01Strategy · ML · GenAI · MLOps
Data Science & AI Consulting
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.
02In short
Key takeaways
- Turns underused enterprise data into production-grade AI with measurable business outcomes.
- Covers AI strategy, ML engineering, generative AI and LLM integration, data pipelines, MLOps and NLP.
- Integration roots ground the models in real enterprise data: faster to build, more accurate, easier to maintain.
- Use cases: predictive maintenance, process intelligence, document processing, conversational interfaces.
03Context
Data is your advantage, if you can use it
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. No science projects: AI that delivers measurable business outcomes.
04Context
The EAI + AI edge
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.
05What we deliver
What we deliver
- 1
AI strategy & roadmap
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.
- 2
Machine learning engineering
Production-grade models for classification, prediction, anomaly detection and recommendation. Built, trained and deployed to be explainable, maintainable and ready for real-world data.
- 3
Generative AI & LLM integration
Large language models inside your workflows, from intelligent document processing and automated reporting to conversational interfaces, on OpenAI, Anthropic and open-source models.
- 4
Data pipelines & feature engineering
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.
- 5
MLOps & model lifecycle
CI/CD for machine learning. Model versioning, automated retraining, performance monitoring and drift detection keep your models accurate and your team productive.
- 6
NLP & intelligent document processing
Structure from unstructured text: classification, entity recognition, sentiment analysis and automated document processing for the data that flows through your integration layer.
06Use cases
Use cases
| 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 |
07Platforms
Technologies we use
- Python
- TensorFlow / PyTorch
- LangChain / LlamaIndex
- Apache Spark
- MLflow / Kubeflow
- Vector databases
- OpenAI / Anthropic APIs
08FAQ
Frequently asked questions
What does the KONDEVS Data Science & AI Consulting service include?
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.
What makes KONDEVS' AI consulting different?
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.
Who is AI consulting for, and what outcomes can we expect?
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.
How does an AI consulting engagement start?
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.
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10Related
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11Next step
Your objective becomes our objective.
Tell us the objective, and we'll tell you honestly how we would approach it.