Candidates increasingly ask AI answer engines before they apply:
- “Should I work at [Company]?”
- “What’s it like to work at [Company]?”
- “Is the engineering team strong?”
- “Is the company stable?”
- “Does it have good work-life balance?”
Large language models (LLMs) now synthesize public information, third-party commentary, news and employer content into narrative summaries that shape first impressions.
AI has become the most important reputation surface.
AI-generated summaries across ChatGPT, Google AI Overviews, Perplexity and other large language models increasingly shape candidate evaluation before a career site is ever visited.
For many candidates, the first interaction with your employer brand is no longer your website, it is an AI-generated narrative synthesized from distributed sources.
This shifts employer reputation from a passive brand asset to an actively generated system output. That surface is dynamic, model-dependent and increasingly influential in candidate decision-making.
Built In's AI Employer Intelligence platform is the systematic measurement and management of how large language models describe an employer in response to candidate queries.
Built In provides the monitoring and action layer for this new AI reputation surface — starting with enterprise employers.
For large, multi-market organizations, AI-generated employer narratives create outsized risk and opportunity. Enterprise brands face:
- High application volume and cost of talent misalignment
- Complex, multi-role hiring across functions and geographies
- Disproportionate exposure to third-party commentary and press
- Greater sensitivity to perception shifts that impact investor, customer and talent markets simultaneously
AI systems do not summarize enterprise employers uniformly. They produce role-specific, geography-specific and narrative-dependent outputs that can diverge meaningfully from internal reality or brand intent.
Built In’s AI Employer Intelligence platform is designed to be enterprise-first:
- Multi-role and multi-market segmentation at scale
- Continuous cross-model monitoring across major LLM providers
- Source-level attribution to manage complex narrative ecosystems
- Structured intervention workflows aligned to enterprise hiring priorities
This is not lightweight brand monitoring, but operational infrastructure for managing AI-mediated employer reputation at scale.
Built In is not only a monitoring system. It is an indexed authority layer within the AI ecosystem.
Built In content, company profiles and employer signals already surface at scale across AI-generated search experiences. This creates structural leverage: organizations are not merely reacting to AI narratives, they are influencing a surface that AI models already reference.
Monitoring without distribution is diagnostic. Monitoring combined with authoritative distribution is strategic. For enterprise employers, this means AI reputation management is not limited to analysis — it includes the ability to shift narrative inputs at scale.
Built In's AI Employer Intelligence System
Built In’s platform operates as a closed-loop system across four integrated components:
- Observation
- Segmentation
- Attribution
- Intervention
Together, these create a measurable, inspectable framework for understanding and improving AI-driven employer perception.
1. Observation: Employer Brand Reputation Score (EBR)
The Employer Brand Reputation Score (EBR) quantifies how leading AI models describe your company across the themes that influence candidate decisions.
Built In:
- Submits structured prompts to major LLM providers (e.g., ChatGPT, Gemini, Perplexity)
- Rotates phrasing to reduce prompt bias
- Collects responses daily across model
- Analyzes sentiment, tone strength and recurring themes
- Normalizes outputs into 0–100 scores across core reputation dimensions
Measured dimensions include:
- Overall employer perception
- Career growth & development
- Compensation & benefits
- Culture & values
- Leadership & management
- Work-life balance & wellbeing
- Stability & growth
This is not a survey or review aggregation, but a direct measurement of AI-generated employer narratives. The score is one analytic output of Built In’s broader AI Employer Intelligence system.
2. Segmentation: Role & Market-Level Tracking
AI narratives are rarely uniform across functions or geographies.
An overall company score may mask meaningful variation, such as:
- Strong leadership perception, but burnout concerns in engineering
- Positive culture reputation in one location, but instability concerns in another
- Favorable sales team narratives, but skepticism around product execution
Built In enables employers to track AI perception at:
- Role or functional level (e.g., Engineering, Sales, Product)
- Location or market level
This segmentation transforms reputation from a single composite view into a multi-layer signal system.
It provides organizations with the ability to identify localized risk, emerging perception shifts and narrative divergence across teams and markets.
3. Attribution: Narrative Control & Citation Visibility
AI outputs are shaped by underlying sources. Built In surfaces the inputs influencing AI-driven reputation by providing:
- Visibility into the sources referenced or relied upon in model outputs
- A breakdown of owned vs. third-party citation influence
- Identification of dominant external narratives
- Detection of source gaps or imbalances
This does not attempt to manipulate AI responses. It makes the influence layer visible.
Narrative Control allows organizations to understand:
- What percentage of AI reputation is shaped by content they control
- Where external commentary outweighs owned messaging
- Which sources disproportionately drive perception
- Where misinformation or outdated content may be influencing models
Without attribution, AI reputation feels opaque. With attribution, it becomes diagnosable.
4. Intervention: Action Plan & Guided Remediation
Measurement without execution creates awareness not control.
AI-generated employer narratives are not static. They evolve as models retrain, sources shift and new content enters the influence layer. Intervention must therefore be continuous, structured and aligned to enterprise hiring priorities.
Built In translates AI reputation telemetry into prioritized, executable influence strategies.
From Insight to Structured Action
The Action Plan converts observed narrative patterns and citation imbalances into targeted interventions across owned and high-authority surfaces.
This includes:
- Role-specific narrative correction (e.g., engineering burnout themes, leadership skepticism, stability concerns)
- Location-based perception shifts tied to market-level hiring goals
- Source-level rebalancing when third-party commentary outweighs owned signals
- Identification of ambiguous or inconsistent messaging that amplifies model uncertainty
Intervention is not about manipulating AI responses. It is about improving the inputs AI systems synthesize.
Operational Execution Across Employer Surfaces
Built In supports execution through:
- Structured profile optimization aligned to high-frequency AI query patterns
- Standardized, AI-ready FAQ frameworks that reduce narrative ambiguity
- Content normalization to increase clarity, consistency and thematic reinforcement
- Signal alignment across job listings, company profiles and distributed employer content
These actions are sequenced based on:
- Reputation priorities
- Competitive positioning
- Model-level variation
- The strongest negative or unstable drivers in AI outputs
Closing the Enterprise Loop
Enterprise employers operate across functions, geographies and stakeholder groups. AI narratives can diverge meaningfully across these layers.
Intervention ensures that remediation is:
- Measurable
- Prioritized
- Role- and market-specific
- Continuously monitored for impact
This closes the system:
Observe → Segment → Attribute → Influence → Re-measure
Built In's AI Employer Intelligence platform is not static brand management. It is active governance of AI-mediated employer perception.
What This Platform Is — and Is Not
This is not:
- A review aggregator
- A survey tool
- A brand perception poll
- An AI response manipulation service
It is:
- A structured monitoring system for LLM-generated employer narratives
- A telemetry layer for AI search reputation
- A diagnostic and intervention framework for AI-driven employer perception
As AI increasingly intermediates candidate discovery and employer evaluation, LLM perception becomes an operational layer of reputation.
Built In's AI Employer Intelligence platform makes that layer measurable and manageable.
Why This Matters
Reputation used to be shaped primarily by:
- Review sites
- Press coverage
- Direct employer messaging
- Word of mouth
Now it is increasingly shaped by AI synthesis. When candidates ask AI whether to work at a company, the answer is generated in seconds and framed as authoritative.
That output can influence:
- Application volume
- Candidate quality
- Offer acceptance rates
- Employer brand positioning
- Competitive perception
Organizations that do not monitor this layer are operating without visibility into a growing influence channel.
Built In's AI Employer Intelligence platform provides that visibility — and the tools to act on it
