From AI Experiments to Enterprise Platforms

How leading companies are turning scattered GenAI pilots into secure, reusable systems of intelligence.

Exadel works with more than a hundred customers, mostly large enterprises and advanced technology companies, all building their own software. This gives us a rare vantage point to follow and observe the AI journeys of some of the world’s leading businesses and identify technology trends before they become mainstream.

Some of our most mature clients began experimenting with GenAI in early 2023, running pilots to explore custom-built AI applications. By mid-2024, many had anywhere from a few to a dozen standalone AI apps in production. Around that time, they started to realize they wouldn’t get away with just a couple of implementations. GenAI is here to stay — a new technology primitive that enables valuable functionality previously out of reach. Recognizing that many more implementations were on the horizon, they began moving toward centralizing their efforts to address:

• Consistency in architectural approaches — avoiding situations where similar problems are solved using different approaches across divisions of the same company.
• Cost savings — building reusable components once and integrating them across multiple applications.
• Control over AI spend — as AI applications can be resource-intensive, centralized tracking helps keep token consumption and related costs under control.
• Security and compliance — with LLMs introducing a new and still-evolving attack surface, centralized governance is increasingly essential.

First-Movers

Where data depth meets engineering discipline.

It’s no surprise that the first companies to start building enterprise AI platforms shared two defining characteristics:

• Extensive proprietary data — organizations sitting on vast amounts of unstructured data that AI has finally enabled them to capitalize on.
• Strong engineering maturity — companies already accustomed to developing custom cloud and mobile applications through centralized platforms built on reusable components, shared services, and governance mechanisms.

They simply applied the same architectural logic to AI: centralize the foundational work, so different business lines and teams can safely and efficiently innovate on top of a solid base.

Typical Platform Architecture Components

The blueprint behind scalable enterprise AI

After supporting several large enterprises through their AI transformation journeys, Exadel began to notice consistent patterns in how mature organizations approach platformization.
Although industries and business priorities vary, their architectural blueprints tend to converge around a common set of core layers — the structural backbone of an Enterprise AI Platform.

Data and Knowledge Layer

Giving AI a memory….and context that matters

The Data and Knowledge Layer serves as the platform’s memory system — turning enterprise content into machine-understandable context for retrieval and reasoning.

It begins with chunking and vectorization, where documents, emails, and tickets are split into meaningful segments and converted into embeddings — dense representations of semantic meaning. These embeddings are stored in vector databases such as Pinecone, Weaviate, or pgvector, enabling semantic search and retrieval-augmented generation (RAG).

When a query arrives, the platform retrieves and re-ranks the most relevant chunks based on similarity, metadata, and recency. These passages are assembled into a context window that grounds the model’s reasoning in enterprise knowledge rather than model memory.

Advanced implementations apply hybrid retrieval, context compression, and feedback-driven reindexing to continually refine performance. Over time, this layer evolves into a living, self-optimizing knowledge system — ensuring each AI interaction is context-rich, relevant, and trustworthy.

Model Layer

Mixing foundation and fine-tuned models for control and reach.

The Model Layer powers reasoning and generation. Most enterprises now operate within a hybrid model ecosystem: using external foundation models such as GPT, Claude, or Gemini for general capabilities, and internal fine-tuned models trained on proprietary data for sensitive or domain-specific use cases.

A model-routing service dynamically determines which model to use based on task type, sensitivity, latency, and cost. Sensitive requests can run entirely within the enterprise’s secure VPC, while lower-risk workloads may rely on external APIs. To improve cost efficiency and auditability, responses are often cached and versioned, allowing deterministic replay for testing and compliance.

Together, these mechanisms bring control, reliability, and predictability to what is otherwise a probabilistic technology surface.

LLM Security, Compliance, and Governance

Making every model decision visible, auditable, and safe.

Security and governance are built directly into the platform’s control layer, surrounding model execution. The goal is to make every model interaction observable, auditable, and policy-compliant without altering the inference process itself.

At the center of this layer sits an LLM governance gateway, acting as an intelligent proxy between business applications and model endpoints. It performs pre- and post-processing to enforce corporate and regulatory policies:

• Incoming prompts are scanned for prompt injections, jailbreak attempts, adversarial tokens, or sensitive data exposure.
• Outgoing responses are checked for factual accuracy, hallucinations, toxicity, and bias, ensuring generated content remains safe and compliant.

Exadel’s in-house implementation of this component — the Anchored AI Framework — extends these capabilities across two main domains:

• Security checks — detecting jailbreaks, ASCII-art or token-level injection attempts, and preventing confidential or training data leakage.
• Corporate rule enforcement — integrating fact-checking, hallucination and bias detection, toxicity filtering, and programmable conversation rails connected to internal policy knowledge bases.

All model traffic passing through this gateway is logged and versioned for full auditability, supporting frameworks such as ISO 27001, SOC 2, and GDPR. Anchored AI transforms compliance from a downstream review process into an active enforcement layer — one that protects enterprises from both technical and reputational AI risk.

Monitoring and FinOps

Because you can’t manage what you can’t measure.

As AI becomes part of daily operations, visibility into its performance and cost is essential. The Monitoring and FinOps Layer provides both technical and financial observability across all model interactions.

Technical monitoring tracks latency, throughput, and token consumption. The FinOps component builds on this by correlating usage data with business ownership, enabling cost allocation, anomaly detection, and budgeting at the application or department level.

These insights help organizations optimize workloads, prevent runaway expenses, and determine when to replace external APIs with self-hosted or open-weight models for greater efficiency and control.

Agents and Orchestration Layer

Turning isolated model calls into systems of reasoning.

The Agents and Orchestration Layer defines how intelligence is modularized and reused across the platform. It combines a library of pre-built agents with orchestration patterns that coordinate how those agents collaborate to perform complex reasoning or automation tasks.

The agent repository includes both domain-specific and general-purpose components. Some are tailored to particular applications; others are reusable across contexts — for example:

• a Text-to-SQL agent that translates natural language into database queries,
• an Intent Reader agent that classifies user requests,
• a Data Summarizer agent that condenses retrieved information.

Each agent encapsulates its role, access permissions, and model dependencies, making it portable and simple to integrate into new solutions. The repository acts as a shared capability pool that accelerates delivery and ensures consistent behavior across applications.

On top of this foundation, the orchestration framework standardizes how agents interact. Common patterns include sequential pipelines (one agent’s output becomes another’s input), group-chat orchestration (agents collaborate as peers), hierarchical structures (supervisors delegate to specialized agents), and dynamic handoffs (control shifts based on context or confidence).

Together, these mechanisms transform AI from isolated model calls into coordinated, reusable systems of reasoning — making enterprise platforms more scalable, adaptable, and governable.

Prompt Management and Evaluation (Evals)

Bringing CI/CD discipline to Generative AI.

Prompt Management and Evaluation create the quality and governance loop within the enterprise AI platform. Prompts are treated as versioned, governed artifacts rather than static text strings.

Each prompt moves through defined lifecycle stages — from experimentation to production — with automated checks and human approvals ensuring consistency and compliance.

The evaluation layer continuously measures model behavior against quantitative metrics (accuracy, relevance, latency, cost) and qualitative ones (tone, safety, policy adherence). Both automated and human-in-the-loop reviews identify regressions, hallucinations, or drift before they reach production.

By operationalizing prompt management and Evals, enterprises establish a repeatable feedback cycle that keeps AI outputs reliable, auditable, and aligned with organizational standards — effectively bringing CI/CD discipline into the lifecycle of generative AI.

Interaction and Interface Layer

Where human insight meets AI intelligence.

While lower layers manage reasoning, retrieval, and orchestration, many enterprise AI applications share common interaction patterns — chat interfaces, document review tools, annotation dashboards, and human-in-the-loop workflows.

The Interaction and Interface Layer provides reusable UI components and integration templates that standardize how users interact with AI capabilities. Typical elements include:

• Chat and Copilot interfaces — configurable front-ends with conversational context, session memory, and role-based access.
• Document interaction modules — for reviewing, commenting, or approving AI-generated content such as policy drafts or summaries.
• Human-in-the-Loop workbenches — where users validate outputs, provide corrections, or trigger feedback loops feeding into the evaluation layer.

Packaged as reusable front-end modules or API-driven widgets, these components ensure consistent user experiences across AI tools, accelerate delivery, and keep human oversight central to the enterprise AI workflow.

Developer Experience Layer

Putting power in developers’ hands — safely.

The Developer Experience Layer makes the platform practical for product teams by simplifying access to models, data, and governance. Its goal is to provide ready-to-use environments and bootstrap tools that allow developers to build AI features quickly and safely.

This layer typically includes:

• Self-service environment provisioning with preconfigured access to necessary resources.
• Bootstrap templates for various project types.
• SDKs that standardize integration patterns and workflows.

In essence, the Developer Experience Layer plays the same role for AI that DevOps once did for cloud engineering — turning complex infrastructure into a frictionless, governed workspace.

Accelerated AI Platform Build

The fast track to a governed, scalable AI foundation.

Having supported multiple enterprises on their journey to build internal AI platforms, Exadel has developed a clear view of which components demand the most effort and carry the highest implementation risk. Across projects, certain capabilities — such as secure model gateways, monitoring and FinOps tooling, agent frameworks, and retrieval pipelines — consistently prove to be the most complex to build and integrate.

To help clients move faster, Exadel has invested in developing these foundational components in-house. Over time, this investment has evolved into a library of pre-built modules and reference architectures that can be reused and adapted to different enterprise environments. These assets significantly shorten the time required to establish a compliant, scalable AI foundation while maintaining flexibility for customization and extension.

Our repository includes production-ready implementations of key layers such as:

• Anchored AI for LLM security and governance
• Agent orchestration frameworks
• Retrieval and vectorization pipelines
• FinOps and observability dashboards
• Developer environment bootstrap kits

Each component follows enterprise-grade standards for security, modularity, and auditability. Clients can adopt them individually or as part of a complete platform build-out — accelerating delivery while preserving flexibility and compliance.

Refer to the diagram below for an overview of these pre-built assets in our internal repository — the same building blocks we use to accelerate the delivery of Enterprise AI Platforms for our clients.