TECHNOLOGICAL ADOPTION PROGRAM

Fast-track technology adoption
in enterprise finance.

Main Sequence drives technology adoption—bringing your teams up to speed in developing from the business perspective—alongside end-to-end consulting services designed to industrialise, scale, and orchestrate your financial workflows.

Choose Your Path to Modernisation

Whether you need us to build the solution for you, or teach your team how to build it themselves, we have a structured approach to meet you where you are.

PATH 1

End-to-End Consulting

We work alongside your business to map, refactor, and scale your critical workflows. We handle the heavy lifting of industrialisation and platform integration.

  • Workflow Refactoring & Optimisation
  • Python Library Development
  • Platform Orchestration
PATH 2

Tailored Education Program

We train your finance and analytics teams to become self-sufficient developers. A curriculum designed specifically for the financial domain.

  • Python for Finance Fundamentals
  • Best Practices & Governance
  • Collaborative Development

Path 1: End-to-End Consulting

A Key Principle: Deliberately Platform-Independent

Most of the work we do together is deliberately platform-independent. We do not aim to push all your processes into the Main Sequence Platform.

Instead, we first industrialise and standardise your core workflows in a platform-agnostic way (Phase 1).

Then, only where it clearly adds value, we scale and orchestrate those workflows on the Main Sequence Platform (Phase 2).

This ensures you retain full ownership, portability, and flexibility over your logic and models, and avoid unnecessary platform lock-in.
PHASE 0

Workflow & Value Mapping

Objective: Understand how your teams work today and identify the fastest paths to value.

Main Sequence Will:

  • Map critical workflows and pain points across your finance organisation.
  • Identify quick wins that can be translated rapidly into business value.
  • Define project scope and priorities based on impact, complexity, and time-to-value.
  • Estimate ROI for integrating selected workflows into the Main Sequence Platform.
Deliverable

A workflow and value map with a prioritised list of use cases, expected benefits, and an initial roadmap.

In this phase, we refactor and standardise workflows independently of any specific platform, focusing on scale, maintainability, and auditability.

Typical Activities:

  • Analysing existing assets (e.g. Excel spreadsheets, R scripts, MATLAB code) that are hard to maintain or do not interoperate.
  • Extracting and consolidating reusable business logic into well-structured Python libraries.
  • Implementing clean interfaces, separating data, logic, and presentation layers.
  • Producing documentation and examples so business and analytics teams can confidently use and extend the libraries.
  • Supporting knowledge transfer and institutionalising the new way of working.
Note: At this stage, the results are fully portable. The Main Sequence Platform is not a requirement.
Deliverables
  • Refactored, reusable Python libraries encapsulating key workflows.
  • Technical and user documentation with clear examples.
  • A robust, maintainable process landscape.
PHASE 1

Platform-Independent Workflow Refactoring

Objective: Industrialise your core processes before scaling them.

Why Python?

We standardise on Python because:

  • 1 It is the de facto language of data science and analytics.
  • 2 It has a rich ecosystem for numerical computing, data processing, and integration.
  • 3 It is widely adopted in finance, making talent easier to hire and retain.
PHASE 2

Scaling & Integration on the Main Sequence Platform

Objective: Scale refactored workflows and unlock cross-system interoperability.

In Phase 2, we take the prioritised workflows from Phase 0 and the refactored assets from Phase 1 and:

  • Deploy and orchestrate them on the Main Sequence Platform, where appropriate.
  • Use Main Sequence as the interoperability layer between models, data sources, and applications.
  • Integrate with existing systems (e.g. risk engines, data warehouses, reporting tools) as required.
  • Set up monitoring, governance, and versioning to ensure reliability and control.
Deliverables
  • Production-grade, integrated workflows running on the Main Sequence Platform.
  • A scalable architecture that supports future use cases and extensions.
  • Clear operating procedures and ownership across business, IT, and analytics teams.

Consulting Success Stories

ASSET MANAGER

Automating Investor Reporting

Context

An asset manager was spending hours manually building customised investor presentations in PowerPoint—copying and pasting from Excel spreadsheets.

What we did
  • Refactored source spreadsheets so all core calculations were implemented in a shared Python library.
  • Separated data from logic by building a dedicated data layer outside of Excel.
  • Automated the production of tables and charts, replacing legacy Excel charts.
  • Orchestrated the entire workflow end-to-end.
FINANCIAL INSTITUTION

Institutionalising Pricing Calculators

Context

A financial institution had critical pricing and calculator logic scattered across R notebooks, MATLAB scripts, and Excel files.

What we did
  • Consolidated and refactored disparate processes into a single institutional Python pricing library.
  • Replaced siloed calculators with interactive dashboards.
  • Integrated the dashboards into the Main Sequence Platform for permission-based access.

Path 2: Tailored Education Program

Empowering Your Teams to Build

Our education track isn't just about syntax; it's about Business-Led Development. We teach your finance professionals how to translate their domain expertise into robust, scalable code.

Methodology: "Learning by Refactoring"

We don't use generic datasets. We use your Excel sheets and your legacy scripts. Your team learns by rebuilding their own daily tools, delivering immediate ROI while they learn.

Curriculum Highlights

  • 1
    Python for Financial Engineering Moving from Excel formulas to Pandas, NumPy, and vectorised calculations.
  • 2
    Software Engineering Standards Version control (Git), unit testing, code review processes, and CI/CD pipelines.
  • 3
    Architecture & Design Patterns Structuring projects for maintainability, separating data from logic, and API design.

Frequently Asked Questions