We deliver AI that runs in production and takes care of real business environments.
We've been building production-grade systems since 2002, long before "AI" became a sales term, which allows us to deliver integrations without any rewriting required.
Our AI team consists of senior engineers and applied mathematicians with production ML experience across enterprise Java, .NET, and cloud-native systems. Every AI feature we build goes into your existing codebase, CI/CD pipeline, and infrastructure.
Selecting us as an AI enhancement partner, you can expect full-cycle AI development from a team that has shipped AI features into real enterprise environments.
Integration of GPT-4o, Claude, Gemini, or open-source models directly into your existing product, embedded into the current database – with chat interfaces, document analysis, code generation, or intelligent search tailored to your data model.
Retrieval-Augmented Generation pipelines that let the LLM reason over your private data (docs, CRM, ERP, databases) without data leaks or hallucinating. Includes vector database design, chunking strategy, and embedding pipeline.
Multi-step autonomous agents designed to execute routine operations: processing documents, triggering API calls, routing approvals, or summarizing operational data.
Custom machine learning models built end-to-end for your specific domain: demand forecasting, anomaly detection, churn prediction, quality control – through data pipeline design, model training, evaluation, deployment, and monitoring.
Not sure where AI may be a fit in your system? We provide a detailed product and data landscape audit to identify high-ROI integration points, and deliver a practical adoption roadmap – including effort estimates and risk flags.
Increased performance for industrial, logistics, or retail environments through image classification, object detection, and defect recognition. Works wherever repetitive visual operations form the core: live camera feeds, document scans, or batched image pipelines.
AIoT transformations that make systems fully autonomous – edge intelligence on devices, in vehicles, and on factory floors. Anomaly detection on sensor streams, predictive maintenance, and real-time classification, combining our IoT protocol expertise (CAN, MQTT, NB-IoT) with ML inference at the edge.
Discover our IoT & Big Data pageA dedicated team of AI engineers embedded directly into your delivery process, working alongside your existing teams as your AI roadmap scales.
Tell us about your system – we'll show you where AI can add value, and where it can't.
We assess your current product and data landscape and tell you honestly where AI can add value, where it can't, and what it would realistically take to ship it.
We take ownership of the full AI feature lifecycle – from requirement definition and data audit through model selection, integration, testing, and production release.
We connect AI capabilities directly into your backend, existing APIs, current databases, and live CI/CD pipeline – without rewriting.
Need AI that doesn't touch public model APIs? We deploy open-source models (Llama 3, Mistral, Phi-3) on your own infrastructure – on-premise or private cloud. GDPR-compliant by design.
We instrument your AI features with monitoring and evaluation frameworks, drift detection, latency tracking, and feedback loops – so you see exactly how your models perform in production.
Our AI stack is selected for production stability and enterprise integration.
We start by understanding your product, data, and the actual problem – not the AI solution you think you need. We audit data availability and quality, map integration points into your existing architecture, and identify which AI approach fits your constraints (latency, privacy, cost, accuracy).
Before any production work, we validate the approach. A working PoC tests the core AI capability against your real data in a controlled environment. Within a few weeks – typically up to 3 – you see it work (or see clearly why it doesn't) before committing to a full build.
With the PoC validated, we design the full AI system: data pipelines, model selection and configuration, prompt engineering, vector store design (for RAG), integration points, API contracts, and evaluation methodology.
We build the AI features alongside your existing codebase – not as a separate system bolted on later.
AI features have a different testing surface than traditional software. We test for accuracy, hallucination rate, latency, regression, and adversarial inputs. Evaluation sets are built from your real-world data, not synthetic benchmarks.
We deploy with proper observability in place from day one – logging, latency monitoring, cost tracking, and model performance dashboards. If something degrades in production, you'll know before your users do.
AI features are never "done". Model performance drifts, usage patterns change, and new capabilities emerge. We stay on as your AI engineering partner – monitoring, fine-tuning, and extending as your product evolves.
Free. No commitment. Usually takes 30 minutes.
We integrate into what you have. Our AI engineers work with Java, .NET, Node.js, and Python backends. We don't require a rewrite, and we won't recommend one unless it's genuinely the only path forward.
Yes. We deploy private, self-hosted AI models on your infrastructure – on-premise or private cloud. Nothing leaves your network.
A PoC is typically 1–3 weeks. A production-ready AI feature is typically 6–16 weeks from discovery to release. We give accurate estimates after the readiness assessment, not before.
Yes. Our AI Audit & Strategy offering covers exactly this: an independent assessment, clear recommendations, and an adoption roadmap that can be driven by any vendor.
Absolutely. We commonly build on top of existing infrastructure – connecting AI tools to new surfaces, extending capabilities, or replacing brittle no-code automations with properly engineered systems.
We will get back to you in 12 hours or sooner
Totally non-obliging 
Speak with our consultants and get the possible cost and duration of your project