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GenomeCure.AI

Services

Practical AI and data services, end to end.

From applied machine learning and RAG systems to data engineering and analytics — we work across the full lifecycle of an AI-enabled system.

01 / Service

AI and Machine Learning

Predictive models, deep learning prototypes, and evaluation pipelines designed to be useful, testable, and maintainable.

  • Predictive modeling and classification
  • Deep learning and computer vision prototypes
  • Model evaluation, benchmarking, and documentation
  • Custom ML workflows for production and research

02 / Service

LLM, RAG, and Knowledge Systems

Retrieval-augmented assistants and agentic workflows that turn scattered documents and tools into useful, grounded answers.

  • Retrieval-Augmented Generation (RAG) systems
  • Internal knowledge assistants and semantic search
  • Literature and document intelligence tools
  • Prompt engineering and agentic workflow design

03 / Service

Data Engineering and Automation

Pipelines, orchestration, and cloud data architecture that give AI systems a foundation they can depend on.

  • ETL and ELT pipelines
  • Workflow orchestration and scheduling
  • Cloud and lakehouse-style data architecture
  • Data quality, validation, and reproducibility

04 / Service

Analytics and Decision Intelligence

Dashboards, experimentation, and analysis that translate complex data into decisions the rest of the team can act on.

  • Dashboards and analytics-ready datasets
  • Metrics design and experiment analysis
  • Business intelligence prototypes
  • Data science consulting and insight reviews

05 / Service

Scientific and Research AI

Research-grade AI workflows for genomics, scientific imaging, and other complex datasets, built with reproducibility in mind.

  • Bioinformatics and genomics workflows
  • Scientific machine learning and imaging
  • Reproducible computational pipelines
  • AI support for academic and biotech teams

How we engage

A simple, honest engagement model.

We prefer focused, well-scoped engagements over open-ended retainers. Most projects start with a short discovery call and a defined first milestone.

  1. 01

    Discovery

    We start with a short, focused conversation to understand the problem, the data, and the constraints. No commitment required.

  2. 02

    Scoping

    We translate the goal into a clear scope, deliverables, and success criteria. You get an honest read on feasibility before any build work begins.

  3. 03

    Build

    We develop pipelines, models, or systems in tight iterations, with documentation and evaluation built into the workflow rather than bolted on.

  4. 04

    Handover

    We hand over working code, clear documentation, and evaluation artifacts. You can take it forward yourself or continue with us in a support role.

Next step

Not sure which service fits?

Send us a few sentences about the problem. We will reply with the shape of an engagement, or with an honest 'this is not a fit' and a pointer where to look next.

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