Document intake and summary
Extracts required fields, classifies incoming documents and prepares a source-linked summary for review.
- document classification
- field extraction
- source-linked summaries
- review queue
Working with companies in Kazakhstan and remotely.
AI agents for company knowledge and documents
We build agents that search approved corporate knowledge, process documents, review requests and support controlled actions with roles, logs and human confirmation.
Knowledge
RAG with source references
Documents
summaries and extraction
Checks
requests and rules
Control
roles, approval and audit
Four practical roles for an AI agent.
Start with one role and one source set. Expand only after the answers and actions are validated against real tasks.
Corporate knowledge agent
Uses RAG to answer from approved policies, instructions, contracts and project documents.
- answers with source context
- role-based document access
- policy and instruction search
- document summaries
Request and document reviewer
Checks fields, rules and budgets before a request moves to the next approval stage.
- completeness checks
- budget and limit checks
- draft comments
- risk flags for a reviewer
Controlled AI agent
Performs agreed operations between systems only within its permissions and confirmation rules.
- approved API actions
- human confirmation
- action and response log
- rollback and error handling
When the same questions keep coming back.
An agent is useful when it has a narrow role, approved sources, clear rights and a measurable work scenario.
Knowledge is scattered
Policies, instructions, contracts and project history live in different folders and systems.
The same policy questions repeat
Employees repeatedly ask where a rule is written, which version applies and what source supports the answer.
Documents are checked manually
Requests, documents and budgets are repeatedly checked against the same rules.
Actions require control
The agent may suggest or perform only approved operations, with role checks, human confirmation and an audit trail.
Define the agent’s role and limits.
The scope starts from one role, approved sources, a measurable output and explicit action boundaries.
First stage
One user role and one scenario: knowledge search, document intake, request review or a controlled action.
Client inputs
Source documents, role and access matrix, current procedure, sample requests, acceptance examples and forbidden operations.
Estimate factors
Source volume and formats, permission complexity, system APIs, action risk, confirmation rules, audit depth and deployment environment.
Deliverables
Working agent interface, source and role configuration, instructions, approved tools, confirmation flow, evaluation set and audit rules.
Good fit
The task repeats, approved sources exist, user rights are known and a human can review answers or confirm critical actions.
Not a fit
For KPI anomalies, driver analysis and forecasts without document workflows or system actions, use AI analytics.
Start with one role. Test it at work.
One user group, one source set and one expected result make quality and business value testable.
Use case
Select one role, expected result and actions that must remain outside the agent.
Data
Check sources, permissions, data quality, document structure and integration constraints.
Prototype
Build the initial prompt and instruction set, RAG context, interface and test set.
Integration
Connect approved data, documents and API tools with confirmation rules.
Testing
Validate answers, citations, permissions, unsafe requests, actions and recovery paths.
Launch
Deliver access and documentation, enable logs and monitoring, and agree on the next scenario.
Agents or analytics?
An AI agent works with knowledge, documents and actions. AI analytics explains KPI changes.
Choose AI analytics for anomalies, drivers and forecasts. Use process automation when deterministic workflow rules are enough. Add data integration when 1C, Bitrix24, SAP, iiko, Directum and other systems need a reliable exchange layer.
Questions about AI agents.
The agent is limited by approved sources, roles and process rules.
What tasks can an AI agent perform?
Search corporate knowledge, prepare document summaries, check requests and budgets, and support approved actions with human confirmation.
How is it different from a regular chatbot?
An agent works with approved company sources and roles, follows a specific process and records its requests, answers and actions.
How are agent actions controlled?
Rights are limited by role and source. Critical operations require human confirmation, and every request, answer and action is logged.
Which systems and documents can be connected?
1C, Bitrix24, SAP, iiko, Directum, BI, databases, documents and other systems can be connected through available APIs and standard mechanisms.
Start with
your task.
20–30 minutesFree
- Review your process
- Define the first stage and next step
What to bring to the call
State who will use it, which sources are allowed, what result is expected and which actions must always require human confirmation.