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ConsumerSuit — UK Legal-Tech AI Platform

"Stand up for yourself, without the £200-an-hour bill."

Live site: consumer-suit.uk
Portfolio demo: hrushimanju.github.io/ConsumerSuit (update after Pages setup)
Built: May – June 2026 · **Status: In active development — pre-launch


What it is

ConsumerSuit is a consumer-facing UK legal self-help platform. Users describe a dispute — employment, housing, consumer rights, data protection, travel — and the platform:

  1. Analyses the dispute against applicable UK law (EqA 2010, ERA 1996, CRA 2015, GDPR, etc.)
  2. Maps their evidence — STRONG / WEAK / MISSING — against what a claim needs
  3. Builds a draft claim document, complaint letter, or DSAR

The product covers 28 civil law areas, verified against legislation.gov.uk, with compensation figures current to 6 April 2026 (new tribunal caps, Vento bands, Ofcom limits).

Two halves:

  • The app (/) — AI-powered 3-phase flow with subscription tiers (£9 / £29 / £79 / £149)
  • The wedges (/flight.html, /parking.html, /council.html, /section75.html) — focused, zero-AI-cost checkers with lead capture

My role

I am the founder and product lead. I did not write the code — that was my AI co-builder. What I did:

My contribution Impact
Vision and product definition — what it is, who it's for, what it costs Shaped the entire product
QA leadership — 5-case accuracy harness; found critical gaps; directed fixes Correct statutory sections, real settlement figures
Security instinct — caught AI prompts exposed in browser DevTools Protected the core IP before launch
Technology decisions — trialled NVIDIA NIM; rejected at 67s; reverted Kept UX acceptable
Pricing strategy — pushed from £3–£29 to £9–£149 Proper commercial signal
Brand and domain — registered consumer-suit.uk; named 360 Three Sixty Product identity separate from personal identity
DocumentationPROJECT_LOG.md + FOUNDER_JOURNAL.md throughout Decisions recorded with rationale

Technical stack

User → Cloudflare Pages + Edge Functions (consumer-suit.uk)
         ├── D1 SQLite          — users, sessions, subscriptions
         ├── KV store           — leads, feedback, funnel metrics
         ├── Stripe             — checkout, webhooks, customer portal
         ├── Google OAuth       — sign-in, session tokens
         └── AI fallback chain  — Anthropic → Mistral → Groq (Premium)
                                — 1,000/day circuit breaker
                                — Server-side prompts (never client-exposed)

Legal knowledge layer (server-side only):
Compressed system prompt (~5.5k tokens) · EqA 2010 section discipline · NMW floor check ·
Vento bands (2026) · DSAR Art.82 · CCA s.75 · Limitation period warnings · Settlement aggregation


Engineering transferable skills

This project maps directly to engineering competencies:

Skill What I did Engineering equivalent
V&V 5-case accuracy harness; found statutory section errors and settlement undercount; directed fixes; re-run confirmed improvements Test design, defect tracking, corrective action
Systems architecture Multi-provider fallback chain with rate limiting + circuit breakers Redundant system design, FMEA, reliability
Risk assessment Found IP exposure vulnerability; led remediation before launch Hazard identification, FMEA, risk closure
Requirements engineering Translated complex legal domain into precise technical specs SRS, functional decomposition, traceability
Performance engineering Benchmarked NVIDIA at 67s; quantitative rejection; rationale logged Load testing, technology evaluation
End-to-end delivery 0 → test-deployed product in 16 days Sprint execution, milestone management
Technical documentation PROJECT_LOG + FOUNDER_JOURNAL with decisions and rationale Design records, lessons-learned, engineering notebooks

Key decisions (engineering rationale)

Prompt compression (–16%, 6,500 → 5,500 tokens)
Reduced AI cost per call while maintaining a 55-point substance check. Tradeoff: precision over verbosity.

NVIDIA NIM rejection
Trialled as primary provider. Benchmarked at 67 seconds response time against <5s target. Rejected on quantitative grounds. Clean revert with decision logged. Lesson: "more powerful" ≠ better if the UX is unusable.

Server-side prompts
Original build exposed AI prompts client-side (visible in browser DevTools). This is the product's core IP. Moved fully server-side before public launch.

Pricing: £9 / £29 / £79 / £149 (revised from £3 / £7 / £15 / £29)
Original pricing signalled "cheap tool". The target user is someone with a real dispute — a solicitor costs £200/hour. At £29/month you're still cheap by comparison and the price communicates that the output is serious.


Accuracy approach

The legal accuracy framework is the product's core asset:

  • QA scorecard: 5 cases (employment, housing, broadband, used car, CCA s.75) with scoring across 8 dimensions
  • Defects found: wrong EqA sections; NMW not checked; settlement not aggregated across all heads; sector regulatory layer missing
  • Fix process: ranked fix list → prompt update → full re-run → all 4 cases pass
  • Ongoing: weekly robot cross-checks all 28 law summaries against legislation.gov.uk; rewrites only when source has changed
  • Compensation figures: updated to 6 April 2026 — tribunal weekly pay cap (£751), compensatory cap (£123,543), Vento bands, Ofcom limits

Regulatory position

Confirmed sound (13 June 2026 board review):

  • Outside reserved legal activities under the Legal Services Act 2007
  • Not an FCA-regulated claims management company (no taking of fees for claiming)
  • AI output carries score/settlement caveats and one-time acknowledgement gate
  • Document-draft banner distinguishes tool output from legal advice

Project files

File Purpose
PROJECT_LOG.md What was built, when, and current status
FOUNDER_JOURNAL.md My personal record — decisions, ideas, rationale
demo/index.html Standalone portfolio demo (GitHub Pages)

Author

Hrushikesh Manjunath Thotamsetty
AMIMechE #80821391 · CEng pathway · MSc Mechanical Engineering, University of Liverpool
manjunathhrushikesh@gmail.com · consumer-suit.uk


This repository is a portfolio case study. The production codebase is deployed via Cloudflare Pages from a private repository.

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