faster data modernization
0
x
reduction in manual migration
0
%
data validation accuracy
0
%
weeks to enterprise AI readiness
4-
4
Data & AI
Discovery
Semantic Twin – Ontology Graph
Semantic Twin
Knowledge Graph
Data Context
Lineage
Data
Modernization
Modernize Data to
Cloud Native
Data Movement
Code Composer & Converter
Data
Assurance
Industry
Data Model
Synthetic Data & AI Training
Synthetic data Generation
Connected Intelligence
Enterprise AI Workbench (Talk2Data)
Rapid MVP (Prebuilt Agents)
Concept to MVP
Prebuilt Business Agents
Insights &
Decision
Data & AI- Governance Observability,
Optimization
FinOPS & DataOps
The core of Wingspan. Eagle autonomously discovers, profiles, and maps your entire enterprise, building the Semantic Twin that every other agent relies on. No manual ontology mapping. No upfront definition. Just autonomous context discovery that deepens continuously.
Mines SQL logs, maps lineage, profiles workloads, uncovers hidden dependencies automatically
Continuously maps data relationships, business context, KPIs, and system interactions
Automatically creates governed glossaries, business context, one source of meaning across your enterprise
Faster discovery
Weeks to live
Auto context
Manual mapping
The code conversion engine inside Wingspan. Raven automates the translation of SQL, ETL scripts, and legacy code into cloud-native targets.
Teradata, Netezza, Oracle to BigQuery, Snowflake, or Azure Synapse. Raven reads the logic, converts the structure, and preserves business intent.
Converts procedural ETL constructs into modern cloud-native ELT patterns automatically.
AI-powered pattern handling eliminates the rework cycles that cause every conventional migration to run over budget and over timeline.
Cost reduction
Conversion accuracy
Velocity gain
Rework cycles
The data movement engine inside Wingspan. Condor orchestrates continuous data flow between systems, clouds, and platforms. It handles ingestion, pipeline execution, and cross-environment integration.
Moves data across heterogeneous environments, on-prem, multi-cloud, hybrid, without manual pipeline management or brittle point-to-point connections.
Condor adapts as systems change, volumes grow, and new sources come online. Integration stays current without re-engineering.
Connects fragmented systems so data flows to where decisions are made. No waiting on IT to build another connector.
Manual pipeline reduction
Data stranded in silos
Continuous flow
Systems connected
Pelican validates data with 100% accuracy at enterprise scale, without moving data. Cell-level comparison, lineage-powered triaging, and compliance enforcement built directly into the Semantic Twin.
Validates across heterogeneous stores without data movement, AES 256 encrypted
Governance and privacy controls enforced at the semantic layer, not added after the fact
Continuous quality tracking across completeness, accuracy, consistency, timeliness, validity, uniqueness
Validation accuracy
Cost savings
Data movement
Quality dimensions
Kingfisher generates realistic, relational synthetic data without accessing production data. KPI-aligned datasets for AI training, privacy-preserving and fully compliant, ready for production AI development.
Generates relational data from code (DDL/DML) or real datasets, preserving business logic and constraints
Synthetic data eliminates the compliance risk that blocks most AI training pipelines. No real records leave the environment.
RESTful API and LLM support built in. Kingfisher integrates directly into existing AI development pipelines and scales to whatever volume the model needs.
Faster data prep
Privacy violations
Scale range
Compliance with regulatory standards
The intelligence layer inside Wingspan. Phoenix transforms raw enterprise data into real-time conversational answers. Business users talk with data in plain language and get answers grounded in governed KPIs, without going through an analyst, filing a ticket, or waiting two weeks.
Business users ask questions in natural language, Phoenix answers using the Semantic Twin’s business context
Proactively surfaces anomalies, trends, and decision-critical insights, before business users know to ask
Every insight traces back to governed business metrics, no black boxes, full explainability
Analyst cost saved
Insight delivery
Analyst dependency
Business aligned
Eagle FinOps delivers real-time visibility into cloud spend, optimizes continuously across code and infrastructure, and recovers up to 40% of cloud waste automatically, without manual review cycles.
20+ code optimization categories, 50+ infrastructure categories, automated and continuous
Complete visibility into cloud consumption, workload behavior, and optimization opportunities
Eagle FinOps successfully reduced Google Cloud spending by 25% across customers in 2025
Cloud spend saved
Waste recovered
Infra categories
Continuous watch
faster migration
lower cost
data validation
code conversion
Platform
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