Services · AI & Automation
Autonomous AI agents and intelligent workflows for your business.
Beyond simple conversational chatbots: autonomous software agents that execute multi-step workflows, query internal databases, interact with APIs, and free up hundreds of operational hours.
Autonomous ReAct Agents
Goal-driven systems that reason, call external APIs, evaluate results, and self-correct until completion.
Model Context Protocol (MCP)
Universal, open standard linking Claude and LLMs to PostgreSQL, ERPs, CRMs, and internal files.
AI Integrated in Mobile & Web
Native intelligence inside Flutter mobile apps (iOS & Android) and custom web platforms.
Security & Zero Training
Your enterprise data stays private and GDPR-compliant, never used to train public foundational models.
How I work
Workflow Audit
We identify repetitive, error-prone, or time-consuming operational processes ideal for agentic automation.
Architecture & Tooling
I define the Reasoning Loop, tools, API endpoints, permissions, and security guardrails.
Integration & MCP
I build custom MCP servers and integrations connecting the agent directly to your enterprise systems.
Testing & Human-in-the-Loop
Sandbox evaluation with human approvals for critical actions before full production deployment.
From Chat to Action: Why Your Business Needs an Autonomous Agent
Traditional chatbots merely answer questions with predictive text. When a client or employee asks for real status updates or complex operations, a basic chatbot is stuck. An autonomous AI agent, however, has hands and eyes: it parses intent, queries internal databases via API, computes the right action, and updates your CRM or triggers workflows.
This transitions AI from an interesting writing toy into a relentless, 24/7 digital team member executing operations with surgical precision.
Open Standards & Security: Claude, Agent SDK, and MCP
I don't build proprietary black boxes that lock you into a single vendor. I build architectures grounded in industry standards: Anthropic's Claude ecosystem, Agent SDK, and the Model Context Protocol (MCP).
With MCP, your agent communicates modularly with SQL servers, GitHub, Slack, ERPs, or Google Workspace. Your company retains full ownership of data, prompts, and architecture.
- Tier-2 customer support agents grounded in live production data
- Automated financial reconciliation, invoice processing, and order routing
- Automated document and contract extraction from incoming PDFs
- Field operative assistants embedded directly inside Flutter mobile apps
- Strict Human-in-the-Loop controls for critical authorizations
Privacy, Data Governance, and EU Compliance
Data privacy is priority #1 when deploying AI in production. Every solution is architected using enterprise commercial API endpoints with zero data retention for training, strictly compliant with European GDPR and AI Act guidelines.
We enforce least-privilege access rules and comprehensive audit logging, giving you complete visibility into what the agent executed, which tools it invoked, and why.
Frequently Asked Questions
What is the key difference between a chatbot and an AI agent?
A chatbot is passive: it answers prompts with text. An autonomous AI agent follows the ReAct paradigm (Plan, Act, Evaluate, Self-Correct): it understands goals, executes digital tools (APIs, databases, scripts, headless browsers), and perseveres until the objective is accomplished.
Is company data used to train the underlying models?
No. Enterprise commercial APIs (such as Anthropic Claude API) include contractual zero-data-retention guarantees. Your proprietary data is never used to train public models.
Can we integrate AI agents with our existing ERP or CRM?
Yes. Leveraging the Model Context Protocol (MCP) and REST/GraphQL APIs, I connect agents to PostgreSQL, MySQL, HubSpot, Salesforce, custom internal databases, and messaging platforms like Slack or Telegram.
How do you prevent hallucinations and errors?
Through multi-layered architecture: strict JSON schema validations, Retrieval-Augmented Generation (RAG) limited to verified company sources, automated evaluation suites, and Human-in-the-Loop gates where sensitive actions require human confirmation.
How much does it cost to build and run an AI agent?
Pricing consists of two clear parts: the initial engineering and integration fee (fixed-price written quote), and direct usage API token costs (pay-as-you-go, typically low double digits monthly for standard enterprise volumes, vastly outweighed by saved labor hours).