Implementation of AI in business: corporate AI in the company's contours
RESTART introduces artificial intelligence into the business processes of large companies: from process surveys and pilots to production operations of AI assistants, document search and AI agents. The solutions work within the customer's loop - with roles, access rights and logs, and production data is not transferred to external AI services without the written permission of the customer.
Enterprise AI for Business
RESTART Group - implementation of corporate AI for large businesses: process survey, pilot, implementation and maintenance of AI services in a secure environment.
What differentiates enterprise AI from a public chatbot is not the size of the model, but the fact that each answer has a traceable basis: who asked, what data was entitled to, and where the fact came from. Therefore, the implementation of AI does not begin with the choice of model, but with the process, data, access rights and information security requirements.
All AI services of the company can be collected on one corporate AI platform: models, agents, knowledge base, access rights and log - in the customer’s environment.
How AI is being implemented in business
Process Survey
AI-discovery in 10 working days: a register of AI scenarios, checking the readiness of data and information security restrictions, the concept of one pilot and the decision whether to launch it or not.
Pilot
RAG pilot on a corporate basis on real documents, users and questions: “Light” edition – 4 weeks, “Full” – 6 weeks. The term is fixed before the contract.
Implementation
production environment on Restart AI Enterprise Platform: document search, AI department assistants, AI agents for business processes, integration with 1C, SAP, CRM and EDMS.
AI Security and the Data environment
Enterprise AI environment: where the data and models live, who gets access to what, what is written to the logs. For already running AI services - Secure AI audit.
Operation
production operations and development plan by modules, computing environment and GPU power, support and development of the solution.
AI solutions for business processes
Ready-made corporate AI scenarios that are implemented one process at a time and developed in modules on a common core:
For industry, oil and gas, energy, mining, metrology, MRO, HSE and production - industry AI packages with ready-made scripts, data templates and agents.
When do you need enterprise AI?
When employees spend time searching for information, preparing reports, analyzing documents, processing applications, tenders, contracts or internal regulations. Enterprise AI is useful where there is repeatable intelligence and an accumulated knowledge base.
What we design
RAG document search, AI assistants for departments, agent scenarios, integration with 1C/SAP/CRM/EDS, internal knowledge base, AI assistants for finance, contracts, tenders, service desk, development and information security.
Product map of AI modules
RESTART AI Enterprise Platform can be developed as a set of application modules on top of a common core: Knowledge AI, Service Desk AI, CFO AI, Contract / Procurement AI, Tender & Sales AI, Dev AI, Security / GRC AI, HR AI and industry-specific Industry AI Packs.
This approach eliminates the need to create every AI scenario from scratch. Users, roles, data sources, prompts, models, logs, auditing, integrations and security rules remain common, and each new module is connected to an already managed loop.
AI infrastructure and GPU power
The AI pilot quickly comes up against not only prompts and the interface, but also the computational environment: where documents are located, how vector search works, who has access to logs, which GPUs are needed for embeddings, reranking or local models, how dev/test/prod are divided and who is responsible for operations.
Therefore, the RESTART AI direction is complemented by the service AI infrastructure and computing power. We can deliver the platform, implementation and computing resources in a single loop: from pilot to production.
AI for Good: Spina Bifida
The RESTART AI practice is used not only in corporate settings. Socially significant projects on the topic of Spina Bifida show how computer vision, RAG, secure data processing and a human-in-the-loop approach can help doctors, families, foundations and expert communities.
In such projects, the correct role of AI is an additional tool under the supervision of a specialist: it helps to pay attention to possible signs, structure information and quickly find proven materials, but does not replace a medical decision.
Public AI products on the platform
RESTART AI Enterprise Platform is used not only as a corporate platform for enterprise modules. On its basis, RESTART develops application products where AI is combined with data, integrations, security and the human scenario.
1trAIner presents the SportTech ecosystem: eight data sources, a cascade of plans, a trainer-agent with memory, Telegram, MAX, a web dashboard, and an iOS app. The Spina Bifida projects showcase the social AI/MedTech ecosystem: computer vision, RAG, human-in-the-loop, and careful handling of sensitive topics.
FinTech case: AI platform and RAG agents for a bank
In a banking project under NDA, RESTART implemented an AI platform and RAG agents for a bank from the top 5 of Uzbekistan. The solution successfully passed the pilot, received a positive assessment from the customer and is now under maintenance and development.
For the AI direction, this is an important public signal: RESTART can launch not only demo chat bots, but also secure enterprise landscapes with knowledge bases, SharePoint, mobile and web channels, access roles, auditing and quality metrics.
CleverHub and applied AI assistants
For tasks where the client needs not only a platform outline, but also ready-made application products, the group uses the CleverHub line: VoiceHelp for voice requests, Meeting Hub for meeting minutes, Document AI for document processing and Ragify for RAG search for corporate knowledge.
Architectural approach
We start with data sources, access rights and business process. Then we design the indexing layer, model layer, workflow, interfaces and audit loop. This approach reduces the risk of leaks, chaos in prompts, and opaque AI solutions.
Data for AI is prepared in the loop corporate data warehouse: sources, quality, showcases and access rights.
Security and control
The AI environment must take into account roles, access matrices, logging, versioning of prompts, human verification, a ban on sending sensitive data to external services without an agreed upon architecture, and the possibility of on-prem/private cloud hosting.
If AI not only responds, but also participates in the business process, a separate layer is needed secure AI automation: rights, logs, human-in-the-loop, integration and action control.
Before accepting an AI system, a key question is how it behaves under load: whether it supports the expected number of concurrent users and whether response quality degrades during traffic spikes. To address this, the team has a Polygon—a dedicated testbed for acceptance and load testing of AI systems developed by LLC "Invent." In a single run, it evaluates both dimensions and produces a test report with a PASS/FAIL verdict for the acceptance certificate, while quality assessment is performed by a local model within the customer's environment. How the test bench is set up · polig-on.ru.
How to protect models, RAG, and data — on the page AI data security; how AI helps the cybersecurity team itself — AI in information security.
Business result
Information is found faster, manual preparation of documents and responses is reduced, dependence on individual experts is reduced, and transparent automation scenarios appear that can be developed in modules.
First step
The optimal start is AI-discovery or a pilot on one process: knowledge base, contracts, tenders, service desk, finance or internal executive assistant.
Deliverables
- map of the current environment, systems, data and process owners;
- description of the target architecture and integrations;
- priorities, risks and a realistic roadmap;
- team composition, roles, management format and acceptance criteria;
- plan for production operations and development after launch.
Frequently asked questions
How to implement AI in business processes?
One process at a time: process and data survey, pilot on real documents and users with quality criteria, then production loop with integrations, roles, logs and operational plan.
Where to start implementing AI in a company?
With AI-discovery: within 10 working days, a register of AI scenarios is compiled, data readiness and information security limitations are checked, and a concept for one pilot is prepared. The result is a decision whether to run the script or not.
How long does a pilot last?
The RAG pilot for the corporate database comes in two editions: “Light” - 4 weeks, up to 300 documents; “Full” – 6 weeks, up to 2000 documents. The edition and term are fixed before the contract.
Is it possible to implement AI without transferring data to the cloud?
Yes. The architecture can be on-prem, private cloud or hybrid, with source limitation, logging and access control. Production data is not transferred to external AI services without the written permission of the customer.
How is enterprise AI different from ChatGPT?
Corporate AI works within the company: each answer has a traceable basis - who asked, what data had the right to and where the fact was taken from, and access rights, logs and integrations with 1C, SAP, CRM and EDMS are managed by the company. When there is no internal loop, employees carry documents to public services - this is the main risk of leakage.
What AI agents and AI assistants can be implemented?
Search through corporate documents, assistants for service desk, finance, contracts and procurement, tenders and sales, HR and development, AI for information security and compliance, as well as AI agents that perform actions in systems according to permitted scenarios.
Is it possible to start without a big project?
Yes. For most areas, a quick survey, architectural session, or pilot is a reasonable first step.
Let's discuss your environment
Describe the task, current systems, constraints, and expected results. We will offer a practical first step: diagnostics, pilot, audit, roadmap or project team.
