Generative AI is rapidly transforming the way enterprises approach automation, data management, and customer engagement. Large Language Models (LLMs) such as GPT have garnered substantial attention and opened up possibilities that were previously out of reach. Yet, a critical question remains:
Are LLMs standalone solutions — or are they powerful tools that must be woven into a broader AI infrastructure?
At first glance, many organizations treat LLMs as isolated systems. However, they quickly discover that unlocking the full potential of Generative AI requires a more robust AI infrastructure. This reality is not surprising: while LLMs are exceptionally advanced statistical models, they depend on additional tools, data pipelines, and careful orchestration to deliver enterprise-grade results. Even relatively simple retrieval-augmented generation (RAG) workflows — like extracting insights from a set of PDF documents — commonly require numerous components for ingestion, parsing, embedding, inference, and more.
Building AI infrastructure has traditionally been a major challenge for enterprises, and GenAI only adds another layer of complexity. To address this, we must rethink how we design and deploy AI infrastructure in a world increasingly driven by LLMs.
At Corvic AI, we believe there are three critical building blocks — Data, Tools, and Orchestration — that must be reimagined specifically for Generative AI.
1. Data
Don’t Treat Everything as Text
A common mistake in Generative AI projects is to homogenize all data into plain text. Yes, LLMs process text, but simply flattening all information can erase valuable context and nuance. Images, tabular data, and domain-specific formats each carry inherent signals that can improve accuracy and reduce hallucinations.
Many (Gen) AI initiatives fail because of issues like poor data quality, integration difficulties, and inadequate data management.
Key Insight:When designing GenAI solutions, preserve the intrinsic structure and format of data. This ensures LLMs — and the surrounding systems — can tap into richer, more reliable information.
2. Orchestration
LLMs Are One Piece of the Puzzle
LLMs excel at generating human-like text but cannot address enterprise complexities alone. To manage their inherent probabilistic nature and fully realize their potential, they must be integrated into a compound system alongside additional tools such as:
- Data preprocessing pipelines
- Graph-based AI models
- Traditional ML models
- Semantic retrieval
- OLAP analytics
These tools help mitigate risks such as hallucinations while amplifying the value LLMs provide across a range of business applications.
Another challenge is the rapid proliferation of data tools, which has led to a fragmented technological landscape, increasing inefficiencies and cognitive load on employees.
Key Insight:
View LLMs as part of a compound system. Additional tools are essential for controlling the entire lifecycle, from data ingestion and parsing to embedding creation, inference, and results presentation.
3. Productionization
Streamlining Complexity for the End User
As GenAI applications and the supporting infrastructure become more complex, effective deployment becomes crucial. Without robust frameworks, users face a steep learning curve and a higher risk of errors as most GenAI applications don’t make it to production. Automated pipelines, standardized interfaces, and transparent debugging tools are vital to reduce complexity and accelerate innovation.
Key Insight:
Production ready platforms should handle the complexities of data flow, model deployment, and tool integration behind the scenes, allowing end users to focus on outcomes rather than infrastructure headaches.
The Corvic AI Approach
At Corvic AI, we are rethinking AI infrastructure for the new era of GenAI. We recognize that enterprises need:
1. Data-Centric Approach
We focus on preserving the inherent value of the enterprise data, rather than forcing it into one-size-fits-all text formats.
Corvic offers no-code enterprise AI solutions that enable businesses to transform their data into actionable insights through automated feature engineering and generative AI. Corvic’s Mixture of Spaces™ (MoS) technology treats data in its native forms such as tabular, relational, text, image, and time series; leveraging inherent signals based on format and structure. Spaces are various elevated data representations used to derive insights and respond to queries. Traditional semantic embeddings are one example of such spaces. This method enhances data engineering and handling, ensuring higher data quality and better integration, thereby increasing the success rate of AI projects. This approach simplifies the data preparation process, ensuring higher data quality and better integration, thereby increasing the success rate of AI projects.
2. Multi-Tool Orchestration
Harnessing the full potential of LLMs requires guardrails and domain-specific intelligence. Corvic platform features Adaptive Chain of Action (ACoA) orchestration™, a technology that intelligently navigates and integrates various data representations (“spaces”) with the right tools using Corvic’s Agentic Function Calling (AFC™) architecture.
This includes:
— Machine learning (ML)
— Artificial intelligence (AI)
— Graph AI
— Generative AI
By applying the optimal tool for each data format and problem, Corvic ensures superior performance and reliability.
3. Production Readiness
Simplifying complexity, the Corvic platform enables teams to build and deploy GenAI applications in production seamlessly — without requiring deep ML expertise or integrating multiple software platforms.
Corvic is an Operational GenAI data platform: Unlike other data platforms that treat Generative AI as an add-on, Corvic uses it as a core building block, simplifying Generative AI adoption. Corvic is a GenReady™ Architecture which enables enterprise-grade deployment of Reason Mediated Generation™ (RMG) agents, which surpasses simple semantic search capabilities. By facilitating the precise handling of complex, heterogeneous data, Corvic simplifies the adoption of generative AI within organizations.
By focusing on these building blocks — Data, Tools, and Orchestration — Corvic AI is redefining what it means to deliver Generative AI solutions in the enterprise.
Our mission is to ensure organizations can easily adopt and scale GenAI technologies, unlocking new opportunities and driving tangible business value without being hindered by infrastructure and complexity.
Ready to Explore Next-Generation GenAI Infrastructure?
Corvic AI stands at the forefront of this transformation, helping enterprises move beyond the hype and into operational success. By preserving data fidelity, integrating the right tools, and orchestrating everything seamlessly, we enable organizations to truly harness the power of Generative AI.
If you’re looking to enhance your AI capabilities and accelerate GenAI adoption in your enterprise, reach out to learn how Corvic AI can partner with you on this journey.
