anonymized case study

Product-level carbon emissions platform that made expensive advisory work scalable.

A privacy-safe case study on a product-level carbon emissions platform that cut advisory cost and stabilized data intake.

See proof policy

Sector

Sustainability and carbon advisory

Stack

AdonisJSTypeScriptHeroku

privacy boundary

The advisory firm, customer datasets, and exact manufacturing inputs are withheld. The architecture pattern and outcomes are shown instead.

the change

Before

Carbon advisory was specialist time on a calculator. Analysts read manufacturing files by hand, customers complained about speed and templates, and deploys depended on someone knowing the runbook.

Carbon-neutral advisory work was manual: analysts read manufacturing cost and resource files by hand to calculate product-level emissions.

Customer complaints centered on parsing speed and data templates: large files took minutes and corrupt rows broke entire batches.

Server setup and deploys were a manual handoff that slowed every release and reduced confidence in changes.

What we built

The same calculation runs as a platform now. A parsing engine handles the painful part at one million cells every 2.5 seconds, and deploys ship from a pipeline instead of a checklist.

Led architecture inside a 9-person team to turn the manual calculation into a web platform around manufacturing cost and resource data.

Built a CSV parsing and validation engine that processes around one million cells every 2.5 seconds with type checks, external validators, corruption detection, and template rules.

Automated server provisioning and deploys with Ansible and GitHub Actions so releases stopped depending on manual setup.

Controls

Every change has an owner, a fallback, and an audit trail.

Layered validation (type, external lookup, corruption, template) before any row enters the model

Deterministic deploy pipeline with rollback and reproducible infrastructure

Monitoring that held reported uptime at 99.999% across the platform

No public disclosure of customer files, supplier data, or proprietary calculation logic

the result

80%

cost cut on carbon-neutral advisory work

~1M

cells parsed and validated every 2.5 seconds

~75%

fewer complaints about speed and data templates

~87%

shorter server setup time after automation

related services

The work behind the result.

Workflow automation

Turn repeated handoffs, approvals, reports, and admin work into workflow automation your team can trust.

Internal tools

Build internal tools, admin panels, dashboards, portals, and approval queues around the way your team works.

System integration

Connect CRMs, ERPs, payment systems, files, databases, APIs, and spreadsheets without forcing a platform migration.

book a workflow audit

Before you automate anything, find the workflow worth fixing.

A short call is the fastest way to figure out whether you need AI automation, custom software, integrations, or simply a clearer process.

workflow audit call

30 min

Bring one repeated process: a report, quote, approval, inbox, or handoff that keeps wasting time. We decide together whether it needs AI, software, integration, or just a cleaner process. No pitch.

or send details instead

We talk through one messy workflow

You describe where work starts, who touches it, what tools are involved, and where things slow down.

We decide if automation is even the right answer

Some problems need AI. Some need better process, clearer ownership, or a small internal tool. We separate them.

You leave with a practical next step

If there is a real opportunity, we outline the smallest useful build. If not, you avoid automating the wrong thing.