Sreda

Automatically extracts employee information from unstructured sources (emails, documents, spreadsheets, HRIS exports) using NLP and entity recognition to identify names, titles, departments, contact details, and employment history. The system normalizes inconsistent formatting across sources and deduplicates records using fuzzy matching and semantic similarity, consolidating fragmented employee data into standardized database records without manual intervention.

Ingests employee data from multiple heterogeneous sources (HRIS systems, ATS platforms, email directories, LinkedIn, internal databases) and automatically maps disparate schemas to a unified company database schema. Uses schema inference and field matching algorithms to identify equivalent fields across systems (e.g., 'emp_id' vs 'employee_number' vs 'staff_code') and resolves conflicts through configurable merge rules and priority weighting.

Stores normalized and aggregated employee data in a queryable database with full-text search, structured SQL-like queries, and semantic search capabilities powered by embeddings. Users can search for employees by name, title, department, skills, or natural language queries ('find all engineers in the NYC office who know Python') without writing SQL, with results ranked by relevance and confidence scores.

Continuously monitors the unified database for data quality issues including missing fields, formatting inconsistencies, duplicate records, outdated information, and logical contradictions (e.g., end date before start date). Uses rule-based validation and statistical anomaly detection to flag records that deviate from expected patterns, generating quality reports and suggesting corrections without modifying data automatically.

Accepts bulk uploads of employee data in multiple formats (CSV, Excel, JSON, XML) and processes them in batches through the extraction and normalization pipeline. Provides progress tracking, error reporting with line-by-line diagnostics, and rollback capabilities to revert failed imports. Supports scheduled batch imports from connected systems to keep the database synchronized with source systems on a defined cadence.

Augments internal employee data with external information from public sources (LinkedIn, company websites, industry databases, news feeds) to enrich company profiles with market context, competitive intelligence, and organizational insights. Uses web scraping, API integrations, and data matching to identify and link external data to internal records, filling gaps in internal data and providing market context for recruiting and business development.

Implements fine-grained access control allowing administrators to define which users/teams can view, edit, or export specific employee records or data fields based on roles (HR, recruiting, managers, executives). Supports field-level masking to hide sensitive information (SSN, salary, performance ratings) from unauthorized users and maintains audit logs of all data access and modifications for compliance and security monitoring.

Generates pre-built and custom reports on employee data including headcount by department/location, turnover rates, hiring pipeline metrics, skills inventory, and organizational structure visualizations. Uses aggregation and statistical analysis to surface insights (e.g., 'Engineering has 40% higher turnover than average') and supports scheduled report delivery via email or dashboard integration.

Exposes REST APIs for programmatic access to employee data, database operations, and reporting, enabling third-party integrations and custom workflows. Supports webhooks for real-time event notifications (new employee added, record updated, data quality issue detected) allowing downstream systems to react automatically. Includes SDKs for Python and JavaScript for easier integration.