7 systems — products, our own internal tools, and client work. Each one explains, in plain language, the problem it solves and what it actually does.
We do not name clients, and we do not publish outcome percentages we cannot evidence. Every figure below is checkable against something real.
01
PropDash AI
Real estate CRMLive demo2026Why it was built
Property enquiries arrive faster than an agency can qualify them, and the ones that go unanswered for an hour are usually gone. Agencies were tracking them in spreadsheets nobody trusted.
Evidence on record
Live demo you can actually click through — real AI answering, not canned responses
Role
Designed and built end to end, AI included
Stack
Next.jsTypeScriptVercelNVIDIA NIM
Delivered
- —Every enquiry scored automatically, so agents call the hottest leads first
- —A WhatsApp assistant that asks buyers the qualifying questions for you
- —Maps showing which localities and budgets your demand is coming from
- —Built so adding a second agency is a signup form, not a rebuild
View it live →02
Lead Hunter
Finds your next customersInternal product2026Why it was built
Buying a lead list gives you names, not customers. We needed a system that finds businesses that actually match what we sell, digs up a working phone number and email for each, and skips everyone we already know.
Evidence on record
Checks 12 different sources every night, automatically
Role
We built and run it for our own sales
Stack
PythonApifyNotion APIPM2
Delivered
- —Searches 12 sources — directories, maps, industry sites — while you sleep
- —Finds and verifies real contact details for each business
- —Scores every lead so you know who to call first
- —New leads filed into your CRM every morning, no typing
03
Content Engine
Writes and posts for usInternal product2026Why it was built
AI that writes your marketing will happily publish something wrong — an invented number, a claim you never made. The hard part is not the writing, it is the checking. So we built the checking.
Evidence on record
5 separate AI checks before anything is published, running on our own LinkedIn today
Role
It writes and publishes our own LinkedIn content
Stack
PythonNVIDIA NIMOllamaLinkedIn API
Delivered
- —One AI writes, a different AI criticises — no AI grades its own homework
- —A fact-checker that blocks any claim it cannot verify, run twice before posting
- —Confidential material stripped out before any AI ever sees it
- —Posts on schedule without anyone touching it
04
The Watchdog
Catches silent failuresInternal product2026Why it was built
The scary thing about automation is not that it breaks — it is that it can stop quietly and nobody notices. Ours once stopped for six days while every monitor said "healthy". So we built the watchdog that actually notices.
Evidence on record
Watches 9 of our own systems, checking every 5 minutes
Role
It guards every automation we run
Stack
PythonPM2Telegram API
Delivered
- —Notices within minutes when a job that should have run, didn't — and runs it
- —Catches jobs that are stuck, not just jobs that are dead
- —Never runs the same job twice by mistake
- —Knows customer-facing work must never fire at 3 AM
05
Automation Rescue
Fixed a broken automationClient engagement2026Why it was built
A client's automation ran every day and quietly produced wrong results. Nobody could say which step was broken, so nobody could say what a fix should cost.
Evidence on record
6 separate problems found and written up, with two priced ways to fix them
Role
Found what was wrong, priced both ways to fix it
Stack
n8nWebhooksGoogle Sheets
Delivered
- —Walked through the whole automation, step by step
- —Every problem documented in plain language: what breaks, and what it costs you
- —Two honest options: patch what exists, or rebuild it with proper alerts
- —A written report the client could act on with us or without us
06
MediGuide
Healthcare, built for IndiaPrototype2026Why it was built
Type a symptom into most health tools and you get American medicine brands that mean nothing at an Indian pharmacy counter.
Evidence on record
Built on Indian medicine brand names, not a US drug database
Role
Built the working prototype end to end
Stack
PythonRxNormNVIDIA NIMEdge TTS
Delivered
- —Describe the symptom, get the medicine
- —Answers use brands actually stocked at Indian pharmacies
- —A narrated demo video produced for evaluation
07
Trading Systems
Where we learned honestyInternal product2026Why it was built
A trading bot that looks profitable on paper usually is not once real costs are counted. We built the measurement honest enough to tell us our own system was losing — and it did.
Evidence on record
595 real trades logged and measured, every cost counted
Role
Built the system, then built the proof it wasn't working
Stack
PythonSQLitescikit-learnWebSockets
Delivered
- —Trades measured against live market data, not a flattering backtest
- —An AI model that cannot beat a coin flip gets ignored, not trusted
- —Fees and costs counted properly — which flipped 'profitable' to 'not yet'
- —The discipline from this is in every AI system we build for clients
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