Pearlify isn't just a chat interface. It is a comprehensive harness designed to securely ground LLMs in your data, code, and business logic.
Execute secure Python development and heavy data analytics directly in the platform. The native `analyticsEngine` empowers agents to compute metrics, process large CSVs, and dynamically format .docx files within a sandboxed environment.
A native, user-specific file directory. Upload .pdf, .docx, and .csv files safely. The agent can seamlessly read, modify, and create files directly within the interface, strictly siloed to the individual user to prevent data leakage.
Automate complex, repeatable enterprise jobs. Design robust multi-step pipelines where a single pipeline functions as an independent workflow, or combine multiple pipelines into a massive, orchestrated execution flow.
Deploy sophisticated AI models using Single Agent or Multi-Agent Architecture (SWARM). Orchestrate dynamic collaboration between specialized, hierarchical agents to solve intricate, multi-faceted problems autonomously.
Deploy zero-configuration chat interfaces restricted to specific organizational directories. Policy Bots allow employees to safely query governed documents (like HR policies or IT protocols) with guaranteed factual adherence.
Review uploaded documents and intuitively organize them into semantic Collections. Search across key concepts, analyze topic clusters, and prepare unstructured data for targeted Knowledge Graph exploration.
Transform unstructured text into structured intelligence. Leverage Custom Ontology-Focused Ingestion to map complex entity relationships and build highly connected, interactive Enterprise Knowledge Graphs.
Bring your enterprise structured data to life. Empower agents to seamlessly translate natural language into secure, optimized SQL queries to extract accurate, real-time insights from your internal relational databases.
Connect Pearlify natively to your broader technological ecosystem. Utilize the Model Context Protocol (MCP) and safe API integrations to interface with external SaaS applications and internal enterprise microservices.
Ensure comprehensive auditability for all AI operations. Access granular Agent Traces to review the exact step-by-step reasoning, internal thoughts, and actions executed by the agent during complex problem-solving.
Maintain strict governance over autonomous processes. Set rigid limits on agentic loops and require explicit Human-in-the-Loop approval, feedback, or parameter overrides before an agent can proceed with critical operations.
Stop playing with chat interfaces. Start building automated workflows, utilizing secure Python analytics, and querying bespoke knowledge graphs with Pearlify.