From raw data to real decisions: we build the ML pipelines, predictive models and AI strategies that turn your enterprise data into a competitive advantage.
Every enterprise sits on a goldmine of operational data: integration logs, process metrics, customer interactions, partner transactions. Most of it goes unused.
We change that. Our AI practice combines data science with the engineering it takes to move models from notebook to production. Production AI, tied to measurable business outcomes.
What sets KONDEVS apart: we understand enterprise data at the infrastructure level. Our integration roots mean we know where data lives, how it flows and what it means in context. That makes our AI solutions faster to build, more accurate and easier to maintain.
We assess your data maturity, identify high-impact use cases and build a pragmatic roadmap aligned with your business objectives. No hype: clear priorities and measurable milestones.
Production-grade models for classification, prediction, anomaly detection and recommendation. Built, trained and deployed to be explainable, maintainable and ready for real-world data.
Large language models inside your workflows, from intelligent document processing and automated reporting to conversational interfaces, on OpenAI, Anthropic and open-source models.
Robust ingestion, transformation and feature pipelines, from batch ETL to real-time streaming with Apache Spark and modern data tools, so your models always get clean, fresh data.
CI/CD for machine learning. Model versioning, automated retraining, performance monitoring and drift detection keep your models accurate and your team productive.
Structure from unstructured text: classification, entity recognition, sentiment analysis and automated document processing for the data that flows through your integration layer.
| Use case | What it does | Built on |
|---|---|---|
| Predictive maintenance | Predicts system failures before they impact operations, reducing downtime and support costs | Integration platform telemetry |
| Process intelligence | Identifies bottlenecks, predicts SLA breaches and recommends workflow optimisations in real time | Machine learning on BPM data |
| Intelligent document processing | Automates data extraction and classification | Unstructured documents in B2B integration flows: invoices, orders, compliance documents |
| Conversational enterprise | Lets business users query enterprise data, trigger workflows and get answers in natural language | LLM-powered interfaces to enterprise data |
It turns enterprise data into measurable business outcomes. It covers AI strategy and roadmap, machine learning engineering, generative AI and LLM integration, data pipelines and feature engineering, MLOps and the model lifecycle, and NLP and intelligent document processing.
Its integration roots: KONDEVS understands enterprise data at the infrastructure level (where data lives, how it flows and what it means in context), which makes AI solutions faster to build, more accurate and easier to maintain.
It is for enterprises with underused operational data, such as integration logs, process metrics and customer interactions, that want production-grade AI. Example use cases are predictive maintenance, process intelligence, intelligent document processing and conversational interfaces to enterprise data.
With an assessment of your data maturity and the high-impact use cases, which becomes a pragmatic AI roadmap with clear priorities and measurable milestones. From there we move the first models from notebook to production.
Both. We work with commercial model APIs such as OpenAI and Anthropic and with open-source models, and choose per use case and data requirements.
When its safety classifiers flag a prompt, Claude Fable 5 falls back to Claude Opus 4.8. Integration layers need routing policies, fallback detection and human checkpoints.
Claude Opus 4.8 belongs in the premium tier of a routing policy. This guide covers effort levels per process step, mid-conversation system messages, guardrails and testing before migration.
In the τ²-bench airline domain, 78% of a budget agent's failures were silent wrong-state violations. Deterministic pre-execution gates raised success from 29.6% to 42.0% on gpt-4o-mini.