Gurugram, 10 June 2024 — Virtuos, an enterprise technology and digital transformation company, today announced Cohering, a new AI-platforming solution suite designed to help organizations operationalize artificial intelligence and translate emerging AI capabilities into measurable business value.
Introduced as part of the company's broader AI-Ambition initiative, Cohering brings together multiple artificial intelligence techniques, reusable development assets and governance practices within a composable enterprise technology framework.
The announcement comes at a defining moment for enterprise AI adoption.
Following the rapid rise of generative artificial intelligence and a period of widespread experimentation during 2023, organizations are increasingly seeking practical ways to move AI initiatives from prototypes into production.
While generative AI has demonstrated considerable potential across knowledge work, customer interactions, content generation and business automation, many enterprises continue to face challenges involving integration, reliability, governance and measurable returns.
Cohering has been conceived to address these challenges.
Rather than depending on a single AI model or technique, the solution suite promotes Composite AI—an approach that combines different forms of intelligence to solve complex business problems.
Virtuos intends to use Cohering to support enterprises pursuing intelligent automation, adaptive applications and more sophisticated Customer Experience and business operations.
The company announced plans to make the solution commercially available in selected countries during the third quarter of 2024, initially concentrating on customer-facing enterprise applications.
From AI Experimentation to Enterprise Execution
The emergence of generative AI has transformed the technology agenda of businesses worldwide.
Organizations are experimenting with large language models, conversational assistants, automated content generation and AI-supported decision-making.
However, developing a compelling demonstration is fundamentally different from operating a reliable AI application within an enterprise environment.
Production systems must integrate with existing applications, protect sensitive information, maintain consistent performance and comply with organizational policies.
They must also deliver outcomes that justify the investment required for implementation and ongoing operation.
In its June 2024 announcement, Virtuos highlighted three challenges facing AI initiatives:
- An estimated 54% of AI projects fail to reach completion.
- Moving an AI prototype into production can take an average of eight months.
- AI model development frequently takes place in loosely governed environments without sufficiently structured lifecycle controls.
These figures were presented in the original announcement as industry observations rather than measurements of Virtuos customer projects.
They illustrate the gap between enthusiasm for AI and the operational capabilities needed to deploy it successfully.
Cohering aims to help organizations bridge that gap through a structured approach to AI engineering, integration and governance.
The objective is to make AI more manageable as an enterprise capability rather than a collection of isolated experiments.
Introducing Cohering: A Composite AI Solution Mix
At the center of the announcement is Cohering, a suite of AI-platforming tools and composable products.
The name reflects the central proposition of bringing multiple AI capabilities together into a coherent architecture.
Different AI technologies offer different strengths.
Machine learning can identify patterns from historical data. Generative AI can create content and support natural-language interactions. Symbolic AI can represent explicit rules and relationships. Software tools can perform calculations, retrieve information and execute defined processes.
Many enterprise problems require a combination of these capabilities.
Cohering is designed around the principle that no single AI technique is sufficient for every business requirement.
Its composite architecture seeks to combine complementary approaches to create more capable and adaptable systems.
The announced solution mix includes four foundational components.
Data-Driven AI
Machine Learning
Logic & Tools
Symbolic AI
1. Data-Driven AI — Turning Enterprise Information into Intelligence
Data-driven AI uses information from business systems, customer interactions and operational activities to identify patterns and support informed decisions.
Enterprise organizations generate substantial volumes of information through CRM systems, service applications, commerce platforms and other digital environments.
However, this information often remains fragmented across systems and departments.
Cohering's data-driven approach is intended to help organizations make more effective use of relevant business information.
Potential applications include customer segmentation, service analysis, operational forecasting and the identification of process improvement opportunities.
The emphasis is on connecting intelligence to business context rather than treating AI models as independent technologies.
2. Machine Learning — Discovering Patterns and Improving Predictions
Machine learning provides methods for developing systems that recognize relationships within data and support predictions or classifications.
These techniques can be valuable when organizations need to identify trends, estimate future outcomes or detect anomalies.
In customer-facing applications, machine learning may support demand forecasting, customer propensity analysis, case classification and service prioritization.
Within Cohering, machine learning is intended to complement other AI methods rather than operate as a separate capability.
This approach creates opportunities to combine predictive insights with business rules, generative interfaces and automated workflows.
The resulting applications may support more effective decision-making and operational execution.
3. Logic and Tools — Connecting Intelligence with Business Processes
AI systems must often do more than generate responses or analyze information.
They may need to retrieve records, apply business rules, invoke software functions or initiate approved workflow activities.
Cohering identifies logic and tools as a core element of its composite approach.
This component emphasizes the importance of connecting AI capabilities with enterprise applications and structured processes.
For example, an intelligent customer service application may need to understand an inquiry, retrieve relevant information, determine applicable policies and recommend or execute a defined action.
The reliability of such systems depends on the relationship between AI reasoning and controlled business operations.
Cohering aims to support this integration through composable technologies and reusable patterns.
4. Symbolic AI — Introducing Explicit Knowledge and Rules
Symbolic AI represents knowledge through defined concepts, relationships, rules and logical structures.
Unlike purely statistical approaches, symbolic techniques can support systems where explicit reasoning and rule-based constraints are important.
This can be particularly relevant in enterprise environments involving complex business policies, contractual obligations or regulated processes.
Within a composite AI architecture, symbolic techniques may complement machine learning and generative models by providing structured representations of knowledge.
The combination creates opportunities to develop systems that are both flexible and grounded in defined business requirements.
Cohering's approach recognizes the potential value of bringing these techniques together rather than treating them as competing alternatives.
Composite AI: Why Combining Technologies Matters
The rapid development of generative AI has created enormous interest in large language models.
These models are powerful, but they also have limitations.
They may produce inaccurate responses, lack access to current enterprise information or struggle with tasks requiring deterministic calculations and strict business rules.
Composite AI offers a broader architectural approach.
Instead of requiring a single model to perform every task, organizations can combine specialized technologies according to the requirements of the business process.
A composite system might use a language model to interpret a customer's request, a retrieval mechanism to access approved knowledge, a predictive model to assess relevant information and a rules engine to enforce business policies.
Each component contributes a distinct capability.
This approach can help organizations design applications that are more appropriate to their operational requirements.
However, combining technologies also introduces engineering complexity.
Effective orchestration, testing, security, monitoring and governance remain essential.
Cohering seeks to provide a structured foundation for addressing these requirements.
Three Foundations of Adaptive AI: Learning, Awareness and Autonomy
The Cohering announcement identifies Learning, Awareness and Autonomy as foundational characteristics of the next generation of adaptive AI systems.
These concepts describe a progression from conventional automation toward applications capable of responding more intelligently to changing business conditions.
Learning
Learning concerns the ability of AI systems to improve their performance or adapt their behavior through relevant information, feedback and model updates. In enterprise environments, this may involve identifying changing customer patterns, improving classifications or refining recommendations. Effective learning also requires appropriate evaluation and controls to ensure that changes do not undermine system reliability.
Awareness
Awareness refers to an AI system's ability to use relevant context when interpreting information or supporting actions. Context may include a user's role, customer history, process status, business policies or the state of connected applications. An awareness-driven application can potentially deliver more relevant responses than a system operating without such information. This concept does not imply human consciousness. It describes the practical use of contextual information within software systems.
Autonomy
Autonomy concerns the ability of software to perform defined tasks or coordinate activities with reduced manual intervention. In enterprise settings, the degree of autonomy must be appropriate to the risks and requirements of each process. Some activities may be suitable for automation, while others require review, approval or direct human control.
Cohering's adaptive AI vision combines these three capabilities to support more responsive and intelligent enterprise applications.
Reusable AI Design Patterns and Shared Development Assets
A distinctive element of Cohering is its emphasis on creating a shared pool of development assets.
Enterprise AI projects frequently encounter similar architectural and operational challenges.
Teams must determine how models interact with business data, how workflows are orchestrated, how results are evaluated and how systems are monitored.
Developing each solution entirely from the beginning can increase complexity and implementation effort.
Cohering proposes the use of reusable assets, including AI Design Patterns, to support more consistent application development.
These patterns can provide reference approaches for recurring engineering requirements. Potential examples include:
By organizing reusable capabilities within a composable framework, Virtuos aims to help organizations accelerate development while maintaining architectural consistency.
The announcement establishes this shared-assets approach as a central design principle; it does not publish a detailed inventory of completed tools or patterns.
Responsible AI: Trust Must Be Engineered into the Platform
As AI systems become more deeply integrated into business operations, governance becomes increasingly important.
Organizations must consider the risks associated with inaccurate outputs, biased decisions, inappropriate data access and insufficient oversight.
The Cohering strategy emphasizes Responsible AI as a fundamental requirement for enterprise adoption. Its governance priorities include:
These considerations must extend beyond written policies.
They should influence system architecture, data handling, testing, deployment, monitoring and operational responsibilities.
For example, an enterprise AI application may require controls governing which information can be accessed, which actions can be executed automatically and when human approval is mandatory.
Organizations may also need processes for reviewing model performance, investigating failures and managing changes.
Cohering's platforming approach is intended to bring these concerns into the broader AI engineering lifecycle.
The objective is to help enterprises develop systems that are useful and scalable while remaining subject to appropriate controls.
Model Lifecycle Orchestration: Moving Beyond the Sandbox
One of the major challenges identified in the announcement is the transition from experimental AI environments to governed production systems.
Prototype applications are often developed with limited integration, temporary datasets and relatively informal operating procedures.
Enterprise deployment requires a more disciplined approach.
Models and applications must be evaluated, versioned, deployed, monitored and maintained.
- Evaluate
- Version
- Deploy
- Monitor
- Maintain
Organizations also need clarity regarding ownership, security, access permissions and operational responsibilities.
Cohering emphasizes model lifecycle orchestration as an important capability for scaling AI.
This involves coordinating the stages through which models and AI-enabled applications are developed and operated.
It also requires an understanding of how different AI techniques interact within a composite architecture.
By addressing these requirements, Virtuos aims to help enterprises establish a more systematic approach to AI engineering.
AI Workbook Planning: Four Pillars of Enterprise AI Strategy
Cohering is designed to support not only technical implementation but also the planning required to translate AI investments into business outcomes.
The announcement introduces AI Workbook Planning, a structured approach built around four strategic pillars.
Vision
Organizations must define what they want AI to achieve. A clear vision connects technology initiatives with business priorities, competitive positioning and operational requirements. Without this clarity, AI experimentation can become disconnected from meaningful business objectives.
Value Realization
AI initiatives should be evaluated according to the business value they are expected to create. Potential outcomes may include improved productivity, stronger customer experiences, faster processes or new revenue opportunities. Value realization requires appropriate objectives, measures and accountability.
Risk Management
Organizations must identify and manage risks arising from data use, model behavior, system integration and operational dependence on AI. Risk management should be incorporated throughout the AI lifecycle rather than treated as a final compliance activity.
Adoption Plans
Even technically capable systems may fail to create value if employees and customers do not use them effectively. Adoption planning addresses organizational readiness, skills, process changes, communication and ongoing support.
Together, these four pillars provide a framework for aligning AI technology with business strategy.
Initial Focus: CRM, Customer Experience and Contract Lifecycle Management
Virtuos announced that Cohering would initially concentrate on customer-facing enterprise applications.
The priority areas identified for the planned commercial introduction were:
Customer Relationship Management (CRM)
Exploring AI-enabled capabilities across sales, customer information, engagement and relationship management.
Customer Experience (CX)
Supporting intelligent service interactions, knowledge access, personalization and the coordination of customer journeys.
Contract Lifecycle Management (CLM)
Investigating opportunities to assist with contract-related workflows, document analysis, knowledge retrieval and process automation.
These areas align with Virtuos' established consulting expertise and enterprise technology partnerships.
They also provide practical environments in which different AI techniques can be combined.
For example, customer service may benefit from language understanding, knowledge retrieval and workflow automation, while contract management may require document interpretation alongside explicit rules and approval controls.
The initial industry focus therefore reflects the relationship between Virtuos' existing capabilities and its emerging AI engineering ambitions.
Collaboration Across the AI and Technology Ecosystem
The Cohering announcement also highlights Virtuos' collaboration with more than a dozen technology and AI partners.
The company intends to draw on this ecosystem to develop and introduce AI-platforming capabilities suited to enterprise requirements.
AI innovation depends on a rapidly evolving combination of models, cloud infrastructure, software frameworks, enterprise applications and specialized tools.
A partner ecosystem can provide access to complementary technologies and implementation expertise.
For Virtuos, the strategy builds on its experience working with enterprise technology providers.
The announcement does not identify the complete partner list or specify which collaborations are governed by formal commercial agreements.
Accordingly, the partnership activity is presented as part of the Cohering development and delivery ecosystem without attributing unconfirmed roles to individual providers.
Planned Commercial Availability in Q3 2024
Virtuos stated that Cohering was expected to become commercially available in selected countries during Q3 2024.
The proposed introduction would initially focus on CRM, Customer Experience and Contract Lifecycle Management applications.
This represented the company's announced product roadmap as of 10 June 2024.
Commercial availability, geographic coverage and the maturity of individual capabilities would depend on subsequent development and launch activities.
The announcement did not establish that every component of the proposed solution suite was already generally available.
This distinction is important because Cohering encompasses both a technology architecture and a wider vision for enterprise AI engineering.
AI-Ambition: A Broader Commitment to Enterprise Intelligence
Cohering forms part of Virtuos' wider AI-Ambition initiative.
The program was established to help organizations understand, adopt and operationalize artificial intelligence in ways aligned with their strategic objectives.
AI-Ambition recognizes that enterprise AI transformation requires more than selecting a model or purchasing a software tool.
Organizations need an appropriate technology portfolio, capable teams, operating models, governance structures and implementation strategies.
Cohering contributes to this vision through its focus on composite AI, reusable engineering assets and platform-level orchestration.
Together, the initiatives are intended to help organizations progress from AI experimentation toward more systematic adoption.
The relationship between AI-Ambition and Cohering reflects Virtuos' broader strategy of connecting technology innovation with practical enterprise transformation.
A New Chapter in Virtuos' Digital Innovation Journey
Since its founding in 2008, Virtuos has expanded its capabilities from CRM and Customer Experience consulting into enterprise cloud applications, digital automation, Employee Experience and emerging technologies.
The company has also pursued innovation and business incubation through its Experience Economy activities.
Cohering represents a further step in this evolution.
It introduces a structured approach to developing intelligent enterprise applications through the combination of multiple AI techniques.
The initiative also reinforces Virtuos' ambition to participate in the creation of new technology capabilities rather than focusing exclusively on the implementation of established enterprise platforms.
As businesses seek to convert AI investments into reliable operating systems, the demand for disciplined engineering, integration and governance is expected to grow.
Virtuos intends to address this opportunity through its AI-Ambition program and the Cohering solution mix.
With Cohering, Virtuos is advancing a vision of enterprise AI that moves beyond isolated models and experiments—toward connected, adaptive and responsibly governed intelligence capable of creating meaningful business value.
