TS Plastering: a Telegram-to-website pipeline that publishes case studies from photos

The bottleneck

TS Plastering is a one-team plastering and rendering business. The work is good, the photos prove it, and case studies on the website would compound into a portfolio that wins jobs. The problem: nobody on the team has the time, the writing inclination, or the CMS familiarity to turn finished jobs into published case studies.

The standard route — log into a CMS, upload images, write a description, format the layout — never happens after a 9-hour day. The result was a website that looked thin even when the business was actually busy.

What I built

Meet the operator where he already works. He uses Telegram all day to send photos to the office. So the case-study pipeline starts in Telegram.

• On-site. The plasterer snaps a few photos of the finished job. Captions them with a one-line description ("Kitchen replaster, Fleetwood, skim coat finish on damp wall"). Sends them to the bot. Total time: about 30 seconds.
• AI processing. The Telegram bot triggers a Supabase Edge Function. GPT-4 Vision reads the photos to extract job scope, surface conditions, and finish quality. GPT-4 Turbo composes the case study — title with location and technique, full narrative, structured "work completed" (challenges → approach → result), and a materials list. Total AI time: around 60 seconds.
• Pending review. The draft lands in the website's admin queue with a "pending review" status. The operator (Dean or the business owner) reviews it in about 5 minutes, edits anything the AI got wrong, and publishes.
• SEO-aware. AI-generated content is keyword-conscious by design — location, finish, technique, and materials all appear naturally in titles and descriptions. Each case study compounds into the site's SEO surface.

Built bespoke on React + Supabase + Telegram, with OpenAI for the vision and language calls. Hosting runs on the Supabase + Netlify free tiers — total ongoing infrastructure cost: zero.

What changed

• Case-study publication time drops from "never happens" or 30–60 minutes per job to ~5 minutes of operator review.
• Cost per case study: £0.10–£0.30 in AI usage. Lunch money.
• SEO surface compounds with every job — each case study is a fresh, indexable page with location and technique keywords already present.
• Workflow respects the operator. The plasterer never opens a CMS. He uses Telegram. The system meets him there.