Software · Data · Simulation

Data, AI and simulation. Built for production.

Sphenic develops software for data processing, machine learning and numerical simulation. We plan, build and operate systems from the first dataset to the production cloud environment.

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Research level
Computer science, engineering and physics at doctoral and postdoctoral level
Proven frameworks
Established methods for architecture, operation and security
One point of contact
Analysis, delivery and operation from one team
Services

What we build

Eight disciplines, one continuous approach. Each project combines sound models with robust software.

Machine learning and AI

Models that work with your data and stay measurable.

  • Natural language processing (NLP)
  • Supervised and unsupervised learning
  • LLM Fine-Tuning
  • Retrieval-Augmented Generation (RAG)
  • Model evaluation and monitoring

Data engineering and big data

Raw data becomes reliable, structured datasets.

  • ETL pipelines
  • Structured data extraction from documents
  • Large-scale data processing
  • Data quality and validation
  • Dashboards and visualisation

API development

Interfaces for high load and clear integration.

  • REST and streaming APIs
  • Search and query services
  • Custom web applications
  • Documentation and developer access

Infrastructure and DevOps

Stable platforms, automated and traceable.

  • Cloud architectures
  • Containerised applications
  • Servers, network and storage
  • CI/CD and infrastructure as code
  • Monitoring and operation

Security

Security is part of the architecture from the first design.

  • Security Design
  • Zero trust architecture
  • Security analysis and hardening
  • Access control and audit

Physics simulation

Numerical models for systems that cannot be tested in a laboratory.

  • Gravitational dynamics and N-body systems
  • Hydrodynamics
  • Parallel and high-performance computing
  • Numerical methods and solvers

Financial and portfolio simulation

Calculate scenarios and risks before capital is committed.

  • Monte Carlo simulation
  • Strategy backtesting
  • Portfolio and risk analysis
  • Analysis of regulatory filings

System architecture and modernisation

Understand, document and develop existing systems.

  • Architecture review
  • Legacy system migration
  • Technical documentation
  • Technical consulting
Approach

From problem to running system

Four steps with clear results. After each step, you see where the project stands.

  1. 01

    Analysis

    We define the goal, the data and the constraints. The result is a clear problem statement.

  2. 02

    Design

    We select methods and architecture. A prototype tests the assumptions early.

  3. 03

    Delivery

    We develop in short cycles, with tests and documentation.

  4. 04

    Operation

    We move the system into production, monitor it and develop it further.

Sectors

Who we work with

Organisations with large data volumes, high quality requirements and complex domain models.

01

Financial services and asset management

Portfolio analysis, risk models and data platforms

02

Financial data and regulation

Extraction and analysis of regulatory filings

03

Legal and advisory

Document analysis and research with NLP

04

Research and science

Simulation, high-performance computing and data analysis

05

Industry and engineering

Physical models and process data

06

Software and digital services

APIs, infrastructure and operation

Contact

Tell us about your project.

Describe your task in a few sentences. We reply with an initial assessment and a proposal for the next steps.

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