AI Overview & Nutrition Facts
Keyplay uses LLMs to enhance its account intelligence platform, helping to deliver richer insights, clearer segmentation, and more advanced ICP modeling. Keyplay does not process PII, never sends sensitive customer data to LLMs, and maintains privacy and security best practices when working with AI.
Category | Details |
AI Use Cases | ICP definition, account scoring, custom agents for account research & enrichment, data cleaning. |
Data Types | Public web data (websites, job posts, social media), Keyplay-collected company-level signals. |
Models Used | Production: OpenAI (latest GPT model families, embeddings). Experimental/Roadmap: Gemini, Claude. |
Privacy | No customer 1P data is used or shared. Keyplay opts out of OpenAI model training.Only public company data is processed. |
Processing | API calls to OpenAI are server-to-server; no client IPs or personal data transmitted. |
Update Cadence | Public data refreshed weekly–quarterly depending on type. Agents schedules are set by users. AI models and orchestration updated continuously with product development. |
Limitations | Outputs may contain inaccuracies or reflect bias in public data; coverage varies by company footprint. |
AI Capabilities
AI Agents for Account Research & Enrichment
Customers can define prompts and customize agents for a wide range of account intelligence use cases.
Agents crawl and analyze public web data to generate structured and unstructured account enrichment. More info here.
Agents are orchestrated by Keyplay; customer 1P data is never used or accessed.
Lookalike Model & Similarity Scores
Uses AI-powered vector analysis to find accounts with business models similar to your best-fit customers.
Scores accounts based on similarity to help you prioritize and build targeted plays around your ICP.
Industry Classification
AI-driven industry categorization provides more nuanced and reliable classifications than traditional frameworks like NAICS or self-reported data.
Supports primary and secondary categories for precise filtering and segmentation. For example, AI is a sub-category under SaaS & Cloud.
Account Research
AI analyzes large amounts of public data to produce detailed company descriptions, including:
Business model and operating activities
Market positioning
History and current status
Personality and culture
Data Quality
Uses AI to clean, validate, and enrich data for accuracy and consistency.
AI Risk Assessment
Keyplay never trains models on customer data.
All LLM inputs are limited to publicly available company information.
API calls to OpenAI are server-to-server; no client data or IP addresses are transmitted.
Keyplay opts out of OpenAI model training.
Risk Category | Details | Mitigation Strategies |
Data & Privacy | No customer 1P data used or shared. External AI only processes public company information. API calls are server-to-server; no personal data shared. | Enforced opt-out for OpenAI model training; strict architectural separation; privacy-centric design. |
Hallucination / Accuracy | Agents may generate inaccurate or speculative information, especially on ambiguous queries. | Human QA, preview on subset of accounts, customer feedback loop, backtesting against known lists. (docs.keyplay.io) |
Bias in Public Data | Outputs could reflect bias or gaps present in scraped public sources. | Multi-source collection, bias awareness in model design, continuous iteration. (docs.keyplay.io) |
Coverage Gaps | Companies with sparse public footprints may yield limited insights. | Signal layering, similarity scoring, enrichment cadence, fallback to higher-level signals. (docs.keyplay.io) |
Compliance & Security | Use of external models may raise governance concerns in regulated industries. | SOC 2 controls (audit process), internal output monitoring, no PII usage, security-first customer data handling. |
Operational Risk | System downtime or model drift could impact scoring and enrichment reliability. | Continuous monitoring, versioning, and rollback capabilities in pipeline orchestration. |