Custom artificial intelligence development
We build AI systems that integrate directly into your existing processes and work with your own data. Not generic off-the-shelf tools: solutions designed for your specific case, with the technical architecture needed to run reliably in production.
Production-grade AI architecture
We develop AI systems that truly work in real environments: with error handling, controlled latency, predictable costs and monitoring. Every project starts with a rapid prototyping phase to validate technical viability before investing in the full solution. We work with APIs from leading providers (OpenAI, Anthropic, Google) and select the optimal model for each task based on accuracy, speed and cost.
- Chatbots that answer customer queries using your own internal documentation
- RAG systems with vector databases (Pinecone, pgvector, Qdrant)
- Autonomous agents that execute complex multi-step workflows
- Data extraction pipelines for invoices, contracts and emails
- Automatic classification and intelligent triage of tickets, leads or documents
- API integrations with your existing systems (CRM, ERP, helpdesk)
- Model fine-tuning for industry-specific vocabulary and specialised tasks
- Monitoring of costs, latency and response quality in production
from kodai import Agent, RAG
# Set up the agent with context
knowledge = RAG.load(
source="docs/company",
model="embeddings-v3",
)
agent = Agent(
model="claude-sonnet",
knowledge=knowledge,
tools=["search", "analyze"],
)
# The agent responds with context
response = agent.run(
"Analyse Q1 sales data"
)
print(response.answer)Concrete examples
RAG-powered support assistant
A chatbot connected to your knowledge base (manuals, FAQs, product sheets) that answers customer questions with accurate, verified and up-to-date information, available at all times.
Internal process agent
An agent that receives supplier emails, extracts relevant data (amounts, dates, references), updates your ERP and sends a summary to the person in charge. No human intervention needed.
Document data extraction
A pipeline that processes hundreds of invoices or contracts in PDF, extracts specific fields (tax ID, amounts, clauses) and structures them into a database or spreadsheet.
Automatic classification and triage
A system that reads support tickets, web form leads or internal requests, classifies them by urgency and topic, and automatically assigns them to the right person or department.
From idea to production
Technical analysis
We study your data, existing systems and the problem you want to solve. We evaluate which models and architectures fit best, and whether AI is truly the right solution for your case.
Functional prototype
We build a working prototype you can test with your real data. This validates technical viability and solution value before investing further.
Full development
We build the solution with error handling, tests, security and monitoring. Each sprint includes a demo to validate we're on the right track.
Deployment and monitoring
We deploy to production, set up performance and cost alerts, and support you through the initial phase to fine-tune and optimise.
What we master
Models & LLMs
Frameworks
AI Infrastructure
Integration
Frequently asked questions
It depends on the task. For a support chatbot, a fast and economical model may suffice (GPT-4o mini, Claude Haiku). For complex document analysis, a more powerful model may be needed (GPT-4o, Claude Sonnet). We run benchmarks with your real data to choose the best quality/cost ratio.
The main cost is the API consumption of models (processed tokens). A chatbot with moderate usage can cost between 20 and 100 euros per month in API. We design the architecture to minimise costs: caching frequent responses, model selection by complexity and configurable usage limits.
Yes. We build solutions that connect to your systems via API, webhooks or direct database access. If your current software doesn't have an API, we can create an integration layer. No need to replace anything — AI is added on top of what you already have.
Hallucinations are a real risk with LLMs. To minimise them, we use techniques like RAG (making the model consult verified sources), response validation, confidence thresholds and human review when needed. We also monitor quality in production to detect and correct issues quickly.