Survey intelligence agent

Conversational analytics for a multinational manufacturer.

Survey scores and comments lived in flat files. Comparing regions, business units and account managers required repeated manual analysis.

An agent that turns a business question into a filtered analysis and a report.

The Challenge

• Survey data is locked in static files. Results accumulated in flat files segmented by cycle, region, account manager, and service aspect, with no live query layer on top.
• Manual extraction bottleneck. Extracting meaningful insights required analysts to run pivot tables and cross-reference dimensions manually, creating delays between data collection and decision-making.
• Business users are locked out. Commercial directors, account managers, and regional leads could not independently query their slice of results; every data request had to go through the analytics team.
• Free-text responses were noise, not signal. Thousands of open-ended comments required manual parsing to surface themes, complaints, and competitive mentions. Cycle-over-cycle, this was impractical at scale.
• Churn risks going undetected. Signals of dissatisfaction and competitor consideration buried in comment text were rarely caught before accounts were at risk.
• Underdelivering a strategic program. A structured, high-investment biannual survey program was not generating proportional commercial or operational value due to these access and analysis barriers.

We developed an AI-powered survey analytics agent that gives commercial and operations teams a natural language interface to the company's full customer satisfaction dataset, covering 35,000+ responses across multiple cycles, geographies, and service dimensions.

• KPI Dashboard on demand. Instantly surface overall satisfaction rate, average score, % satisfied (4–5), % dissatisfied (1–2), and response volume for any segment, without opening a spreadsheet.
• Aspect-level scoring. Compare how each service dimension is rated: Sales, Delivery, Product Quality, Technical Support, Credit & Collections, and Product Development.
• Segment breakdowns. Drill into results by country, business unit, market sector, or account manager in a single conversational query.
• Top and bottom client rankings. Identify the highest- and lowest-rated clients with minimum response thresholds applied for statistical reliability.
• Low-score analysis. Isolate poor ratings, identify which aspects and clients are driving dissatisfaction, and surface the specific questions that scored lowest.
• Free-text comment analysis. The agent groups and summarises open-ended responses, surfacing recurring themes, complaints and positive feedback for commercial review.
• Competitive intelligence. Automatically detects comments mentioning competitors or migration intent, flagging churn risk signals for commercial review in real time.
• Full report generation. Produces complete multi-section satisfaction reports combining all of the above into a single structured narrative, filterable by account manager, service aspect, or client.

The agent supports both typed queries and voice interaction. Under the hood, it uses a multi-model architecture: a lightweight model handles filtering and scope resolution, while a full-size model drives analysis and report generation, balancing response speed with analytical depth.

Outcome

Business teams query survey segments and generate reports on demand.

The workflow supports repeated analysis across survey cycles and segments, with commercial review of the resulting signals.