Our Platform - Enterprise Intelligence
Our Enterprise Intelligence Platform integrates knowledge, models, tools, and workflows into a unified, intelligent system that accelerates innovation, adds velocity to value delivery, captures and scales institutional best practices, and unlocks capabilities long trapped in silos. By transforming fragmented data into contextual intelligence and embedding AI-driven decision-making across the enterprise, it enables faster, smarter, and more consistent outcomes. Designed with security, governance, and adaptability at its core, it serves as the operational backbone for a truly agile and insight-powered organization.
Weave enterprise data, partner data and public data into actionable intelligence
Knowledge Fabric is our foundational layer for an enterprise intelligence platform that unifies, contextualizes, and continuously organizes information across diverse systems, formats, and workflows. It weaves together structured data (like databases and spreadsheets), unstructured content (such as PDFs, emails, and documents), and real-time signals (from APIs, IoT, or digital interactions) into a coherent, queryable memory. Powered by semantic indexing, vector embeddings, graph databases and metadata-aware enrichment, the Knowledge Fabric transforms fragmented enterprise data into a dynamic, interconnected graph of meaning. This enables AI agents, decision-makers, and automation tools to access and reason over enterprise knowledge with contextual precision—dramatically accelerating time-to-insight and scaling operational intelligence.
Learn from your proprietary data sources, partner data and public data
Build rapidly evolving modular and adaptive AI
- Model Fabric is the dynamic layer of our enterprise intelligence platform that enables seamless creation, deployment, and governance of AI models tailored to business context. It abstracts and modularizes machine learning and large language model components into reusable, composable services that can be orchestrated across workflows and domains. Model Fabric supports hybrid AI strategies—combining proprietary models, open-source frameworks, and API-based LLMs—while maintaining rigorous versioning, monitoring, and performance evaluation pipelines. By integrating models into a governed, policy-aware ecosystem, the Model Fabric ensures AI outputs remain accurate, and aligned with enterprise objectives. It empowers teams to rapidly iterate on intelligent capabilities while retaining full control over model lifecycle and compliance.
Build flexible MLOps, MLDevSecOps pipelines
Equip your enterprise intelligence with action — safely and scalably.
- Tool Fabric is the operational layer of our enterprise intelligence platform that bridges reasoning with execution by connecting AI models to enterprise-approved tools, systems, and APIs. It enables intelligent agents to act, automate, and retrieve real-time data by securely invoking functions—ranging from database queries and CRM updates to document generation and workflow triggers. Tool Fabric includes robust schemas, access policies, and guardrails that ensure every action taken by AI is role based, observable, governed, and context-aware. With support for declarative tool registration, role-based permissions, and fine-grained logging, it empowers organizations to scale AI-powered operations while preserving control, traceability, and trust across all interactions.
Integrate your proprietary tools and partner tools into your intelligence infrastructure
Orchestrate decision flows with intelligent automation.
- Workflow Fabric is the coordination layer of our enterprise intelligence platform that seamlessly integrates AI-driven reasoning into human and machine workflows. It enables the orchestration of multi-step processes—such as approvals, escalations, document handling, and decision loops—by embedding AI agents, rules engines, and event triggers into business operations. Workflow Fabric supports dynamic routing, context-aware branching, and hybrid execution (human-in-the-loop + automation), ensuring that decisions are timely, auditable, and aligned with organizational policies. By abstracting complex logic into modular, reusable components, it allows enterprises to rapidly deploy and adapt intelligent workflows that respond to evolving business needs while enhancing agility, consistency, and operational intelligence.
Orchestrate role based best practices, tools to generate well reasoned workflows and automation
The Ensemble Adversarial ML System That Optimizes Your Models
- Benchmark your Models – Evaluate your models using standard model evaluation metrics and benchmark against industry standards
- Adversarial Agent Oversight – Deploys intelligent adversarial agents to continuously challenge, test, and refine ML models in real-time.
- Bias Detection & Correction – Adversarial agents stress-test models against benchmarks, eliminating hidden biases.
- Continuous Adversarial Training – Uses adversarial learning techniques to harden models against attacks, edge cases, and drift.
- Explainable AI Governance – Generates real-time reports on performance, adversarial challenges, and decision-making transparency.
- Depend on our Zero Shot Learning Models – Use GANs (Generative Adversarial Networks) and our Variational Autoencoders (VAEs) to generate synthetic data and use our Contrastive and Self-Supervised Learning models to create an effective adversarial testing infrastructure for your AI system.
- Seamless MLOps Integration – Works with all major ML frameworks (TensorFlow, PyTorch, Scikit-Learn) and deployment pipelines.
Make your AI system Enterprise Quality by hardening them with our Adversarial System.
AI Governance Re-imagined: The ML System That Governs ML Systems
- Bias Detection & Mitigation – Proactively identifies and corrects biases in AI decision-making.
- Contextual Relevance Optimization – Makes sure your RAG system and prompt engineered systems are being contextually relevant in a long interaction session. Evaluates an evolving context and score the ML system for contextual relevance.
- Zeitgeist Awareness – Integrates trend analysis to ensure models remain culturally and socially relevant, preventing outdated or tone-deaf outputs.
- Social Impact Monitoring – Predicts and flags potential for social instigation, preventing unintended consequences from AI-driven decisions.
- Transparent AI – Provides clear justifications for model decisions, ensuring trust, compliance, and accountability.
- Ethical AI at Scale – Governs ML deployments enterprise-wide, enforcing best practices in bias minimization, accuracy, and adaptability.
- Self-Evolving Intelligence – Learns from new data and feedback, ensuring governance rules evolve alongside AI systems.
- Seamless Integration – Compatible with major ML frameworks and regulatory standards, making responsible AI governance effortless.
Get an advanced start on Model governance with our Governance platform.