AI audit and roadmap
We analyze processes, time costs and data to prioritize implementation scenarios.
AI specialist for business
AI solutions that take on part of the operational workload
We identify high-value processes, connect company data and tools, and build assistants and agents with controlled logic.
CRM
DOCS
INBOX
AI AGENT
CONTEXT CONNECTED
SUPPORT
SALES
REPORTS
01
Request recognized · context verified · action approved
0.8s
24/7handling requests
APIconnected systems
HITLhuman control
Practical AI
Not a technology demo, but a working tool for the team
We start with the task and a measurable outcome. The model, data, integrations and control rules are selected for the specific process.
Assistants and chatbots
We build assistants that answer from the company knowledge base, documents and operating rules.
AI agents
We automate multistep work: find data, decide, update a system and notify the team.
Sales and marketing
Lead qualification, proposal preparation, personalized communication and content workflows.
Analytics and reports
We combine metrics from multiple sources, explain changes and prepare recurring reports.
Integrations and control
We connect CRM, ERP, email and messengers, then add evaluations, permissions and action logs.
Use cases
Where AI creates a tangible business result
We choose processes with repeatable actions, sufficient data and a clear quality criterion.
Customer support
Answers, ticket classification, solution search and escalation of complex cases to an operator.
Sales
Lead qualification, response preparation, CRM updates and next actions for managers.
Knowledge base
Search across documents, regulations, catalogs and internal materials with source references.
Operations
Processing requests, documents and tasks while synchronizing data between systems.
Content
Drafts, content adaptation, localization and tone-of-voice checks.
Analytics
Recurring reports, anomaly explanations and fast answers based on business data.
AI stack
A flexible architecture without lock-in to one service
We combine models, orchestration, data retrieval and communication channels around the task, budget and security requirements.
01
Models
OAOpenAIGMGeminiCLClaudeLMLlama
02
Orchestration
APIResponses APILCLangChainN8n8nMKMakePYPythonNDNode.js
03
Data and RAG
PGPostgreSQLPVpgvectorQDQdrantPCPineconeRDRedisS3S3
04
Systems and channels
CRMCRM / ERPTGTelegramWAWhatsAppSLSlackEMEmailGDGoogle Drive
Implementation stages
From a candidate process to controlled work inside the team
We first prove value in a focused scenario, then connect systems, add evaluations and scale.
- 01
Process audit
We study tasks, data, roles, risks and current time costs.
- 02
Prioritization
We choose a scenario with measurable impact and a realistic launch path.
- 03
Prototype
We quickly test answer quality and logic on real examples.
- 04
Data and integrations
We connect sources, tools and access permissions.
- 05
Evaluation and security
We test quality, action boundaries, failure cases and human oversight.
- 06
Launch and growth
We train the team, monitor production work and improve scenarios.
Control
Automation must remain predictable
We define access rules, approval for critical actions, quality evaluation and transparent system logs.
Human approval
Critical operations are executed only after confirmation by a responsible person.
Roles and access
The agent sees only permitted data and can use only explicitly allowed tools.
Quality evaluation
Test sets reveal accuracy, stability and regressions after every change.
Monitoring and logs
We record requests, actions, errors, cost and quality metrics for further optimization.
Next step
Let us find the process where AI creates value in its first version
Describe a recurring task, available data and the desired outcome. We will propose a realistic pilot and success criteria.
Start a conversation