TS Plastering: a Telegram-to-website pipeline that publishes case studies from photos
A bespoke website plus a Telegram-to-website pipeline. The plasterer sends a few photos and a one-line description after each job; GPT-4 Vision reads the images, GPT-4 Turbo drafts the case study, and the website queues it for one-click publish. Drafting effort per case study drops from 30–60 minutes to 5 minutes of operator review. Cost per case study: lunch money.
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.
By the numbers
The operational economics of running a Telegram-to-website AI case-study pipeline for a single-team trade business.
Services used in this build
The capability and service pages this engagement delivered against. Use them to scope a similar build for your operation.
Custom AI Systems →
GPT-4 Vision reads on-site photos and extracts scope, materials, and finish quality.
AI Apps & Websites →
The bespoke trades website that the AI pipeline publishes case studies into.
Document Automation (use case) →
Photos → structured case study, written and published in ~5 minutes at £0.10–£0.30 per published item.
AI Workflow Automation →
Telegram-to-website pipeline running end-to-end with operator approval inside the loop.
Other industries I've built for
Different operations, same engineering discipline.
Want a build like this for your operation?
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