Binerals delivers AI and ML consulting services that move from strategy to production — not just proof-of-concept. As a hands-on ai and machine learning consultancy, we cover ML model engineering, AI/ML enablement, large language model integration and end-to-end AI solutions and machine learning consulting for companies that need results, not slide decks.
Let's TalkStrategy before code. We assess your data maturity, identify the highest-value ML use cases and define a delivery roadmap — so engineering effort goes where it actually moves your business metrics.
End-to-end ML model engineering services: problem framing, feature engineering, model selection, training, evaluation and production deployment. We own the full pipeline, not just the notebook.
Embed ML capabilities into your existing product or platform. AI/ML enablement services include building inference APIs, integrating models into your data pipelines and setting up the tooling your team needs to ship and iterate on AI features independently.
Document classification, entity extraction, semantic search, summarisation and conversational AI. We build NLP systems on top of open-source and commercial LLMs — tuned for your domain and data.
Object detection, image classification, OCR, quality inspection and visual anomaly detection. We deploy models that process images and video in real time — in the cloud or at the edge.
Demand forecasting, churn prediction, risk scoring, anomaly detection. We build models that give your operations and product teams actionable predictions — connected to your existing dashboards and workflows.
RAG architectures, fine-tuned language models, AI agents and copilots. We integrate GPT, Claude, Gemini and open-source LLMs into your product — with grounding, tool-calling and guardrails for production reliability.
CI/CD for ML models, experiment tracking, feature stores, model registries and monitoring for drift. We build the infrastructure that lets your team train, deploy and retrain models without manual intervention.
Practical applications we have delivered or are actively building — not theoretical roadmaps.
Before writing a single training script, we audit your data quality, pipeline maturity and deployment environment. This upfront work is what separates prototypes that get shelved from models that run in production for years.
Yoobelong is transforming how seniors connect, interact, and thrive in their communities. By blending social networking, location-based services, and curated content, this innovative German startup is creating a digital ecosystem that fosters genuine connections and supports vibrant local living for the 60+ community.
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Jil Gunsenheimer
Managing Director, YOOBELONG GmbH
“Overall, we're very satisfied with this collaboration.”
A cutting-edge spiritual social platform that fuses gamification and business features, available through web and mobile applications.
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Roman Rombaba
Founder of Karmabook
“They were extremely professional and focused even during a very difficult time for them.”
The combined mobile and web platform connects the sports teams and athletes with the service providers to arrange comprehensive sports trips around the world. The team and coaches can choose from a variety of hotels, sports objects, and services to make the best trip possible.
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Oleg Shats
Founder of Active Planet Sport LTD
“We felt like we had a true partner, not just a vendor. What we value most is that they went beyond development - supporting us during the investment stage and helping us present the product in the best light. Thanks to their care and commitment.”
From use case discovery to monitored production models
We evaluate your data assets, business objectives and technical environment to identify the ML use cases with the best ratio of impact to implementation cost. The output is a prioritised roadmap — not a list of AI buzzwords.
We structure, clean and version your training data, and set up the ingestion and transformation pipelines that make model development reproducible. No good model survives bad data infrastructure.
We build, train, evaluate and deploy models into production — integrated with your existing systems via APIs. Each model ships with monitoring hooks, rollback capability and documented performance baselines.
Models degrade. We track drift, schedule retraining on new data and alert before business metrics are affected. We also measure actual business impact — not just accuracy on a holdout set.
Our data scientists and ML engineers have production experience across multiple domains. We do not put junior engineers on client projects with senior supervision — the people who scope the work are the ones who build it.
We do not hand over a Jupyter notebook and call it done. From use case discovery and data pipeline design to model deployment and monitoring — we own delivery end to end and measure success in production metrics, not lab metrics.
AWS SageMaker, GCP Vertex AI, Azure ML, PyTorch, TensorFlow, HuggingFace, LangChain — we work with the tools that fit your infrastructure, not the ones we happen to know best.
Bias testing, model explainability, data privacy compliance and human-in-the-loop design are part of every engagement — not optional add-ons. Models we build are ones you can explain to regulators and customers.