Community trust infrastructure
CircleWorks
Trust infrastructure for the informal local economy inside residential societies — workers build a reputation that travels, residents find reliable help.
The problem
The informal labour market inside residential societies — plumbers, electricians, cleaners — runs entirely on word-of-mouth: no trust signals, no transparent pricing, no accountability. Residents cannot find reliable workers; good workers cannot build a reputation that travels.
How it works
- A resident posts a job by typing or speaking — in English, Hindi, Kannada, or Marathi, using browser-native speech recognition. A natural-language parser extracts the structured details — skill, duration, headcount.
- An LLM pricing engine (Claude) suggests a fair rate range from category, location, and historical rates.
- Matched workers receive Telegram alerts, accept with one tap, complete the work, and build a verifiable trust score from completions and reviews.
- All payments are peer-to-peer via UPI — the platform never touches money.
- Built on Django 5.1 + PostgreSQL on Railway, with a Telegram bot for worker onboarding and notifications, plus full SOPs, job-lifecycle, and dispute-resolution design authored with an on-ground partner.
Architecture
Resident posts a job
type or speak · 4 languages
AI pricing & matching (Claude)
Worker
One-tap accept
Complete work
Verifiable trust score
Payment direct, peer-to-peer (UPI)
the platform never touches money
Backend
Resident posts (type or speak, 4 languages) → parsed & priced → workers matched via Telegram → trust score; money flows P2P, never through the platform.
The journey
Designed for how informal work actually happens in residential societies — no formal contracts, no app-to-app coordination. Word-of-mouth trust, now with a structure underneath it.
The resident types or speaks what they need — in their own language
They open the app and describe the job exactly as they'd tell a neighbour — "Need someone to water the plants this Saturday" — by typing or by voice, in English, Hindi, Kannada, or Marathi. The system reads it, extracts the structure, and estimates a fair price, all before they confirm anything.
Plain English in — AI reads, extracts, and prices before you confirm
The LLM pulls job structure from the description automatically
Skill, date, duration, headcount — extracted without a dropdown or a form field. The resident reviews what the AI understood, corrects anything if needed, and confirms. They never touched a category picker.
LLM extracts skill, date, duration, headcount — no form filled
Claude sets a specific, justified price
Not a range to negotiate down from. A concrete number — ₹200 — derived from job category, duration, and local market rates for that society. Residents don't haggle. Workers know what to expect before they apply.
A specific price, not a range — derived from category and market rates
The right workers in the society get a Telegram alert — no new app to install
Workers matched by skill and availability receive a job notification in the app they already have open. The platform never pushed them to download anything new. One tap to see the details, another to apply.
Job alert lands in Telegram — apply without leaving the chat
Full schedule management via bot commands — /online, /offline, /jobs
ID-verified workers are browseable — skills and availability visible before you hire
The Pros directory shows only ID-claimed professionals from your society. The resident can see who they're dealing with — skill tags, availability windows, tenure — before committing to anyone.
Verified pros from your society — ID on file, skills tagged, available to hire
Highlights
- Voice + text in 4 languages
- Natural-language job parsing
- LLM pricing engine
- Telegram-native
- Peer-to-peer payments
Stack
Why it matters
A marketplace designed for trust, not transactions — with the operational design (SOPs, disputes, field ops) that a real pilot needs, not just the code.