AI Agents
AI Agents Development
Design and development of AI agents and multi-agent systems that automate complex business workflows — research, analysis, qualification, outreach, support.
Who it's for
- Companies with routine processes that can be automated
- B2B teams: sales, marketing, support, analytics
- Products that need an intelligent assistant
Outcome
- Working AI agents in your processes, not demos
- A multi-agent architecture built for your task
- Hours of manual work saved every week
Problems it solves
A single LLM chat does not automate a business process. You need agents with roles, tools and quality control. That is exactly what we design and build.
Who it's for
Employees spend hours on research, data collection and reports
The ChatGPT approach does not fit into business processes
No quality control or safety for AI actions
Agents work in isolation without coordination between stages
Orchestrated LLM calls get expensive and lose context
When you need it
The process consists of repetitive steps with data
You need multi-stage analysis: research → assessment → action
AI must work with your tools (CRM, Telegram, API)
You require a transparent, controllable AI workflow
Approach
We design agents as an engineering system: roles, tools, guardrails, quality metrics.
01
Discovery
Research, requirements and AI opportunity analysis
02
Architecture
AI system design: agents, RAG, data layer
03
MVP
Working product delivered in weeks, not months
04
Production
Deployment, monitoring, scaling and iteration
Technical expertise
Technologies our multi-agent systems are built on.
AI Agents
Role-based agents with tools and memory
OpenAI / Claude / Gemini
Model selection per role and task
OpenRouter
Routing between models and cost control
RAG
Agent access to corporate knowledge
React / Next.js
Interfaces for managing agents
PostgreSQL
State, logs and metrics storage
Expert articles
How we approach this in our own products — from the knowledge base.
FAQ
How is an AI agent different from a simple LLM chat?
An agent has a role, tools (API, CRM, databases), memory and the right to take actions. A chat answers questions; an agent performs work and returns results.
How many agents do we need for our process?
It depends on the process. Usually 2-5 specialized agents: researcher, analyst, executor, quality controller. We design a minimally sufficient architecture.
How do you control agent quality and errors?
Each agent has tasks, guardrails and an output format. All actions are logged, there is a manual review mode for critical steps and quality metrics.
Will agents work with our tools?
Yes. We connect agents to your APIs and systems: CRM, Telegram, email, internal databases. Access and permissions are scoped by role.
How fast is the first agent running?
A working prototype of a single agent usually takes 1-3 weeks. A full multi-agent system takes 4 to 10 weeks depending on the number of integrations.
Will this replace employees?
No. Agents take over routine work: data collection, preparation, drafts. People make decisions and control results — that is the architecture we build.