Internal Knowledge Copilots
LLM assistants trained on your docs, Confluence, and Slack that answer employee questions instantly — reducing onboarding time and knowledge silos.
Stop Experimenting
in Notebooks.
Production AI: LLM copilots, RAG systems, ML pipelines that automate complex workflows.
Trusted across Europe
Industries we serve.
Engineering teams in regulated, mission-critical industries — every engagement audited, documented, and production-graded.
Banking & Payments
FinTech
PCI-DSS compliant payments and core banking infrastructure — sub-100ms p99 latency, end-to-end audit trail, and tokenization at the edge.
PCI-DSS · ISO 27001
Patient Data
Healthcare
HIPAA-aware patient data pipelines
HIPAA · SOC2
5G & Networks
Telecom
5G core network observability at scale
NFV · ETSI MANO
Retail & Marketplaces
E-Commerce
99.99% uptime during peak traffic events
PCI-DSS · GDPR
Sovereign & Public
Government
Sovereign cloud with full audit trails
eIDAS · FIPS 140-2
Fleet & IoT
Logistics
Real-time fleet tracking & IoT ingestion
MQTT · OPC-UA
LLMRAG & Copilots
MLOpsProduction Pipelines
80%Noise Reduction
GPUCloud AI Clusters
What we deliver
Our ai services
From prototype to production — AI solutions that solve real business problems
Cut employee search time by 90% with AI copilots that understand your domain. We connect large language models to your internal data with retrieval-augmented generation for chatbots, document Q&A, and AI assistants.
↓ 90% search time · 85% answer accuracyTurn raw data into predictive power with end-to-end ML workflows. From feature engineering to model serving, we build predictive models with full experiment tracking and reproducibility.
end-to-end ML · automated retrainingFree teams from repetitive tasks that cost thousands in manual effort. AI-driven decision engines for document classification, invoice processing, email routing, and anomaly detection.
↓ 70% manual review · 85% auto-processedExtract actionable insights from unstructured text at scale. Sentiment analysis, entity extraction, and summarization that turn documents, emails, and conversations into structured data.
real-time text analytics · structured outputAutomate visual inspection and analysis that previously required human eyes. Image classification, object detection, and quality control for manufacturing, retail, and security workflows.
95%+ accuracy · real-time inferenceKeep models accurate long after initial deployment. Model versioning, A/B testing, drift detection, and automated retraining pipelines ensure production AI stays reliable.
0 model drift · continuous monitoringFree assessment
Get a free AI assessment
Our engineers review your current setup and deliver a prioritized roadmap — no strings attached.
AI use cases
Real-world applications
Four production patterns delivering measurable impact across insurance, manufacturing, finance, and engineering.
Predictive Maintenance
ML models that predict equipment failures before they happen, cutting unplanned downtime by up to 40%.
Document Intelligence
Automated extraction and classification of invoices, contracts, and compliance documents.
AIOps
Intelligent alert correlation, anomaly detection, and root cause analysis — reducing alert noise by 80% and cutting MTTR across engineering teams.
AI stack
Built on proven AI foundations
Best-in-class tools across the AI ecosystem — from frontier LLMs like GPT-4 and Claude to open-source models you can self-host. Our MLOps stack ensures every experiment is tracked, every model versioned, every deployment monitored.
Core technologies
Python, PyTorch & TensorFlowLangChain, LlamaIndex & RAG frameworksOpenAI, Anthropic Claude & open-source LLMsHugging Face TransformersMLflow, Weights & BiasesKubernetes GPU clusters & cloud AI services
Real Project
AI-Powered Document Processing for an Insurance Company
01 / 02
An insurance company was manually processing over 2,000 claims per day with a 48-hour average turnaround, creating bottlenecks and increasing operational costs.
Tech stack
PythonLangChainGPT-4Tesseract OCRFastAPIPostgreSQLDocker
Manual processing of 2,000+ claims/day, 48h average turnaround.
RAG pipeline with LLM for document classification, data extraction, and automated routing.
85% of claims auto-processed, turnaround from 48h to 2h, 70% reduction in manual review.
Outcomes & method
AI that Delivers business value
Most AI initiatives fail because of poor problem framing, insufficient data preparation, or lack of production engineering. We bridge the gap between experimentation and reliable systems at scale.
Business outcomes
- 01
Data-driven decision making
ML models surface patterns humans miss — from demand forecasting to fraud detection — giving leadership actionable intelligence.
- 02
Operational efficiency at scale
Intelligent automation handles tasks that previously required hours of manual effort, reducing costs and accelerating throughput.
- 03
Competitive differentiation
AI-powered products create moats that are hard to replicate — from personalized experiences to predictive capabilities.
How we implement
- 01
Discover & scope
We assess your data landscape, identify high-impact AI opportunities, and define success metrics before writing model code.
- 02
Build & validate
Iterative development with rapid prototyping, stakeholder demos, and rigorous evaluation ensures models solve the right problem.
- 03
Deploy & evolve
Production deployment with monitoring, drift detection, and automated retraining pipelines that keep models performing over time.
Engagement model
How we work
From first call to production — a proven 4-step engagement model that keeps the conversation transparent and the velocity honest.
- 01
Discovery
We audit your current stack, identify gaps, and align on business goals.
- 02
Assessment
A detailed roadmap with priorities, effort estimates, and quick wins.
- 03
Delivery
Our engineers embed with your team and execute sprint by sprint.
- 04
Support
Ongoing monitoring, optimization, and knowledge transfer to your team.
Related disciplines
Related services
Adjacent practices that pair well with this one — most engagements blend two or three.
Common questions
Frequently asked questions
Practical answers about scope, timelines, and how engagements with our AI team usually look.
A proof-of-concept typically takes 4-6 weeks. Production deployments range from 8-16 weeks depending on data complexity and integration requirements. We always start with a scoping phase to define success metrics and validate feasibility.
It depends on the use case. LLM-based solutions like RAG copilots can work with existing documents and knowledge bases. ML models for prediction need historical data — we assess data readiness during the discovery phase and recommend strategies for data gaps.
A 2-hour deep dive into your data landscape, business processes, and AI opportunities. You receive a prioritized list of use cases with feasibility ratings, estimated ROI, data requirements, and a recommended implementation roadmap.
Yes. We integrate with whatever data stack you have — from data warehouses like Snowflake and BigQuery to raw databases and file systems. If your data infrastructure needs improvement, we can help with that too.
We follow strict data handling protocols including encryption at rest and in transit, access controls, and data minimization. For sensitive data, we recommend self-hosted LLMs and on-premises deployment options that keep data within your infrastructure.
Talk to engineering
Let's talk about your AI strategy
Whether you're starting from scratch or scaling what you have, our engineers are ready to help.