SimpleOne GenAI Platform Overview: How to Stop Running Pilots and Start Scaling AI

Generative AI keeps making deeper inroads into corporate processes, but many companies are still rolling it out the old way — with a separate point solution for every individual use case. Ilya Radchenko, Head of Platform Products at SimpleOne, explains why that approach hits a ceiling and what's replacing it.

Why Point AI Solutions Don't Work at the Enterprise Level

Generative AI has stopped being an experiment — companies are starting to embed it into real business processes. But not every project makes it from a polished pilot to stable production use: 95% of AI pilots at companies fail, meaning they never reach a measurable ROI.

To simplify things, companies today have two basic approaches to AI automation.

The first path: a point solution for every scenario.

For a specific task — helping a first-line support agent, parsing invoices, supporting sales — a company picks a separate product or builds a small internal project. In the short term, this delivers a fast payoff: one process speeds up, people are happy. But once there are dozens of these initiatives running, systemic problems start to surface:

• different stacks and teams behind every AI use case;
• duplicated integrations with the same underlying systems;
• inconsistent approaches to access, logging, SLAs, and support;
• no way to move a successful scenario from one part of the business to another without rewriting it from scratch.

The second path: build your own AI platform from scratch.

Here, the company goes for maximum control: it takes open source tools, pipeline builders, and model orchestrators, and builds everything within its own environment. On paper, this looks like the ideal option, but in practice it quickly turns into a heavy engineering product with its own roadmap, technical debt, dependency on developers, and a high barrier to entry for business units.

Both approaches run into the same limitations:

• Pressure to deliver results fast. The business needs an effect within weeks or months, not a year after a lengthy rollout or in-house build.
• Vendor dependency or mounting technical debt. A vendor product doesn't bend easily to real-world processes, while an in-house platform gradually consumes more and more resources over time.
• Enterprise-grade security. You need to control data, model actions, access, and audit trails all at once, on top of meeting personal data requirements — and this isn't something you can bolt on after the fact.
• Rigid dependency on a single LLM provider. Models, pricing, and policies change faster than an IT landscape can keep up, and any inflexible solution risks becoming outdated fast.

The third path: a unified GenAI platform.

Instead of multiplying point solutions or building your own zoo of tools, the company relies on an infrastructure layer where:

• different departments can build their own AI scenarios on shared building blocks;
• security, access, and audit are solved once, at the platform level;
• integrations and corporate memory are reused across use cases;
• there's the freedom to switch between LLM providers without breaking existing processes.

In this article, we look at how the SimpleOne GenAI platform plays exactly that role: it lets teams and business processes keep the initiative, while still keeping everything inside a single, managed environment instead of a set of disconnected AI pilots.

How It's Used in Practice

The real value of a GenAI platform shows up in how it changes teams' day-to-day work. Below are three scenarios that can be built on SimpleOne.

IT Support: From Ticket Classification to Agentic Incident Analytics

In a classic service desk, a lot of time goes into routine work: parsing a ticket, finding the right knowledge base article, routing the request to the correct queue, resetting a password. The AI assistant inside SimpleOne's ITSM layer handles this part without an agent's involvement: it identifies the type of request, pulls a relevant answer from the corporate knowledge base through RAG, and when needed, carries out technical actions on its own — for example, triggering a password reset through an Active Directory integration. For the end user, it looks like an ordinary support chat; for the IT department, it means a shorter queue and serious relief for the first line.

The next level up is an AI agent — for incident analytics, say. Its job isn't just to answer a question, but to carry out a whole chain of actions. When it receives a failure signal, the agent gathers data on its own from multiple sources: the history of similar incidents, monitoring logs, the configuration database. It then analyzes the patterns, formulates a probable root cause, and creates a task for the team with the context already filled in. The on-duty engineer sees a structured summary with recommendations instead of a stream of raw alerts, and can spend their time fixing the problem instead of gathering information.

Finance: Processing an Invoice in 2 Minutes Instead of 15

Approving incoming invoices remains one of the most resource-draining operations: receiving the document, extracting the details, matching it against the purchase order, sending it for approval, and scheduling payment. In SimpleOne, this cycle is built from three components. OCR reads the scanned or PDF invoice. Smart Filling automatically fills in the record's fields — number, date, amount, counterparty, expense category. Then an approval workflow kicks in, routed by rules: amount thresholds, expense type, the responsible approver. As a result, processing time for a single invoice drops from around 15 minutes to 2 — without any ERP customization or large-scale employee retraining.

Sales: From a Call to a Filled-Out CRM Record

In sales, one of the most painful parts of the job is manually filling out the CRM after a negotiation. A rep finishes a meeting or call, then spends 20 to 30 minutes transferring everything from memory into the system: what was discussed, what was agreed, what the next steps are. SimpleOne's AI assistant plugs directly into this process: it transcribes the call recording, extracts the key entities — contact, need, objections, outcome — and automatically fills out the deal record in the CRM. The rep just has to quickly check the result and make any needed corrections. Time is saved, and data quality in the system improves too: records become complete and up to date.

SimpleOne GenAI Platform: Architecture and Key Components

None of the scenarios described above work as a set of disconnected modules — they work as a single system. That's because GenAI functionality is built directly into the SimpleOne platform, which already brings together low-code development tools, an Enterprise Service Management (ESM) layer, and ready-made business applications for ITSM, HR, and sales.

The Technology Foundation

RAG and corporate memory. Language models know nothing out of the box about how a specific company is actually organized — its processes, knowledge bases, CMDB, or policies. The RAG approach (Retrieval-Augmented Generation) gives the model a way to learn what it needs. Before querying the model, the platform searches the corporate knowledge store for relevant fragments — knowledge base articles, instructions, request history, configuration records — and feeds them into the query as context. The result: AI responds within the bounds of the facts and rules that actually apply inside the organization. Importantly, this corporate memory lives inside SimpleOne itself, without exporting data to external SaaS services.

Model orchestration through Nexus. The second pillar is working with different LLMs without being locked into a single provider. This is handled by a component called Nexus: it provides a single endpoint for applications and agents, decides on its own which model to send a given request to, and lets you connect multiple providers in parallel — from public APIs to on-prem deployments. When prices change, security policy shifts, or a better-suited model comes out, there's no need to rewrite business processes and scenarios: only the Nexus configuration changes, not the workflow logic. Model orchestration is handled by our technology partner, Ainergy.

Universal Tools

The SimpleOne GenAI platform provides two types of AI entities that can be used separately or together: ready-made AI tools and custom AI agents.

Ready-made AI tools and services are basic "building blocks" for specific tasks that can be embedded directly into processes. Out of the box, there are tools for typical data and text operations: OCR for document recognition, Smart Filling for auto-filling forms, classification, summarization, text generation, and semantic search. These tools are configured once for a class of tasks and then reused across different scenarios, with no need to write code or launch a separate integration project.

The workflow builder and AI actions. All AI components — including both tools and agents — are available inside the visual workflow builder. There, AI actions work as individual steps: you can set input data, launch conditions, branching, and follow-up actions. A typical example: "an invoice arrives by email → OCR → Smart Filling → rule-based approval → scheduled for payment." A scenario like this is assembled from ready-made blocks in a few hours and doesn't require ongoing developer involvement.

The AI agent builder. An AI agent in SimpleOne isn't just a scripted chatbot — it's a component that can independently choose its sequence of actions based on context. Unlike a rigidly defined workflow, an agent acts as an adaptive participant inside a process or user scenario. An agent consists of:

• the agent itself — the LLM-based layer that decides what to do next;
• an adapter — responsible for connecting to a specific model through Nexus;
• methods — atomic operations on platform entities (for example, creating a request, updating a record, reading a record);
• tools — external integrations, including via the Model Context Protocol (MCP), for when the agent needs to reach beyond SimpleOne.

An agent like this can live almost anywhere on the platform: on the self-service portal, inside an approval process, in an integration scenario with an external system — anywhere a simple set of if-then rules is no longer enough and you need a process participant that can adapt to context.

A conversational interface for building agents. An agent that has methods for working with platform entities can create the configuration for a new agent right from a conversation: the user describes the task, and the assistant assembles an agent for it. This follows naturally from the platform's architecture: an agent works with platform entities through methods — and the agent entity itself is no exception.

Security and Compliance

When GenAI comes up at large companies, the first serious question usually isn't about the quality of its answers — it's about governance: who exactly can do what with AI, how is that recorded, and what can you show auditors and regulators afterward. In SimpleOne, these things are built into the platform's architecture, not bolted on afterward as separate plugins.

Access control and audit. Every AI action — a query to a model, a tool call, an agent's step in a process — runs within the platform's role-based access control (RBAC) model. An agent is physically unable to read data or run an operation it isn't permitted to. At the same time, the platform writes a detailed log: the prompt, the model's response, token usage, user ID, timestamp, and execution context. This helps with both incident investigation and tracking the cost of AI scenarios by team and process.

Deployment within the perimeter. For organizations with sensitive data — employee personal data, financial reporting, healthcare, the public sector — it's critical that data doesn't leak into third-party vendor clouds. In SimpleOne, GenAI components can be deployed fully on-premise: the RAG chain, model operations, and integrations all run inside the customer's own infrastructure. This removes a major barrier for organizations that, for security or legal reasons, can't use public GenAI services.

Operating within the regulatory landscape. GenAI is now squarely in the crosshairs of multiple sets of requirements at once — from the EU AI Act and the NIST AI RMF to ISO/IEC 42001 and national data protection legislation. The platform was designed from the ground up with these frameworks in mind: it includes logging, access management, and lifecycle and version control for models. As a result, a company doesn't just "formally comply" — it can clearly demonstrate during an audit which decisions were made with AI involvement, based on what data, and under what rules.

Conclusion

Point AI solutions really can deliver value quickly within a single process, but they don't add up to a coherent whole: every new scenario means dragging in a new tool, building a separate integration, and inventing your own security perimeter. A platform-based approach changes that logic: instead of a scattering of pilots, you get a single environment where AI automation is built, launched, and scaled by the same set of rules.

The SimpleOne GenAI platform helps companies move from one successful case to systematic AI work across the entire organization. Tools, agents, and processes live in one space, follow a shared role-based model, and leave a complete audit trail. In this form, AI stops being an experiment and becomes a normal, managed business function.