Wingspan 2.0: Agentic AI For Enterprise Data Modernization

The Intelligence Fabric
for the AI-First Enterprise

Introducing the next evolution of Wingspan, Onix’s agentic AI platform powered by a semantic twin that builds the cloud-native knowledge foundation enterprises need. As one of the best AI agent platforms for cloud and data engineering, it continuously modernizes the data platform, optimizes operations intelligently, and enables high-accuracy, low-hallucination AI at scale.

Performance and results that keep compounding with an AI agents platform

faster data modernization

x

reduction in manual migration

%

data validation accuracy

0 %

to enterprise AI readiness

4- 0 week

You’ve been transforming for years. But somehow, the finish line keeps moving.

Modernization moves fast, yet new systems quickly become legacy. AI pilots stall, cloud costs rise without clarity, and engineering teams remain tied to ongoing migrations. This isn’t an execution gap, it’s a structural challenge in how transformation is approached, and why an AI agent platform is essential to break the cycle.

of organizations estimate their data is not AI-ready

0 %

million lost every year to poor data quality

$ 0

of AI projects lacking AI-ready data will be abandoned through 2026

0 %

of AI projects make it into production

0 %

What changes everything

The missing semantic layer.
Now it exists.

Wingspan is the AI agent platform for data to AI automation, autonomously building a living model of your enterprise. That model is your Semantic Twin, where every data relationship, lineage path, process, and KPI is automatically mapped, continuously maintained, and available to every initiative that needs it.

Data & AI
Discovery

Semantic Twin – Ontology Graph

Semantic Twin
Knowledge Graph
Data Context
Lineage

Metadata
Dictionary

Business &
Metadata
Context

Controlled
Vocabulary

Taxonomy

Ontology &
Thesaurus

Knowledge
Graph

Governance

Semantic
Inference

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

Metadata
Dictionary

Business &
Metadata
Context

Controlled
Vocabulary

Taxonomy

Ontology &
Thesaurus

Knowledge
Graph

Governance

Semantic
Inference

Enterprise knowledge engine – Semantic Twin

Full enterprise impact because everything connects to one Semantic Twin, enabled by an AI agents platform

Data Platform Modernization

Modernization happens at 3x velocity with full dependency visibility, automated legacy-to-cloud conversion, and 100% validation, making transformation predictable and continuous

Governance: compliance built in, enforced before execution
Enterprise Modernization : legacy to cloud without risk

Intelligent Operations

Operations stop being reactive as autonomous agents monitor, optimize, and govern across DataOps, AIOps, and FinOps, detecting issues, performing RCA, and automating remediation

Unified Operations: Centralized knowledge graph, always on
AI Intelligence: context-aware agents, enterprise-ready

Enterprise Intelligence

Your enterprise turns data into a competitive advantage as users talk to data and get real-time answers, with AI moving from pilot to production with high accuracy and low hallucinations tied to KPIs

AI Creation: KPI-aligned synthetic data at scale
Decision Making: Real-time answers, no analyst bottleneck with AI agents for data analytics.

What changes for you

Your engineers stop maintaining the past

Here is what your enterprise looks like before and after Wingspan, written in the

language of outcomes your board cares about.

Before Wingspan

6–8 weeks rebuilding context at the start of every project
Migrations running 40% over timeline with constant rework
AI pilots stalling before production, data wasn't ready
Cloud bill growing 15% per quarter with no clear explanation
Business teams waiting days for analyst-generated insights without analytics workflow automation using AI agents
Data warehouse migration remained manual, slow, and dependent on fragmented knowledge

After Wingspan

Discovery happens automatically, every initiative starts ahead
Migrations run at 3x velocity with zero rework cycles
AI moves from pilot to production, the foundation finally supports it
Cloud waste recovered automatically, 25% average reduction
Engineers freed from maintenance to build what's next
Business users get real-time answers, no analyst needed with agentic AI for data analytics
Data warehouse migration using AI agents becomes automated, accurate, and continuously optimized

The Semantic Advantage

Traditional approaches require manual ontology mapping, creating massive

effort and platform lock-in. Wingspan discovers context autonomously.

Wingspan
Traditional Approaches

Capability	Wingspan	Traditional Approaches
Capability	Capability		$3999 /year
Semantic Twin / Knowledge Foundation	Autonomous	Manual or absent
Time to enterprise AI readiness	4–6 weeks	6–18 months
Data ontology mapping	Fully automated	Manual, months of effort
Platform lock-in	Zero lock-in	High lock-in
Continuous vs one-time transformation	Continuous	One-time projects
Migration velocity	3x faster	Baseline
Context retention across phases	Persistent semantic twin	Lost between projects
Cloud cost optimization	Automated, continuous	Manual reviews
		Buy Now

Frequently Asked Questions

1. What is Wingspan 2.0?

Wingspan 2.0 is the Enterprise Intelligence Fabric from Onix, an agentic AI platform built around a Semantic Twin: a living, continuously updating model of an enterprise’s data, systems, lineage, and business meaning. Rather than treating migration, AI enablement, and operations as separate one-time projects, Wingspan deploys autonomous, context-aware agents, Eagle, Raven, Condor, Pelican, Phoenix, Kingfisher, and Swift, that coordinate discovery, migration, validation, and intelligence within a single continuous framework. The platform operates across three pillars: Data Platform Modernization, Autonomous Operations, and Connected Intelligence. For technical buyers, the distinction that matters is this: most platforms require you to build context before AI works. Wingspan builds the context automatically, in 4 to 6 weeks, and every agent in the platform operates on it from day one.

2. What makes Wingspan an ai agent platform rather than a collection of point tools?

Most platforms marketed as AI agent tools are really automation scripts with a chatbot layer on top. Wingspan is built differently as a true ai agent platform. Every agent in the platform, Eagle, Raven, Condor, Pelican, Phoenix, Kingfisher, and Swift, operates on the same Semantic Twin. That shared context is the actual architecture decision that makes Wingspan an ai agent platform and not a toolset. An agent that doesn’t share context with the rest of your stack isn’t agentic. It’s scripted. Wingspan’s agents coordinate discovery, migration, validation, operations, and AI enablement as a continuous, self-directed workflow, not as isolated tasks running in sequence. The outcome for technical buyers: you deploy once and the platform keeps reasoning over your environment as it changes, instead of running a fixed pipeline that goes stale the day requirements shift.

3. What does Wingspan offer as one of the ai agent platforms designed specifically for enterprise migration and modernization?

Wingspan’s Migration and Modernization Suite is purpose-built, not retrofitted from a general-purpose agent framework. As one of the few ai agent platforms built specifically for this use case, Eagle handles discovery and assessment, Raven converts legacy code, SQL, ETL, and stored procedures into cloud-native equivalents, and Condor handles data movement. Pelican validates 100% of migrated data at cell level with pushdown architecture, no bulk movement required for validation. Each agent has a defined role, and all three execute against the same Semantic Twin Eagle builds upfront. The benefit that shows up on a project timeline: organizations running this suite see 3x velocity improvement over manual migration, because the discovery and validation work that used to consume months gets executed by agents that already share full context of the environment.

4. How does Wingspan's architecture reduce AI hallucination in production deployment?

Hallucination in enterprise AI is rarely a model problem. It’s a context problem. When an LLM doesn’t have grounded, consistent business meaning to reason against, it guesses, and the guesses are wrong often enough to make production deployment risky. Wingspan’s agents avoid this because they operate on the Semantic Twin rather than raw tables. Phoenix, the conversational intelligence agent, answers questions grounded in one glossary, one lineage, one source of truth, not whatever pattern it infers from disconnected data. This is the basis for Wingspan’s 40% hallucination reduction metric. The outcome for a CDO or CTO: AI agents that are explainable and auditable because their answers trace back to a documented knowledge graph, not a black box inference.

5. What separates Wingspan from other best ai agent platforms when it comes to governance and auditability?

Most platforms claiming to be among the best ai agent platforms treat governance as a feature you bolt on after deployment. Wingspan treats it as a layer that wraps every agent by design. Policies, compliance, and oversight sit around the Semantic Twin and everything built on it, security, privacy, and access control are enforced at the source, not added retroactively once something goes wrong. Every agent decision, whether it’s Raven converting code or Phoenix answering a query, traces back through the knowledge graph to a documented source of meaning. For technical evaluators benchmarking best ai agent platforms on security posture, this matters in practice: you get one place to govern data, semantics, and AI behavior across the entire agent fleet, instead of stitching together compliance controls per tool.

6. What concrete outcomes should a CTO expect from deploying Wingspan versus building agent capability in-house?

Building agent infrastructure in-house means solving the context problem before you solve any business problem, most internal builds stall here. Wingspan ships with that already solved. The Semantic Twin is built automatically by Eagle in 4 to 6 weeks. From there, the platform delivers measurable outcomes across three pillars: data platform modernization through Raven, Condor, and Pelican, autonomous operations through continuous monitoring and intelligent agents, and connected intelligence through Phoenix’s natural language access to enterprise knowledge. Customers typically see demonstrable migration velocity and AI readiness improvements within the first quarter, not after a year of internal platform engineering. For a technical decision-maker, the calculation is straightforward: building this internally means rebuilding what Wingspan already operationalizes, and doing it without the years of architectural iteration already baked into the platform.

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Wingspan 2.0

The Intelligence Fabric
for the AI-First Enterprise

Performance and results that keep compounding with an AI agents platform