Tech Stack

AI tools matched to your company's systems

Company environment

Cloud and platforms

Solution infrastructure

We build AI solutions in the cloud the company already owns, most often Microsoft Azure, GCP or AWS. Documents, files and models stay in one consistent environment, and sensitive data is processed to the highest security standard adopted by the organisation.

Azure

Azure OpenAI
Azure AI Foundry
Azure AI Search
Cosmos DB
Entra ID

AWS

Bedrock
SageMaker
Lambda
S3
DynamoDB

GCP

Vertex AI
Gemini API
BigQuery
Cloud Run
Cloud Storage

Complementary platforms

Cloudflare
Supabase
Vercel

Integration with company systems

The connection layer

We integrate AI solutions with the systems the company uses every day: Microsoft 365, ERP and CRM systems such as SAP, Salesforce or Dynamics 365, procurement platforms, data warehouses and BI systems. Integration is the longest stage of a project, so permissions, data access, the data protection impact assessment (DPIA) and classification under the EU AI Act are settled in the project charter at the very start.

Workplace

Microsoft 365 Copilot
Copilot Studio
MS Teams
SharePoint
Power Platform

Enterprise Systems

SAP
SAP SuccessFactors
Coupa
Ivalua
Salesforce
Dynamics 365
HubSpot
ERP & CRM integration

Governance

EU AI Act
GDPR / RODO
AI Risk Frameworks
DPIA
Security Reviews

AI layer

AI models

The core of the solution

We select AI models individually for each project and its specific tasks, most often models from OpenAI, Anthropic and Google, plus selected open-source models. A cheaper model handles the simple steps, a stronger one comes in where answer quality decides the outcome. The cost of the solution therefore grows more slowly than the number of cases handled.

Language models (LLM)

ChatGPT
Claude
Gemini
Grok
Llama
Mistral
DeepSeek

Image

Midjourney
Stable Diffusion
Flux
ComfyUI

Voice

ElevenLabs
Whisper
OpenAI Realtime
Azure Speech

Video

Sora
Veo
Seedance AI
Runway
Kling

AI agents and orchestration

Agent and process coordination

AI agents carry out multi-step tasks in the company's systems. Each agent receives a defined set of tools, cost limits and approval points where a human makes the decision (human-in-the-loop). The scope of the agent's autonomy and authority is agreed before deployment and written into the project charter, and every action it takes is logged.

Agents & Chains

OpenAI AgentKit
Semantic Kernel
MCP
LangChain / LangGraph
Multi-agent Workflows

Process automation

n8n
Make
Zapier
Power Automate
Azure Functions

Techniques and methods

OCR / Document AI
RAG & Vector Search
Prompt Engineering
Evals & Benchmarks

Chatbots and voicebots

The conversational layer

We build chatbots and voicebots on the company's knowledge base and deploy them in the channels the company already uses: communication platforms, website chat or the helpline. Every deployment has a set of control questions and an accuracy measure, and conversations without a confident answer are handed to a consultant.

Functions

Answers from the knowledge base (RAG)
Context Memory
Intent Recognition
FAQ Automation
Answer quality testing

Contact channels

Web Chat
MS Teams
WhatsApp
Slack
Telegram
Voice / IVR

Engineering and tools

Engineering and maintenance

We build in languages and tools every IT department knows: Python, TypeScript, SQL and Git. The engineering standard is the same for a POC and for production: version control, automated tests, code reviews and documentation. The company receives a solution its own team can read, change and extend with its own resources.

Languages

Python
TypeScript
Node.js
SQL
PowerShell

Interfejsy

React
Next.js
Astro
Tailwind CSS

Data and analytics

Pandas
NumPy
Jupyter
Power BI
Snowflake

DevOps and testing

VS Code
Claude Code
Cursor
Antigravity
Git
GitHub
Docker
Playwright

Not sure where to start?

A 15-minute call is enough to work out which package to start with.

Book an intro call