent to external model providers or used to train external models.

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