Why Having Your Own AI in Your Company Isn't a Luxury, but a Matter of Data Control

What Has Changed in the AI Market Over the Past Six Months

Just a year ago, it was believed that truly powerful artificial intelligence was only available through major cloud services (OpenAI, Anthropic, Google). Their models delivered the best results, but they were expensive- and, more importantly, all your data and queries ended up on third-party servers.

Today, the situation has changed. Open-source models (such as those based on the Llama and Nemotron) deliver results that are nearly on par with top-tier proprietary solutions when properly “tuned” - that is, when adapted for a specific task using specialized frameworks.

Here’s some recent data from a joint study by NVIDIA and LangChain (July 2026). On one of the popular agent-based benchmarks, the open-source model achieves 86% efficiency, while the best closed-source model achieves 87%. The difference is just one percent. However, the cost of a single “run” (processing a typical request with a sequence of actions) is $4.48, compared to $43.48 for its proprietary counterpart—that is, ten times cheaper.

From this, we can conclude that artificial intelligence is no longer a scarce resource. Now, almost any business problem can be solved using a sufficiently intelligent model without overpaying for a brand name. The main question has shifted to a different plane: where does this model operate, who owns its settings and data, and who controls its actions.

A company consists not just of processes, but of “agents”

Jensen Huang, CEO of NVIDIA, put it this way in a conversation with the creators of LangChain: “A company is the sum of its mission-critical proprietary workflows.” Each of these processes, from procurement planning to production optimization, is a separate AI agent. An agent is not just a chatbot, but an autonomous program that can use tools, access databases, make decisions within the scope of its task, and interact with other agents.

The company of tomorrow will be built not on static regulations, but on such agents “encoded” into special frameworks. These frameworks combine a model, available tools, a record of past actions, and safety rules. As a result, routine yet complex tasks such as recalculating logistics routes when a supplier fails or rescheduling production line workloads are performed significantly faster and more accurately than by humans.

It is important to understand that this is not about replacing people but about ensuring that every expert has a “digital assistant” that takes care of routine tasks, evaluates options, and monitors processes, while strategic decisions remain in human hands.

The Right Strategy: “Frontier” First, Then Specialization

Huang offers pragmatic advice for companies just getting started with agent-based AI:

1. Start with the best cloud-based models (he calls them “frontier” models), such as Claude Code, Codex, or GPT-4-class models. They’ll give you an understanding of the “ceiling”: what can actually be automated, what the accuracy is, and how much data is required. Yes, it’s more expensive, but it’s an investment in exploration.
2. Use them until an open-source model with public weights reaches the quality level required for your specific task. According to Huang, this threshold was crossed about six months ago, and now open models are mature enough to handle most enterprise scenarios.
3. Move on to specialization - fine-tune the open-source model with your own data, customize its setup, and integrate it with internal systems. This delivers a tenfold cost savings on every query and, more importantly, gives you full control over the process.

Here’s a business analogy: you hire in-house experts with unique knowledge, but you also bring in external consultants, rent equipment, and purchase licenses for standard software. It’s the same with AI: general skills (programming, writing, analysis) can be “rented” from cloud models, and on top of them, you can build your own unique capabilities—which are what make you competitive.

The subscription trap: you’re handing over your intellectual capital along with your processes

When you start using a subscription-based cloud AI service, you’re not just sending it requests - you’re also handing over:

• your internal data (financial reports, projects, customer information);
• your “recipes” for solving problems how you analyze the market, how you make decisions, and what steps you take;
• your unique know-how, which accumulates over time.

As soon as this information enters the agent’s environment (tools, memory, context), it becomes part of the system’s “intelligence.” And if this agent runs on someone else’s runtime, under someone else’s SLA, then you lose control over your intellectual property. Huang emphasizes: “Intellectual property is built on intelligence”—your value is determined by your intelligence, and outsourcing it makes just as little sense as handing over your development department’s unique expertise to an outside company.

This leads to three typical pain points faced by major players:

• Compliance and regulatory issues. For the financial sector, law firms, government agencies, and state-owned corporations, transferring data outside a controlled environment is often prohibited by law. A cloud subscription, by definition, breaches this perimeter.
• Economies of scale. Every request to a cloud API costs money, and this cost doesn’t decrease on weekends or at night. The more you implement AI, the faster the bill grows. And you’ll want to use affordable AI even more often, for example, to run through dozens of “what-if” scenarios to find the optimal solution. With a cloud-based model, this is financially ruinous.
• Loss of control over your own “crown jewels.” Your super-agents, which solve key tasks, eventually become your most valuable asset. If they run on someone else’s platform, you’re dependent on its stability, pricing policy, and even on whether the provider uses your data to improve its own models.

What does it take to build your own agent ecosystem?

Launching such an agent within a company is no small feat. Huang lists the essential components:

• Language model (LLM) - the foundation of the AI; you can use an open-source model and fine-tune it.
• A suite of tools - access to APIs, databases, and internal systems (e.g., SAP, 1C, CRM).
• Knowledge graph - a structured representation of the domain and the relationships between entities.
• Memory system - the ability to remember previous actions, results, and the context of the dialogue.
• Constraints - rules that prevent the agent from exceeding permissible limits (e.g., prohibiting certain operations or requests).
• A mechanism for fine-tuning and post-training to adapt the model to your company’s specific needs.
• The framework itself - the software infrastructure that ties everything together.
• Runtime (execution environment) - servers where agents operate, with isolation and access control.

And finally, something that’s often overlooked: security and access management. An agent must act like an employee: it is assigned a role, and its access rights to files, networks, applications, and other agents are defined. Huang calls this an “HR system for AI.” You can’t just launch an agent; you need to “onboard” it, define its area of responsibility, and assign a human “manager” who is accountable for its actions.

Without this, deploying agents is just as risky as hiring an employee without a background check or established policies - you risk data leaks, poor decisions, and even attacks carried out through the agent.

The industry is already moving in this direction: the example of NemoClaw

In July 2026, NVIDIA and LangChain released a joint blueprint - a ready-made architecture called NemoClaw. It combines:

• the Nemotron 3 Ultra model (open-source, high-performance);
• the LangChain Deep Agents agent layer (tools, memory, reasoning chains);
• the secure OpenShell runtime (a sandbox with security policies).

All of this is packaged into a single solution that can be deployed within the customer’s perimeter. According to the authors, “there are no more excuses” - all the technical components are already in place, and any large business can build its own sovereign AI agent.

How We Implement This at ITPOD: AI HW/SW Complex with Ainergy

Our offering - the AI HW/SW Complex (hardware and software complex) from ITPOD, integrated with the Ainergy platform - accurately reflects the same logic but is adapted to the requirements of the Russian market: sovereignty, legislation, and operation in isolated networks.

We provide your company with a ready-to-use “AI environment” in your own data center. Here’s how this aligns with the components Huang mentioned:

It’s important to note that Ainergy’s control nodes (Control Plane, Runner, VectorDB, OCR API, etc.) run on standard general-purpose servers without GPUs. GPU accelerators are required only for inference (running models). This is crucial for budget planning, since you pay for “hardware” only based on actual workload, not for fancy architectural diagrams.

Why This Is Cost-Effective and Secure

• Cost savings - you pay for your own hardware once, rather than for every request in the cloud. At the same time, you can use inexpensive open-source models without worrying about your bill going up.
• Security - all data remains under your control and complies with regulatory requirements.
• Flexibility - you’re not tied to a single cloud provider; you can switch models, fine-tune them, and experiment.
• Independence - your agent ecosystem continues to operate even during internet outages or changes in cloud service conditions.

We see the main value not simply in automating yesterday’s processes, but in crystallizing your company’s unique knowledge and algorithms into autonomous agents that reside within your infrastructure. Over time, these agents form your company’s “operating system”- one that belongs solely to you and sets you apart from your competitors.

What to Do Right Now

Choose one process that currently requires the most manual labor and effort from your specialists. For example:

• preparing commercial proposals tailored to individual terms;
• verifying contracts for compliance with internal policies;
• optimizing delivery routes;
• analyzing large sets of financial data.

We’ll show you how your agent will tackle this task and calculate the exact ITPOD server configuration (number of GPUs, memory capacity, and storage) required to handle it at your specific scale.

If you work with clients in regulated industries (finance, legal services, the public sector, manufacturing) and encounter requests for a sovereign AI solution without the cloud, we’re ready to work with you to prepare a comparison of two scenarios for your client: “operating via public APIs” versus “a proprietary closed-loop system on a dedicated AI server.” We’ll provide a customized calculation for your specific use case.