Software Engineer · Founder, Northwind Labs

Manjit Singh

I build production software and mathematical models (from a tamper-evident evidence app for renters to local LLM deployments and market-analysis engines) for people and businesses that need results they can verify.

Computer Science & Mathematics Northwind Labs Ltd London, UK

01 Profile

The foundation

Joint-honours in Computer Science and Mathematics. My work sits where provable maths meets shippable software.

I design and ship production systems end to end: architecture, security, data protection, and deployment. I care about systems that are fast, correct, and measurable, and about explaining what they do in plain terms to the people paying for them.

Discipline
Full-stack engineering
Company
Northwind Labs Ltd
Base
London, UK
Focus
Local LLM · Optimisation · Analytics
02 Services

What I do for businesses

Three problems I solve, described without jargon.

On-prem · RTX 5080

Local LLM deployment

Private AI that never leaves your building. I stand up local language models on high-end GPU hardware so you can analyse sensitive data (customer records, pricing, contracts) without sending any of it to a third-party cloud.

Dealership analytics

Inventory value intelligence

Find the profit already sitting on your forecourt. I score your stock against live market data to flag undervalued vehicles and high-margin specifications before they’re mispriced.

Operations research

Optimisation modelling

Scheduling, routing, and resource allocation solved as maths, not guesswork. Custom models that cut cost and cut waste.

03 Work

Selected work

Two products, shipped end to end.

TenantProof

Live in production

A mobile-first web app that helps private renters in England build a defensible, time-stamped evidence trail (condition photos and repair logs) and export it as a court- and adjudicator-ready PDF.

The Renters’ Rights Act 2025 passed, but its new Ombudsman isn’t expected until roughly 2028, leaving a multi-year gap where tenants have no simple way to document issues and escalate correctly. Free core tool, plus a £12 one-off “Dispute Pack.” Built solo, end to end: product, architecture, compliance design, deployment, and ad operations. Operated as Northwind Labs Ltd, an ICO-registered data controller.

Stack

  • Next.js 16
  • React 19
  • TypeScript
  • Tailwind v4
  • Supabase
  • Stripe
  • @react-pdf/renderer
  • Playwright
  • ffmpeg
  • Resend
  • Vercel

Engineering highlights

Tamper-evident evidence chain
Photos are captured live in-browser via getUserMedia and fingerprinted client-side with a SHA-256 hash. The capture method and hash are printed into the exported PDF, so a third party can verify a file hasn’t been altered since capture.
Security & data protection by construction
Postgres Row-Level Security on every table (a user can never read another’s rows), plus full UK GDPR export and account-deletion flows, data minimisation, and PII masking in session replay.
Consent-compliant analytics
A four-provider stack behind a consent gate, implementing Google Consent Mode v2: analytics load cookielessly denied-by-default so conversions still model, upgrade on accept, and hard-kill on reject, with session replay and the Meta Pixel fully gated.
Programmatic ad-creative pipeline
A Playwright + ffmpeg renderer turns parameterised HTML/CSS into 97 production ad assets across six formats. Root-caused a capture race that opened every clip on ~2s of blank white, and trimmed exactly the measured lead-in with ffmpeg -ss.
Out-of-home attribution
A /r/<slug> redirect routes billboard QR scans through UTM tagging into GA4, making physical out-of-home advertising measurable (normally a black box).
Regulatory-constrained design
Explicitly not legal advice: deterministic template letters with no runtime LLM, persistent disclaimers, and mandatory signposting to Shelter, Citizens Advice and GOV.UK.

By the numbers

Migrations
8 · RLS enforced on every table
Ad assets
97 rendered across 6 formats
Analytics
4 providers · Consent Mode v2
Compliance audit
12 ad variants · 25-agent pipeline

Visit tenantproof.co.uk

Car Arbitrage Engine

Shipped · v4.1

A production Python engine that watches used-car markets for price dislocations, verifies each candidate against DVLA records and multi-round vision AI, and surfaces only the vehicles worth acting on.

Stack
Python · asyncio
Data
DVLA VES integration
Vision
Three-round AI consensus
Pricing
Live median market analysis

View my GitHub

04 Stack

Compute & stack

The hardware behind the on-premises pitch, stated plainly.

Hardware

GPU
NVIDIA RTX 5080
Role
Local inference & fine-tuning
Deployment
On-premises, air-gapped capable

Toolchain

Language
Python
Concurrency
asyncio
Vision
Multi-round consensus
Data
DVLA VES, market feeds
05 Contact

Ready to build?

If you have a problem that looks like maths, or data you can’t send to the cloud, I’d like to hear about it.

Start a consultation

London, UK Remote or on-site