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.
Questions about this build
Because the plasterer is on the tools. He is not going to log into a CMS, fill in a form, upload images, and write a description after a 9-hour job. Telegram is where he is already messaging — taking photos and sending them takes 30 seconds. The bot picks up from there. The choice of channel is the whole point; a Make.com webhook or a manual upload form would have killed the workflow before it started.
GPT-4 Vision reads the uploaded photos to extract scope (kitchen / bathroom / lounge), surface conditions (cracks / damp / damage type), and finish quality. Combined with the plasterer's brief Telegram caption, GPT-4 Turbo drafts the full case study — title with location and technique keywords, narrative description, scope, materials used (e.g. British Gypsum MultiFinish), and the work approach. Everything lands in a "pending review" state on the website until the operator approves it.
Occasionally. That is why the pending-review workflow exists. The operator scans the AI draft, fixes any specifics (e.g. wrong material name), adds a personal note if relevant, and publishes. Total review time per case study: about 5 minutes. Without the AI, drafting a comparable case study from scratch is 30–60 minutes per job. With the AI, the bottleneck moves from drafting to review.
About £0.10 to £0.30 per case study in AI usage (GPT-4 Vision and GPT-4 Turbo combined). Hosting is free at this scale (Supabase free tier covers the database, storage, and Edge Functions; Netlify free tier covers the website). Total ongoing cost: lunch money. The bespoke build itself is the only investment.
Still have a question? Book a discovery call — direct line to me, Dean.
Other industries I've built for
Different operations, same engineering discipline.
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