5 Ways Engineers Use ChainSolve to Replace Spreadsheets

Spreadsheets are the most widely used computation tool on the planet. They are also one of the most error-prone. Studies estimate that 88% of spreadsheets contain at least one error, and spreadsheet mistakes have caused billions in financial losses across industries.

ChainSolve was built to solve this problem. Here are five real ways engineering teams are using ChainSolve to replace their most critical spreadsheets.

1. Structural Load Calculations

Civil and structural engineers routinely calculate load distributions, bending moments, shear forces, and deflections using spreadsheets. A typical structural calculation spreadsheet has dozens of intermediate values that depend on each other in non-obvious ways.

In ChainSolve, each calculation becomes a visible block. The load path through a structure mirrors the data path through the graph. When you change a load value, you can watch the effects propagate through the entire structural model — bending moments update, deflection values recalculate, and safety factors re-evaluate in real time.

The visual representation also makes peer review dramatically easier. Instead of auditing cell references in a spreadsheet, reviewers can trace the data flow visually and verify that the computational logic matches the engineering intent.

2. Signal Processing Pipelines

Electrical and audio engineers process signals through chains of filters, transforms, and analyses. In a spreadsheet, this means columns of intermediate data, complex array formulas, and often VBA macros to implement operations like FFT.

ChainSolve’s block library includes dedicated signal processing blocks:

FFT / Inverse FFT: Frequency domain analysis
Low-pass / High-pass / Band-pass filters: Configurable cutoff frequencies
Windowing functions: Hann, Hamming, Blackman, and custom windows
Convolution: Arbitrary kernel convolution
Resampling: Up-sample and down-sample with anti-aliasing

Each block processes a signal array and passes it to the next, creating a clear pipeline from raw input to processed output. You can tap into any point in the pipeline to visualize the signal using built-in chart blocks.

3. Financial Modeling and Sensitivity Analysis

Financial analysts build complex models with interconnected assumptions, revenue projections, cost structures, and valuation calculations. These models are notoriously fragile in spreadsheet form — change one assumption and you might accidentally break a formula three tabs away.

In ChainSolve, the entire financial model is a single visible graph. Assumptions are input blocks at the top, and outputs (NPV, IRR, payback period) are result blocks at the bottom. Every dependency is an explicit wire.

But the real power comes from sensitivity analysis. ChainSolve’s sweep block lets you vary an input across a range and see how every downstream value responds. Want to know how your IRR changes as customer acquisition cost varies from £50 to £200? Connect a sweep block to the CAC input and a chart block to the IRR output. The answer appears instantly as an interactive graph.

Financial Model Template

Start with our pre-built SaaS financial model template and customize it for your business.

4. Control System Design

Control engineers design PID controllers, state-space models, and feedback loops. Spreadsheets are a poor tool for this because they lack native support for transfer functions, frequency response analysis, and time-domain simulation.

ChainSolve provides a set of control system blocks that make it natural to design and simulate controllers:

Transfer function blocks: Define s-domain or z-domain transfer functions
PID controller block: Configurable P, I, D gains with anti-windup
Feedback block: Close the loop by feeding outputs back to inputs
Step response block: Simulate and plot the time-domain response
Bode plot block: Generate frequency response magnitude and phase plots

The visual graph naturally mirrors the block diagram notation that control engineers already use. A feedback loop in ChainSolve looks exactly like a feedback loop on a whiteboard.

5. Data Pipeline Prototyping

Data engineers often prototype ETL (Extract, Transform, Load) pipelines in spreadsheets before implementing them in code. The spreadsheet serves as a sandbox for testing data transformations on sample data.

ChainSolve is a better sandbox. Its data transformation blocks (filter, map, join, aggregate, pivot) operate on tabular data and can be chained together visually. You can import a CSV sample, build a transformation pipeline, and verify the output — all without writing code.

When the pipeline is working correctly, ChainSolve can export the graph as a Python script or a SQL query chain, giving you a head start on the production implementation.

Step [counter(steps)] Import your sample data

Use the CSV Import block to load a sample dataset from a file or URL.
Step [counter(steps)] Build the transformation pipeline

Add Filter, Map, Join, and Aggregate blocks to transform the data step by step.
Step [counter(steps)] Verify the output

Connect a Table block to view the final result and verify it matches your expectations.
Step [counter(steps)] Export to code

Use the Export block to generate equivalent Python (pandas) or SQL code for production use.

Making the Switch

Migrating from spreadsheets to ChainSolve does not have to be all-or-nothing. Many teams start by rebuilding their most critical or error-prone spreadsheet in ChainSolve, then gradually migrate others as they see the benefits.

ChainSolve can also import Excel formulas and attempt to reconstruct the computation graph automatically. The importer handles basic formulas and cell references, though complex VBA macros require manual conversion.

Start Building Today

Whether you are calculating structural loads, processing signals, modeling finances, designing controllers, or prototyping data pipelines, ChainSolve gives you a clearer, safer, and faster way to work with calculations.