We deploy LLM apps and AI integrations that automate processes and run stable in production — first demo in 10–14 days.
- RAG, agents, tool-use — production-grade, not a demo
- Token cost control — routing, caching, monitoring
No obligations. NDA on request.
AI without architecture = chaos, costs and risk.
- Token costs grow 10× without smart routing and caching
- Manual eval eats 40+ engineering hours per month
- One hallucination in production = reputation and legal risk
- Without a monitoring pipeline, problems emerge after users complain
What does a month without AI architecture cost?
| Token costs without routing | €2–5k/mo |
| Time on manual eval | 40+ hrs/mo |
| Hallucination / data leak risk | priceless |
| Roadmap blocked by AI debt | €5–15k/mo |
3 months of delay = €20–100k+ burned without architecture guardrails
We deliver AI that works in production. Not slides.
RAG & Data Integration
We connect LLMs with your databases, documents and APIs. Retrieval-Augmented Generation with vector search, chunking and re-ranking.
- RAG & Data Integration
- RAG (Retrieval-Augmented Generation): an architecture pattern where an LLM generates answers grounded in retrieved enterprise data instead of relying on training knowledge alone.
Agentic Automation (MCP)
Autonomous AI agents that call tools, browse APIs and execute multi-step workflows. Built on the Model Context Protocol for interoperability.
- Agentic Automation (MCP)
- AI Agent: a system where an LLM autonomously plans and executes multi-step tasks by calling external tools and APIs based on a given goal.
LLM Apps (Web/Mobile)
Full-stack AI applications with chat, search, summarization or content generation. Production-grade UX with streaming responses.
- LLM Apps (Web/Mobile)
- LLM Application: a software product whose core functionality is powered by a Large Language Model, providing natural-language interfaces for business tasks.
Quality Evaluation (Eval)
Automated eval pipelines that measure accuracy, hallucination rate and relevance. LLM-as-judge, human-in-the-loop and regression tests.
- Quality Evaluation (Eval)
- LLM Evaluation: systematic measurement of an LLM system’s output quality using automated metrics, human review and regression benchmarks.
Cost Control (Routing/Cache)
Smart model routing, prompt caching and token budgeting. We reduce API costs by 40–70% without sacrificing quality.
- Cost Control (Routing/Cache)
- LLM Cost Optimization: techniques such as model routing, prompt caching and token budgeting that reduce API costs while maintaining output quality.
Monitoring & Security (RBAC)
Tracing, logging, cost dashboards, RBAC and audit trails. Full observability of every LLM call in production.
- Monitoring & Security (RBAC)
- LLM Observability: real-time monitoring of model calls, latency, cost and quality metrics with alerting and audit trails for production AI systems.
Engineering process. Zero 'we'll see'.
Five steps from data audit to production AI. Each with a clear deliverable.
Discovery & Data Audit
We audit your data sources, define use cases and map the AI opportunity landscape.
Architecture & PoC Design
System architecture, model selection, RAG design, eval strategy. Blueprint before code.
Pilot / Demo
Working prototype with your real data. Stakeholder demo, eval results, go/no-go decision.
Production Build
Full system with RBAC, monitoring, cost controls, CI/CD. Hardened for production traffic.
Maintenance & Monitoring
Ongoing: model updates, drift detection, cost optimization, SLA monitoring.
Security & Eval Checklist
- NDA signed before data access
- DPA / GDPR compliance verified
- RBAC & audit trail in production
- Automated eval pipeline running
- Hallucination monitoring active
- Cost alerting configured
Proof: numbers, reports, deployments.
Automated KYC document analysis with RAG — from 15 min to 90 sec per case
93% accuracy, 10× faster
AI product descriptions and SEO meta from catalog data — 1000+ SKUs automated
60% less editorial time
Clinical note summarization with privacy-first RAG pipeline
< 2% hallucination rate
Security and ownership: it's part of the offer.
- NDA, DPA and GDPR are our standard from day one, not an option
- Data stays on your infrastructure — we minimize access to the bare minimum
- RBAC and audit trail in every production deployment
- Full code ownership — your repo, your IP, zero vendor lock-in
Code & Data Ownership
- Repository on the client's GitHub/GitLab
- Zero vendor lock-in — swap models or providers at any time
- Full documentation: architecture, runbook, API reference
- Data minimization — we access only what's needed for the task
NDA, DPA and GDPR are our standard from day one — not an option.
Packages: from discovery to maintenance.
Discovery Sprint
Data audit, RAG hypothesis, estimate
1–2 weeks
- Data source audit & quality assessment
- Use case mapping & prioritization
- RAG architecture hypothesis
- Model selection recommendation
- Detailed cost estimate
Pilot / PoC
Working prototype with your data
2–4 weeks
- Everything in Discovery Sprint
- Working RAG/agent prototype
- Eval pipeline with baseline metrics
- Stakeholder demo
- Go/no-go recommendation
Production Build
Full AI system in production
4–10 weeks
- Everything in Pilot / PoC
- Production-grade RAG/agent system
- RBAC, audit trail, security hardening
- Cost controls (routing, caching, budgets)
- CI/CD pipeline + monitoring
- Full code handoff & documentation
Maintenance (SLA)
Monitoring, model updates, cost optimization
Ongoing
- 24/7 monitoring & alerting
- Model updates & drift detection
- Cost optimization reviews
- Eval regression monitoring
- Priority support SLA
Final price depends on scope. Free estimate after Discovery call.
What strongly affects the price
- Data volume and complexity (documents, databases, APIs)
- Model mode: cloud API vs on-premise deployment
- SLA level and uptime requirements
- Number and complexity of integrations (CRM, ERP, legacy systems)
What we DON'T do
- AGI or science-fiction promises
- Chatbots without a clear business goal
- "AI for the sake of AI" projects
Stack that delivers in production.
LLM
RAG & Embeddings
Frameworks
Application
Observability
Infrastructure
From day one you get: your repository, full documentation, infrastructure-as-code and the freedom to swap models or providers. Zero vendor lock-in.
Calculate your AI integration costs upfront
Build vs. buy? How much will your RAG pipeline cost? Use our free AI calculators to make data-driven decisions.
Build vs Buy AI Decision Tool
Compare the total cost of building custom AI vs. buying off-the-shelf solutions.
AI Integration TCO Calculator
Estimate the total cost of ownership for AI integration including infra, API calls, and maintenance.
RAG Pipeline Cost Estimator
Model the cost of a Retrieval-Augmented Generation pipeline based on your data volume and query load.
AI Ecosystem Integration ROI
Calculate the expected ROI of integrating AI across your product ecosystem.
FAQ: budget, timeline, risk, maintenance.
AI/LLM Glossary
- RAG (Retrieval-Augmented Generation)
- An architecture pattern where an LLM generates answers grounded in retrieved enterprise data, reducing hallucinations and ensuring up-to-date responses.
- LLM (Large Language Model)
- A deep learning model trained on massive text corpora that can understand and generate human-like text. Examples: GPT-4, Claude, Llama 3.
- Embedding
- A numerical vector representation of text that captures semantic meaning, enabling similarity search and retrieval in RAG systems.
- Eval (Evaluation)
- Systematic measurement of LLM output quality using automated metrics (accuracy, relevance, hallucination rate) and human review.
- Hallucination
- When an LLM generates confident but factually incorrect or fabricated information. Controlled through RAG, eval pipelines and guardrails.
- Fine-tuning
- Adapting a pre-trained LLM to a specific domain or task by training it further on curated data. Used when RAG alone doesn't achieve required accuracy.
Describe your AI challenge. We'll tell you what's realistic.
Free consultation within 24h. NDA on request.
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