AI IN BUSINESS PROCESSES
AI for a specific task.
With result control
We create assistants for support, document searching, and inquiry processing. We connect them to your systems, test them on real scenarios, and set boundaries: what data is available and what actions require human confirmation.
PROCESS
DATA
CRITERIA
PILOT
INTEGRATION
CONTROL
on your task
QUALITY
TIME
COST
projects
of studio
models
Premier Partner status
marketing, analytics
Implementation begins from process and data
We choose a task with clear input and output: answer based on the knowledge base, process a request, prepare a document draft, or fill out application fields. We study current employee workflows and fix what AI should do and where the decision remains human.
We check sources, data quality, and available API. We assemble a pilot on a limited scenario, evaluate the correctness of answers, processing time, and expenses. If data is insufficient or the task requires other automation, we show this before large-scale implementation.
After the pilot, we connect agreed channels and systems. We implement access rights, login checks and action restrictions in the application. For uncertain answers, we provide a refusal or transfer to an employee, for significant operations — confirmation. We hand over instructions, test results and operating procedures.
- Assistants for clients and employees
- Searching for answers in the agreed knowledge base
- Parsing requests and transferring data to CRM
- Document processing with result verification
- Pilot, test scenarios, and instructions for the team
Scenario
We define the task and boundaries
Data
We check sources and access permissions
Pilot
We measure quality, time, and costs
Control
Limiting actions on the server
We check quality using your examples
Before launch, we agree on a test suite: common questions, missing data, ambiguous requests, and attempts to exceed permissions. The acceptance threshold is determined for a specific scenario, along with the handover route to a human.
Three foundations managed implementation
The model, knowledge base, and integrations must work within the scope of a single verifiable task.
Answers supported by sources
We configure search across your documents and the transmission of relevant fragments to the model — a RAG approach. We check the relevance of sources and access rules. This helps to anchor the answer to facts but does not eliminate errors: tests, control, and a no-answer scenario are required.
Actions via verifiable API
Creating an application, searching for status, and changing data are performed through separate server methods. We verify rights, parameters, and permitted operations regardless of the model text. Significant changes require agreed confirmation.
Observation and budget
We estimate request and infrastructure costs during the pilot. We configure limits, event logs, and error handling. We choose the model based on quality, latency, and cost using test cases, not by name.
From one scenario to working integration
We expand the system after pilot verification and approval of acceptance criteria.
Process and requirements
We describe input, result, users, and constraints. We select the first scenario, document the initial performance time, and define quality criteria.
Data preparation
We collect documents, rules, and examples. We check relevance, structure, access rights, and the list of data permitted for use.
Pilot and verification
We assemble a limited prototype. We run through approved examples, evaluate errors, latency, cost, and situations for handoff to an employee.
Channels and integrations
We connect agreed API and channels. We set up server-side parameter verification, permissions, limits, and confirmation of significant actions.
Boundary testing
We check common and ambiguous queries, system unavailability, repeated actions, and attempts to change instructions. We fix results and limitations.
Launch and handover
We deploy the system, train the responsible persons, provide instructions and a verification log. We agree on monitoring, database updates, and the procedure for correcting defects.
What is important in operation
We select the composition of the technical solution after studying the task and data.
Model and placement
We compare suitable models using your examples. We choose cloud or local hosting based on allowed data flow, requirements, quality, and budget; a local model also requires resources and maintenance.
Knowledge base and access
We version sources and set the update order. We restrict access to documents by role. We consider uploaded texts as input data, not permission to change the assistant's operating rules.
Logs and error handling
We record events taking into account the agreed data structure. We set timeouts, retries, and limits. For dangerous retries, we provide protection against duplicate actions and a clear notification to the person responsible.
— COST
Pricing
to AI implementation
Development and implementation. Expenses for APIs, servers, and subscriptions are paid separately.
| Scope of work | AI-assistant One channel and a limited knowledge base 55 000 ₽ From 7 days | POPULAR AI-integration Knowledge base, channels, and connection with CRM 110 000 ₽ From 14 days | Custom AI agent Scenarios and integrations for your process from 220 000 ₽ From 30 days |
|---|---|---|---|
|
Channels
|
1 messenger or site chat | Up to 3 agreed channels | Channels and ERP as per task |
|
Knowledge base
|
Up to 100 pages of materials | Up to 500 pages of materials | Dynamic sources per task |
|
CRM integration
|
Separate estimate | Deal creation by scenario | Approved operations and checks |
|
Model selection
|
For one scenario | Based on pilot results | Comparison for multiple scenarios |
|
Documents and recognition
|
Separate estimate | Separate estimate | Data extraction and import validation |
|
Handover to the team
|
Instructions | Instructions + 1 webinar | Training + 1 month of support |
|
Defect correction
|
14 days | 30 days | 90 days |
Timeline and scope are calculated after data verification and API availability. We define acceptance criteria, action rights, and the handover route to a human. The defect correction period applies to the agreed implementation, not a guarantee of error-free model responses. New scenarios and regular maintenance are priced separately.
— QUESTIONS AND ANSWERS
Everything that matters
before start
Scope of work, source materials, approval order, and launch conditions.
-
Can the assistant fabricate an answer?
Yes, the language model can make mistakes, including when using a knowledge base. We reduce risk through verified sources, test sets, scenario restrictions, and handover to a staff member if data is missing. For significant decisions, we provide human review. -
What data will the model receive?
Before work begins, we agree on the list of data, sources, and permitted processing locations. In the application, we restrict access by roles and the context transmitted. Secrets and administrative access are not included in the model instructions; we verify the conditions of specific providers and infrastructure during design. -
How much does the system cost after implementation?
API models, servers, storage, and necessary subscriptions are paid separately. The budget depends on the model, number of requests, context length, and load. During the pilot, we show a calculation based on your examples and set limits. Local hosting also has operational costs. -
Can AI be allowed to change data in CRM?
Yes, for agreed operations via server methods with permission and parameter validation. First, we define allowed actions, retry rules, and the need for confirmation. Free-form model text does not in itself grant data modification authority. -
What is needed for a pilot?
Description of one process, examples of requests or documents, current rules, and the responsible party on your side. Integrations require API details and test access. Based on these materials, we will agree on the scenario, set of checks, and acceptance criteria. -
What is included in support and defect fixing?
We hand over instructions and fix defects of the agreed implementation within the specified period. The third package includes a month of support. Adding scenarios, regular base updates, and external API changes are discussed separately; reaction to failures and support conditions are fixed in the agreements.