Product positioning
Knowledge AI turns disparate documents into a manageable layer of enterprise knowledge. An employee asks a question in natural language, and the system searches for relevant fragments, generates an answer, shows sources and does not have to come up with something that is not in the knowledge base.
Unlike conventional search, the product takes into account the context of the question, the structure of documents, user roles and security requirements. Unlike a simple chatbot, it is designed as part of a enterprise landscape: with indexes, sources, logs, access rules and a clear process for updating knowledge.
Benefits for the client
Find answers faster
Employees spend less time searching for regulations, contracts, project documentation and internal instructions.
Maintain expertise
Knowledge does not disappear along with people and correspondence: it is indexed, updated and made available by role.
Reduce errors
Answers are based on approved sources, and not on the employee’s memory or outdated files.
Accelerate adaptation
New employees quickly understand processes, products, requirements and project history.
What's included in the product
Connecting sources
Documents, regulations, contracts, project documentation, knowledge bases, file storages, portals, Confluence and other sources.
RAG and vector search
Text extraction, chunking, embeddings, indexing, re-indexing and finding relevant reasons for the answer.
AI response with quotation
Answers in natural language with indication of sources, limitations on the data found and an understandable refusal if there is insufficient information.
Roles and auditing
Access rights, request logs, document processing statuses, quality settings and usage control.
Scenarios where the effect is visible quickly
| Scenario | Benefit |
|---|---|
| Base of regulations and policies | Employees find up-to-date rules faster and are less likely to distract experts with standard questions. |
| Project documentation | Teams quickly recover context, decisions, requirements, and constraints on complex projects. |
| Contracts and procurement materials | Lawyers, procurement and project offices find conditions, deadlines, obligations and risks faster. |
| Training and onboarding | Newcomers get a single window of questions on processes, systems and internal materials. |
How we implement it
Discovery
We fix the business process, data sources, user roles, information security restrictions, benefit criteria and the first scenario.
MVP
We launch the working module in a limited loop, connect data, roles, logs, interface and quality control.
Pilot
We test the effect on real users, set up rules, finalize integrations and prepare for operation.
Scale
We expand the module to new units, sources, roles and scenarios without breaking the platform core.
Enterprise control
Data under control
Sources, permissions, logs, and restrictions are fixed at the architecture level rather than added after launch.
Integrations
Modules connect to ERP, 1C, SAP, DWH, EDMS, Service Desk, GitLab, Jira, Confluence, portals and internal APIs.
Verifiability
Answers and actions should be explainable: sources, versions, logs, processing statuses and the responsible owner of the process.
Scaling
Each of the following scenarios uses the platform core: roles, models, RAGs, logs, connectors, and security rules.
Infrastructure for RAG
A RAG system becomes useful only when documents are consistently loaded, indexed, re-indexed, and accessed by role. AI Compute provides vector storage, fast disks, workers, embeddings, backup and access control for this.
Frequently asked questions
Is it possible to start with the pilot?
Yes. Typically, a pilot is run on a single set of documents and a limited group of users to test the quality of sources, responses, and access rights.
Can the system operate without outputting data to the outside?
Yes. The architecture is selected according to the customer’s requirements: on-prem, private cloud or a hybrid version with control of sources and logs.
What if the answer is not in the documents?
The correct logic of the product is to report that there are no sufficient reasons and record a request to improve the knowledge base.
Enterprise product packaging
Enterprise RAG / Knowledge AI is delivered as a RESTART AI Enterprise Platform module: with a clear area of responsibility, business process owner, data model, integrations, roles, logs, pilot criteria and production plan. This is important for CIOs and CISOs: the module does not live separately from the corporate architecture, but is integrated into the IT landscape, security, operations and change management.
Business effect
Success criteria are formulated before the pilot: time, quality, reduction of manual workload, speed of response, completeness of data or controllability of the process.
Integrations
The module connects to customer systems: ERP, 1C, SAP, BI, DWH, EDMS, Service Desk, Git, portal, mail, documents and internal APIs.
Information security and compliance
The separation of roles, sources, loops, logs, and data is considered at the architectural level rather than added after launch.
Scaling
After the first successful scenario, the module can be expanded to new departments, documents, processes, users and regions.
How to show value per pilot
Select one process
Don’t try to automate everything at once: choose a process with an understandable pain, an owner and a measurable result.
Connect data
Collect a limited but real set of documents, applications, reports, code, regulations or historical requests.
Check with users
Conduct a pilot on working scenarios, collect feedback, adjust the quality of answers and control of controversial cases.
Design an production environment
Fix the architecture, roles, regulations, SLA, monitoring, support and development roadmap.
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.
