Ravi Bommakanti, Chief Technology Officer at App Orchid, leads the corporate’s mission to assist enterprises operationalize AI across applications and decision-making processes. App Orchid’s flagship product, Easy Answers™, enables users to interact with data using natural language to generate AI-powered dashboards, insights, and really helpful actions.
The platform integrates structured and unstructured data—including real-time inputs and worker knowledge—right into a predictive data fabric that supports strategic and operational decisions. With in-memory Big Data technology and a user-friendly interface, App Orchid streamlines AI adoption through rapid deployment, low-cost implementation, and minimal disruption to existing systems.
Let’s start with the large picture—what does “agentic AI” mean to you, and the way is it different from traditional AI systems?
Agentic AI represents a fundamental shift from the static execution typical of traditional AI systems to dynamic orchestration. To me, it’s about moving from rigid, pre-programmed systems to autonomous, adaptable problem-solvers that may reason, plan, and collaborate.
What truly sets agentic AI apart is its ability to leverage the distributed nature of data and expertise. Traditional AI often operates inside fixed boundaries, following predetermined paths. Agentic systems, nevertheless, can decompose complex tasks, discover the precise specialized agents for sub-tasks—potentially discovering and leveraging them through agent registries—and orchestrate their interaction to synthesize an answer. This idea of agent registries allows organizations to effectively ‘rent’ specialized capabilities as needed, mirroring how human expert teams are assembled, somewhat than being forced to construct or own every AI function internally.
So, as an alternative of monolithic systems, the long run lies in creating ecosystems where specialized agents could be dynamically composed and coordinated – very similar to a talented project manager leading a team – to handle complex and evolving business challenges effectively.
How is Google Agentspace accelerating the adoption of agentic AI across enterprises, and what’s App Orchid’s role on this ecosystem?
Google Agentspace is a major accelerator for enterprise AI adoption. By providing a unified foundation to deploy and manage intelligent agents connected to numerous work applications, and leveraging Google’s powerful search and models like Gemini, Agentspace enables firms to rework siloed information into actionable intelligence through a standard interface.
App Orchid acts as a significant semantic enablement layer inside this ecosystem. While Agentspace provides the agent infrastructure and orchestration framework, our Easy Answers platform tackles the critical enterprise challenge of creating complex data comprehensible and accessible to agents. We use an ontology-driven approach to construct wealthy knowledge graphs from enterprise data, complete with business context and relationships – precisely the understanding agents need.
This creates a robust synergy: Agentspace provides the robust agent infrastructure and orchestration capabilities, while App Orchid provides the deep semantic understanding of complex enterprise data that these agents require to operate effectively and deliver meaningful business insights. Our collaboration with the Google Cloud Cortex Framework is a main example, helping customers drastically reduce data preparation time (as much as 85%) while leveraging our platform’s industry-leading 99.8% text-to-SQL accuracy for natural language querying. Together, we empower organizations to deploy agentic AI solutions that actually grasp their business language and data intricacies, accelerating time-to-value.
What are real-world barriers firms face when adopting agentic AI, and the way does App Orchid help them overcome these?
The first barriers we see revolve around data quality, the challenge of evolving security standards – particularly ensuring agent-to-agent trust – and managing the distributed nature of enterprise knowledge and agent capabilities.
Data quality stays the bedrock issue. Agentic AI, like every AI, provides unreliable outputs if fed poor data. App Orchid tackles this foundationally by making a semantic layer that contextualizes disparate data sources. Constructing on this, our unique crowdsourcing features inside Easy Answers engage business users across the organization—those that understand the information’s meaning best—to collaboratively discover and address data gaps and inconsistencies, significantly improving reliability.
Security presents one other critical hurdle, especially as agent-to-agent communication becomes common, potentially spanning internal and external systems. Establishing robust mechanisms for agent-to-agent trust and maintaining governance without stifling mandatory interaction is vital. Our platform focuses on implementing security frameworks designed for these dynamic interactions.
Finally, harnessing distributed knowledge and capabilities effectively requires advanced orchestration. App Orchid leverages concepts just like the Model Context Protocol (MCP), which is increasingly pivotal. This permits the dynamic sourcing of specialised agents from repositories based on contextual needs, facilitating fluid, adaptable workflows somewhat than rigid, pre-defined processes. This approach aligns with emerging standards, comparable to Google’s Agent2Agent protocol, designed to standardize communication in multi-agent systems. We help organizations construct trusted and effective agentic AI solutions by addressing these barriers.
Are you able to walk us through how Easy Answers™ works—from natural language query to insight generation?
Easy Answers transforms how users interact with enterprise data, making sophisticated evaluation accessible through natural language. Here’s how it really works:
- Connectivity: We start by connecting to the enterprise’s data sources – we support over 200 common databases and systems. Crucially, this often happens without requiring data movement or replication, connecting securely to data where it resides.
- Ontology Creation: Our platform routinely analyzes the connected data and builds a comprehensive knowledge graph. This structures the information into business-centric entities we call Managed Semantic Objects (MSOs), capturing the relationships between them.
- Metadata Enrichment: This ontology is enriched with metadata. Users provide high-level descriptions, and our AI generates detailed descriptions for every MSO and its attributes (fields). This combined metadata provides deep context concerning the data’s meaning and structure.
- Natural Language Query: A user asks a matter in plain business language, like “Show me sales trends for product X within the western region in comparison with last quarter.”
- Interpretation & SQL Generation: Our NLP engine uses the wealthy metadata within the knowledge graph to know the user’s intent, discover the relevant MSOs and relationships, and translate the query into precise data queries (like SQL). We achieve an industry-leading 99.8% text-to-SQL accuracy here.
- Insight Generation (Curations): The system retrieves the information and determines essentially the most effective method to present the reply visually. In our platform, these interactive visualizations are called ‘curations’. Users can routinely generate or pre-configure them to align with specific needs or standards.
- Deeper Evaluation (Quick Insights): For more complex questions or proactive discovery, users can leverage Quick Insights. This feature allows them to simply apply ML algorithms shipped with the platform to specified data fields to routinely detect patterns, discover anomalies, or validate hypotheses without having data science expertise.
This whole process, often accomplished in seconds, democratizes data access and evaluation, turning complex data exploration into an easy conversation.
How does Easy Answers bridge siloed data in large enterprises and ensure insights are explainable and traceable?
Data silos are a serious impediment in large enterprises. Easy Answers addresses this fundamental challenge through our unique semantic layer approach.
As an alternative of costly and sophisticated physical data consolidation, we create a virtual semantic layer. Our platform builds a unified logical view by connecting to diverse data sources where they reside. This layer is powered by our knowledge graph technology, which maps data into Managed Semantic Objects (MSOs), defines their relationships, and enriches them with contextual metadata. This creates a standard business language comprehensible by each humans and AI, effectively bridging technical data structures (tables, columns) with business meaning (customers, products, sales), no matter where the information physically lives.
Ensuring insights are trustworthy requires each traceability and explainability:
- Traceability: We offer comprehensive data lineage tracking. Users can drill down from any curations or insights back to the source data, viewing all applied transformations, filters, and calculations. This provides full transparency and auditability, crucial for validation and compliance.
- Explainability: Insights are accompanied by natural language explanations. These summaries articulate what the information shows and why it’s significant in business terms, translating complex findings into actionable understanding for a broad audience.
This mixture bridges silos by making a unified semantic view and builds trust through clear traceability and explainability.
How does your system ensure transparency in insights, especially in regulated industries where data lineage is critical?
Transparency is totally non-negotiable for AI-driven insights, especially in regulated industries where auditability and defensibility are paramount. Our approach ensures transparency across three key dimensions:
- Data Lineage: That is foundational. As mentioned, Easy Answers provides end-to-end data lineage tracking. Every insight, visualization, or number could be traced back meticulously through its entire lifecycle—from the unique data sources, through any joins, transformations, aggregations, or filters applied—providing the verifiable data provenance required by regulators.
- Methodology Visibility: We avoid the ‘black box’ problem. When analytical or ML models are used (e.g., via Quick Insights), the platform clearly documents the methodology employed, the parameters used, and relevant evaluation metrics. This ensures the ‘how’ behind the insight is as transparent because the ‘what’.
- Natural Language Explanation: Translating technical outputs into comprehensible business context is crucial for transparency. Every insight is paired with plain-language explanations describing the findings, their significance, and potentially their limitations, ensuring clarity for all stakeholders, including compliance officers and auditors.
Moreover, we incorporate additional governance features for industries with specific compliance needs like role-based access controls, approval workflows for certain actions or reports, and comprehensive audit logs tracking user activity and system operations. This multi-layered approach ensures insights are accurate, fully transparent, explainable, and defensible.
How is App Orchid turning AI-generated insights into motion with features like Generative Actions?
Generating insights is priceless, but the actual goal is driving business outcomes. With the right data and context, an agentic ecosystem can drive actions to bridge the critical gap between insight discovery and tangible motion, moving analytics from a passive reporting function to an lively driver of improvement.
Here’s how it really works: When the Easy Answers platform identifies a major pattern, trend, anomaly, or opportunity through its evaluation, it leverages AI to propose specific, contextually relevant actions that could possibly be taken in response.
These aren’t vague suggestions; they’re concrete recommendations. As an illustration, as an alternative of just flagging customers at high risk of churn, it would recommend specific retention offers tailored to different segments, potentially calculating the expected impact or ROI, and even drafting communication templates. When generating these recommendations, the system considers business rules, constraints, historical data, and objectives.
Crucially, this maintains human oversight. Really useful actions are presented to the suitable users for review, modification, approval, or rejection. This ensures business judgment stays central to the decision-making process while AI handles the heavy lifting of identifying opportunities and formulating potential responses.
Once an motion is approved, we are able to trigger an agentic flow for seamless execution through integrations with operational systems. This might mean triggering a workflow in a CRM, updating a forecast in an ERP system, launching a targeted marketing task, or initiating one other relevant business process – thus closing the loop from insight on to final result.
How are knowledge graphs and semantic data models central to your platform’s success?
Knowledge graphs and semantic data models are absolutely the core of the Easy Answers platform; they elevate it beyond traditional BI tools that always treat data as disconnected tables and columns devoid of real-world business context. Our platform uses them to construct an intelligent semantic layer over enterprise data.
This semantic foundation is central to our success for several key reasons:
- Enables True Natural Language Interaction: The semantic model, structured as a knowledge graph with Managed Semantic Objects (MSOs), properties, and defined relationships, acts as a ‘Rosetta Stone’. It translates the nuances of human language and business terminology into the precise queries needed to retrieve data, allowing users to ask questions naturally without knowing underlying schemas. This is vital to our high text-to-SQL accuracy.
- Preserves Critical Business Context: Unlike easy relational joins, our knowledge graph explicitly captures the wealthy, complex web of relationships between business entities (e.g., how customers interact with products through support tickets and buy orders). This enables for deeper, more contextual evaluation reflecting how the business operates.
- Provides Adaptability and Scalability: Semantic models are more flexible than rigid schemas. As business needs evolve or recent data sources are added, the knowledge graph could be prolonged and modified incrementally without requiring an entire overhaul, maintaining consistency while adapting to vary.
This deep understanding of knowledge context provided by our semantic layer is key to every little thing Easy Answers does, from basic Q&A to advanced pattern detection with Quick Insights, and it forms the essential foundation for our future agentic AI capabilities, ensuring agents can reason over data meaningfully.
What foundational models do you support, and the way do you permit organizations to bring their very own AI/ML models into the workflow?
We consider in an open and versatile approach, recognizing the rapid evolution of AI and respecting organizations’ existing investments.
For foundational models, we maintain integrations with leading options from multiple providers, including Google’s Gemini family, OpenAI’s GPT models, and outstanding open-source alternatives like Llama. This enables organizations to decide on models that best fit their performance, cost, governance, or specific capability needs. These models power various platform features, including natural language understanding for queries, SQL generation, insight summarization, and metadata generation.
Beyond these, we offer robust pathways for organizations to bring their very own custom AI/ML models into the Easy Answers workflow:
- Models developed in Python can often be integrated directly via our AI Engine.
- We provide seamless integration capabilities with major cloud ML platforms comparable to Google Vertex AI and Amazon SageMaker, allowing models trained and hosted there to be invoked.
Critically, our semantic layer plays a key role in making these potentially complex custom models accessible. By linking model inputs and outputs to the business concepts defined in our knowledge graph (MSOs and properties), we allow non-technical business users to leverage advanced predictive, classification or causal models (e.g., through Quick Insights) without having to know the underlying data science – they interact with familiar business terms, and the platform handles the technical translation. This truly democratizes access to stylish AI/ML capabilities.
Looking ahead, what trends do you see shaping the following wave of enterprise AI—particularly in agent marketplaces and no-code agent design?
The subsequent wave of enterprise AI is moving towards highly dynamic, composable, and collaborative ecosystems. Several converging trends are driving this:
- Agent Marketplaces and Registries: We’ll see a major rise in agent marketplaces functioning alongside internal agent registries. This facilitates a shift from monolithic builds to a ‘rent and compose’ model, where organizations can dynamically discover and integrate specialized agents—internal or external—with specific capabilities as needed, dramatically accelerating solution deployment.
- Standardized Agent Communication: For these ecosystems to operate, agents need common languages. Standardized agent-to-agent communication protocols, comparable to MCP (Model Context Protocol), which we leverage, and initiatives like Google’s Agent2Agent protocol, have gotten essential for enabling seamless collaboration, context sharing, and task delegation between agents, no matter who built them or where they run.
- Dynamic Orchestration: Static, pre-defined workflows will give method to dynamic orchestration. Intelligent orchestration layers will select, configure, and coordinate agents at runtime based on the particular problem context, resulting in way more adaptable and resilient systems.
- No-Code/Low-Code Agent Design: Democratization will extend to agent creation. No-code and low-code platforms will empower business experts, not only AI specialists, to design and construct agents that encapsulate specific domain knowledge and business logic, further enriching the pool of accessible specialized capabilities.
App Orchid’s role is providing the critical semantic foundation for this future. For agents in these dynamic ecosystems to collaborate effectively and perform meaningful tasks, they need to know the enterprise data. Our knowledge graph and semantic layer provide exactly that contextual understanding, enabling agents to reason and act upon data in relevant business terms.
How do you envision the role of the CTO evolving in a future where decision intelligence is democratized through agentic AI?
The democratization of decision intelligence via agentic AI fundamentally elevates the role of the CTO. It shifts from being primarily a steward of technology infrastructure to becoming a strategic orchestrator of organizational intelligence.
Key evolutions include:
- From Systems Manager to Ecosystem Architect: The main target moves beyond managing siloed applications to designing, curating, and governing dynamic ecosystems of interacting agents, data sources, and analytical capabilities. This involves leveraging agent marketplaces and registries effectively.
- Data Strategy as Core Business Strategy: Ensuring data is just not just available but semantically wealthy, reliable, and accessible becomes paramount. The CTO will likely be central in constructing the knowledge graph foundation that powers intelligent systems across the enterprise.
- Evolving Governance Paradigms: Latest governance models will likely be needed for agentic AI – addressing agent trust, security, ethical AI use, auditability of automated decisions, and managing emergent behaviors inside agent collaborations.
- Championing Adaptability: The CTO will likely be crucial in embedding adaptability into the organization’s technical and operational fabric, creating environments where AI-driven insights result in rapid responses and continuous learning.
- Fostering Human-AI Collaboration: A key aspect will likely be cultivating a culture and designing systems where humans and AI agents work synergistically, augmenting one another’s strengths.
Ultimately, the CTO becomes less about managing IT costs and more about maximizing the organization’s ‘intelligence potential’. It’s a shift towards being a real strategic partner, enabling your entire business to operate more intelligently and adaptively in an increasingly complex world.
