Open-Text Theme Synthesis Agent

An open-text theme synthesis agent is an AI agent for employee engagement & listening that reads thousands of free-text survey comments and groups them into themes with representative, de-identified quotes, always at a level where no individual or small team can be recognized, and shows how themes differ across large segments.

How does the open-text theme synthesis agent work?

What flows in, what the agent does with it, where a person decides, and what comes out.

  1. Reads from

    Open-text comments · Segment metadata (aggregate) · Group-size and de-identification rules

  2. AI agent · runs when a survey window closes

    Open-Text Theme Synthesis Agent

  3. A person decides

    Engagement lead picks priorities and decides who sees what

  4. Produces

    Themed summary per segment · Prevalence and sentiment view · Anonymised example quotes · Suppressed-group log

What does the open-text theme synthesis agent do?

Reads thousands of free-text survey comments and groups them into themes with representative, de-identified quotes, always at a level where no individual or small team can be recognized, and shows how themes differ across large segments.

What does it produce?

A themed summary per organization and per segment above the minimum group size, with theme prevalence, sentiment direction and anonymized example quotes

Who decides?

The engagement lead and executive sponsor decide which themes become priorities and what is shared with whom; the agent summarizes and never surfaces comments below the group-size threshold or attributes a comment to a person.

What systems does the open-text theme synthesis agent connect to?

Examples of the kind of systems this agent would read from or write to, so you can picture it in your own stack. The actual set is whatever you run.

  • Survey platform

    comments and segment tags

    Culture AmpQualtricsPeakonGlint
  • BI

    theme dashboards above threshold

    Power BITableauLooker
  • Documents

    leadership narrative

    Google WorkspaceMicrosoft 365

What data does it need?

  • survey open-text responses
  • segment metadata at aggregate level
  • minimum group size rules
  • de-identification rules

How would you measure it?

time from survey close to themed summary shared, per wave; share of leaders above threshold receiving a narrative, per wave; quote re-identification checks passed, per wave

What does a first proof look like?

Take last year's comments, already de-identified, and have the agent theme them blind. Two people who read them originally compare themes and quotes.

You'd call it working when

The themes match what they found, no quote can be traced to a person or small team, and the summary is ready in days rather than weeks.

What usually goes wrong?

  • Quotes that are 'anonymized' but describe a unique role or event
  • Themes that echo the survey headings instead of what people said
  • Segment cuts that drift below the minimum group size when filters stack

What are the guardrails?

  • Aggregate only: no theme or quote below the minimum group size
  • Quotes stripped of names, roles, locations and dates before use
  • Never links a comment to a respondent or a manager
  • Works council consulted on the analysis approach where co-determination applies
  • Every shared summary logged with the segment and threshold used

What leaves your boundary is set per build; the inputs above are the ceiling, and where the model runs, what it retains, and the DPA are agreed with your security team before anything is connected.

Our read

Proven sensitivity medium Order: after a first win

Widely deployed and well understood. Low-risk to build well.

Some ATS, HRIS or LMS suites ship a version of this. Where yours already does the job well, switch it on. The agent earns its place when the native feature is missing, rigid, or does not respect your rules; the strategy month is where that call gets made.

Free text can identify individuals; works councils expect aggregate-only reporting with suppression below a minimum group size

Where it sits in the order

Needs the group-size and de-identification rules agreed first; then it is the most visible time saver in the area

Is an Open-Text Theme Synthesis Agent worth building for your function?

That depends on your numbers, your data, and what else is on the map for you. The strategy month works that out.

How the strategy month works

Book a call

Thirty minutes. Bring the number this would move.