Flagship Case Study
Byg-LCA-Let: from workflow pain to launched niche SaaS
Byg-LCA-Let started as a practical way to reduce LCAbyg/JSON data-entry pain and matured into a broader BR18 climate documentation product with report workflow, pricing, support pages, and product trust signals.
Open live product- Product state
- Live SaaS
- Workflow
- BR18 PDF reporting
- Pricing
- 499 kr. per report
Public pricing page.
- Live public product at byg-lca-let.dk.
- Public pages show BR18 positioning, support pages, pricing, and report workflow.
- Behind-the-scenes product maturity work is summarized through the public case study and visible product surface.
- LinkedIn launch post documents the original LCAbyg/JSON wedge.
Problem
Small construction teams and consultants face documentation work that is too manual for the scale of many projects.
The original pain was not a missing climate calculation engine. It was the data work around preparing useful LCAbyg input and turning that work into a clean documentation flow.
Product
The public product now presents a broader BR18 report workflow: setup, construction templates, project work, payment, and official PDF-oriented documentation.
That evolution matters because it shows product judgment: the first wedge was a narrow workflow helper, but the durable product is the complete job users actually need done.
Architecture
The product is treated as a workflow system rather than a marketing site: structured project data, templates, verification paths, pricing, support content, and handoff-ready outputs.
The public case study frames a maturity arc across payments, SEO, trust content, UX, product images, analytics, and AI/developer-agent setup.
What I Shipped
A live product surface, public pricing, support pages, a sharper landing experience, workflow copy, report positioning, and the operational pieces needed to make a small SaaS credible.
The implementation avoids fake scale claims. The proof is the shipped product, the visible workflow, and the product maturity work behind it.
AI-Assisted Delivery
AI was used as a delivery accelerator around senior engineering judgment: project instructions, review artifacts, faster iteration, type-checking, test loops, and tighter product copy.
The important point is not automation for its own sake. It is a disciplined way to raise throughput while keeping ownership of architecture, correctness, and product decisions.
What It Proves
I can find a narrow operational problem, turn it into a product, ship the product surface, and keep pushing it toward commercial credibility.
It also proves range: domain understanding, data modeling, UX, SaaS operations, AI-assisted engineering, and launch execution.
