Services

Hylosense:

We support Hylosense in strengthening, scaling, and operationalizing their AI-driven weather intelligence capabilities to deliver better accuracy, reliability, and business impact.
Our work focuses on enhancing the modelling and data lifecycle of their platform:
• Improve hyper-local forecasting models by introducing more advanced machine learning techniques, better feature engineering, and continuous learning approaches that adapt to dynamic environmental conditions.
• Implement robust data pipelines and MLOps practices, enabling seamless ingestion of real-time sensor data, automated retraining, monitoring, and high-performance deployment at scale.
• Support the translation of predictions into actionable insights, empowering better decision-making across energy production, trading, grid optimization, and risk mitigation.
• Help structure and integrate climate analytics and compliance frameworks, ensuring that insights are not only accurate but also auditable and aligned with industry requirements.

Kontron:

We provide mentorship to help Kontron build and scale advanced capabilities in Natural Language Processing and AI systems, from foundational concepts to cutting-edge agentic architectures.

Our support spans the full NLP and AI lifecycle:

• Guide teams through foundational NLP concepts and traditional methods, including text preprocessing, feature engineering, and classical techniques like TF-IDF and word embeddings.
• Enable the development of machine learning solutions for text classification, helping organizations design, train, and evaluate models for use cases such as sentiment analysis, spam detection, and document categorization.
• Help adopt modern deep learning approaches, including working with pre-trained language models (e.g., BERT, GPT), fine-tuning strategies, and advanced prompt engineering techniques.
We support the design and implementation of Retrieval-Augmented Generation (RAG) systems, allowing companies to build reliable, knowledge-aware AI solutions that reduce hallucinations and leverage internal data.
• Assist in building AI agents and multi-agent systems that can reason, plan, and act using tools, APIs, and memory, enabling more autonomous and intelligent workflows.

SiaSoft:

We provided mentorship and consulting services to help SiaSoft to build a PoC solution for transport and logistics industry by designing and implementing RAG-based compliance systems that ensure alignment with complex and evolving regulations.

Our support focused on making regulatory requirements understandable, actionable, and auditable through AI:

• Help design Retrieval-Augmented Generation (RAG) architectures that connect internal data and external regulatory sources (e.g., EU directives, transport policies), enabling accurate, context-aware answers grounded in official documentation.
• Guide the implementation of systems that interpret regulations and determine applicability, clarifying what is allowed, restricted, or requires additional controls across different jurisdictions.
• Support building compliance-aware AI workflows that automate tasks such as document analysis, policy alignment, and regulatory reporting—reducing manual effort and errors.
• Help embed explainability, auditability, and traceability, ensuring that every AI-generated output can be linked back to specific regulatory sources—critical for audits and legal accountability.
• Assist in aligning systems with frameworks such as the EU AI Act, which enforces a risk-based approach, requiring transparency, safety, and human oversight—especially for high-risk transport use cases.
• Advise on limitations and guardrails, including handling ambiguity in regulations, managing data privacy (e.g., GDPR), and ensuring that AI systems remain assistive rather than fully autonomous in sensitive compliance decisions.

LearnTwoHelp

We provided mentorship and consulting services to help LearnTwoHelp design and build AI-powered educational platforms that leverage multimodal data orchestration for more effective and personalized learning experiences.

Our support was focused on turning complex data into structured, intelligent learning systems:

• Help design architectures that integrate and orchestrate multimodal data sources, including text, video, audio, images, and interactive content into a unified, scalable platform.
• Guide the development of AI-driven learning experiences, such as personalized content delivery, adaptive learning paths, and intelligent tutoring systems that respond to individual learner needs.
• Support the implementation of advanced data pipelines and orchestration frameworks, enabling efficient ingestion, processing, indexing, and retrieval of large-scale educational content.
• Help apply LLMs and multimodal models to tasks like content summarization, question answering, assessment generation, and semantic search across diverse learning materials.
• Advise on governance, quality, and ethics, including content validation, bias mitigation, data privacy (e.g., student data protection), and alignment with educational standards.