Predict Loss.
Protect Margin. Precision, Not Panic.

ShrinkGuard Twin AI is an AI-powered SaaS platform that builds a digital twin of your retail store, calculates margin-at-risk by product, zone and time window, and ranks every prevention action by its financial return so every security decision is backed by real financial intelligence, not guesswork.

ShrinkGuard Twin AI · Live Engine

Margin-at-Risk Intelligence Dashboard

LIVE
PZ
Product–Zone–Time Risk Score HIGH · Zone C · 5–8pm ⚠
MAR
Margin-at-Risk (Fri Evening) £3,200 Exposed ↑
ROI
Top Intervention ROI Reposition 6 SKUs ✓
LT
Layout Risk Score (Blind Spot) High · Rear Aisle
529K
Shoplifting Offences England & Wales 2025
ShrinkGuard Twin AI · Predictive Defence · Active
The UK Retail Shrinkage Crisis

UK Retailers Are Losing Billions to Shrinkage
While Spending Blind on Prevention.

UK shoplifting offences hit a record high since 2003. Retailers spend £1.2 billion a year on prevention a 65% increase yet stock keeps walking out. The problem isn't motivation or budget. It's the absence of a financially intelligent system that tells retailers exactly where their margin is at risk and which action delivers the highest return. ShrinkGuard Twin AI solves this.

£4.2bn
Total annual UK retail crime cost including prevention spend a market-ready budget with no intelligent allocation system
529,994
Shoplifting offences in England & Wales to June 2025 the highest since records began in 2003
£1.2bn
UK retailers' crime prevention spend 2022/23 a 65% rise in one year with no intelligent ROI system
304K+
SME retail businesses in the UK with no dedicated loss prevention analyst the entire target market
Six Patent-Pending Innovations

Six Innovations. One Complete Shrinkage Intelligence Platform.

ShrinkGuard Twin AI combines a Margin-Weighted Risk Graph, Store Layout Digital Twin, Shrinkage Opportunity Score, Margin-at-Risk Engine, Intervention ROI Optimiser, and Selective Protection Recommender into one unified SaaS no hardware needed, no specialist LP team required.

Retail store shelves risk scoring
Product–Zone–Time Risk Graph
Element A · Risk Graph

Margin-Weighted Product–Zone–Time Shrinkage Risk Graph

Dynamic 3D risk scoring across product, store zone, and trading time window simultaneously weighing 6 factors including gross margin, exit proximity, and staff visibility.

A premium jacket near the exit on a Friday evening rush carries vastly different financial risk from the same item at the till on Monday morning. ShrinkGuard Twin AI scores this in real time. No other retail LP or analytics product models this three-dimensional risk relationship. Built on Neo4j graph database for millisecond traversal of complex risk relationships.
Retail store floor plan layout
Store Layout Digital Twin
Element B · Digital Twin

Store Layout-to-Margin Leakage Digital Twin

A virtual replica of your physical store where every zone, blind spot, fitting room, exit and shelf position carries a financial risk weight not a footfall tool, a margin-leakage model.

Built with PostGIS spatial geometry and NetworkX graph relationships. Measures structural theft opportunity even without incident history critical for new stores. Draw your map in the browser in under 30 minutes. The system scores layout vulnerability from day one.
Financial analytics and margin exposure
Margin-at-Risk Engine
Element D · MAR Engine

Margin-at-Risk (MAR) Engine

Restructures shrinkage as a probability-weighted financial exposure telling retailers exactly how much gross margin is at risk by product, zone, and time period.

Powered by XGBoost gradient boosting integrating theft probability, gross margin, zone risk weights, and time multipliers. Monte Carlo simulation provides a confidence range. Output: "£4,800 margin-at-risk between 4–8pm Friday, Zone C." A financially actionable number not a vague risk rating.
Why ShrinkGuard Twin AI

Built for Precision Retail Protection.
Not Just Another Security Dashboard.

Predict Loss Before It Happens

By product, zone, and time window, ShrinkGuard Twin AI predicts theft potential and enables retailers to act before a loss is recorded shifting from reactive security to proactive margin protection.

Turn Shrinkage Into a Financial Number

The platform doesn't flag products as "high risk" it tells retailers exactly how much gross margin is at stake, zone by zone, time period by time period, product category by product category.

Rank Interventions by Return, Not Guesswork

Every prevention option tagging, staff repositioning, layout change, CCTV prioritisation is ranked by its expected financial return before a penny is spent. Precision over coverage.

Identify Layout Risk on Day One

Even for brand-new stores with zero theft history, the digital twin scores layout vulnerabilities blind spots, exit proximity, fitting room access before any incident can occur.

No Hardware. CSV Upload. Live in Hours.

Enterprise LP platforms require RFID infrastructure, large budgets, and specialist teams. ShrinkGuard Twin AI starts from a stock CSV, sales data, and a simple browser store map.

Formally Confirmed Novel Patent Pending

A formal novelty search across global patent databases confirmed all six elements are novel. UK patent application filed. PCT international application in progress. No direct competitor exists.

About Us

Built by Someone Who Saw the Problem First-Hand.
Designed to Protect SME Retail Margins.

ShrinkGuard Twin AI is a UK-based B2B SaaS venture founded by Hassan Asghar combining academic rigour in international business with direct observation of how smaller retailers react to shrinkage: reactively, inconsistently, and without any intelligent link between store layout, product placement, and financial loss.

Core Values

What Drives Everything We Build

Financial Precision

Every recommendation is grounded in probability-weighted gross margin exposure not vague risk scores. Retailers deserve commercially actionable intelligence, not black-box alerts.

SME-First Design

Helping independent and mid-market retailers protect the margins that larger enterprise platforms have always ignored software-first, affordable, and deployable without a specialist LP team.

Simplicity Without Compromise

Powerful shrinkage intelligence delivered from a CSV upload and a simple store map. Retailers gain live risk outputs within hours no hardware, no IT project, no waiting.

IP-Protected Innovation

Six novel elements confirmed by formal patent search, UK patent application filed, PCT international filing in progress. The methodology is owned, protected, and defensible.

Avoid Over-Security

Over-investing in blanket security locks products, slows sales, and frustrates customers. ShrinkGuard Twin AI enables precision protection tag only what needs tagging, guard only where it pays.

UK-Born, Globally Scalable

Built from the ground up for UK SME retailers. Designed from the start to scale into Ireland, Australia, Canada, and Western Europe as retail crime pressures grow internationally.

Founder

The Person Behind ShrinkGuard Twin AI

HA

Hassan Asghar

Founder & CEO

Hassan Asghar brings the commercial and analytical foundation required to take ShrinkGuard Twin AI from concept to a market-ready platform. His MSc in International Business from Ulster University (2024) and MBA from Mirpur University of Science and Technology (2020) equipped him with expertise in market analysis, business model design, financial planning, and operational strategy all directly applied in the margin-at-risk methodology, the intervention ROI framework, and the platform's commercial roadmap.

He formulated the complete product concept, defined all six patent-pending innovations, commissioned the formal novelty search confirming global novelty, and built the full technical architecture of the platform across all ten modules. He directly observed how SME retailers experience shrinkage reactively, without predictive tools, and without any connection between store layout and financial exposure and designed ShrinkGuard Twin AI to fill exactly that gap. Hassan leads product direction, customer development, commercial strategy, and all UK market execution.

Full Platform

Six Patent-Pending Innovations.
One Predictive Shrinkage Command Centre.

Every feature is built specifically for UK SME retailers not adapted from enterprise tools. Our six novel elements integrate into a decision-intelligence system that delivers financial clarity over shrinkage for the first time in the SME market.

Retail store risk zone analysis
Element A · Product–Zone–Time Risk Graph
Element A · Risk Graph

Margin-Weighted Product–Zone–Time Shrinkage Risk Graph

Dynamic 3D risk calculation across product, zone, and time window simultaneously weighing product value, gross margin, store zone, exit proximity, staff visibility, and time-of-day patterns.

Built in Neo4j graph database. This is the core innovation. No other retail LP product models risk in three dimensions. A premium trainer on the back wall during a Friday rush carries vastly different financial risk from the same item at checkout on Monday morning. ShrinkGuard Twin AI scores this dynamically.
Store layout digital twin mapping
Element B · Store Layout Digital Twin
Element B · Digital Twin

Store Layout-to-Margin Leakage Digital Twin

Every structural element entrance, exit, fitting room, blind spot, rear aisle, checkout becomes a risk node carrying a financial exposure weight. Not a design twin. A margin-leakage model.

Built with PostGIS spatial modelling and NetworkX graph relationships. Scores layout vulnerability even before any theft incident occurs essential for new store openings. Draw your map in the browser in under 30 minutes. No CAD files, no specialist software needed.
Retail zone heatmap scoring
Element C · Shrinkage Opportunity Score
Element C · Opportunity Score

Layout-Derived Shrinkage Opportunity Score

Measures structural theft opportunity independent of incident history using exit proximity, staff visibility gaps, concealment potential, shelf height, and distance from staffed areas.

A display can score high purely from its structural location even if theft has never been recorded there. This provides accurate intelligence for SMEs with sparse or missing historical data. Confidence ranges produced by Monte Carlo simulation reflect data quality explicitly.
Financial margin exposure dashboard
Element D · Margin-at-Risk Engine
Element D · MAR Engine

Margin-at-Risk (MAR) Engine

Integrates theft probability, gross margin, stock volume, zone risk weights, and time multipliers into a probability-weighted financial exposure score. A financial tool, not a risk alert.

Powered by XGBoost gradient boosting. Facebook Prophet handles seasonal patterns school holiday periods, promotional uplifts, payday cycles. Monte Carlo simulation outputs a confidence range: "£2,800–£4,100 across 10,000 scenarios." Not a false-precision single number. Commercially actionable intelligence.
ROI analysis and intervention ranking
Element E · Intervention ROI Optimiser
Element E · ROI Optimiser

Intervention-Level ROI Simulation

Frames prevention as a constrained optimisation problem: given available interventions, costs, and budgets find the mix that maximises protected margin. Powered by PuLP linear programming.

The system might find it more cost-effective to reposition 8 high-exposure SKUs toward checkout than to tag an entire product category. Recommendations are store-specific, financially ranked, and account for operational burden and customer experience friction not generic security advice.
Smart retail precision protection
Element F · Selective Protection Recommender
Element F · Selective Protection

Selective Protection Recommendation System

Breaks away from blanket security. Calculates exactly which products, zones, and time windows justify protection spend based on predicted margin exposure.

Instead of locking all cabinets or tagging all categories: "Tag these 12 SKUs. Staff Zone B from 5–7pm Saturday. Reposition this display closer to checkout." Precision over coverage. Maximise financial return on every prevention decision while minimising customer friction and unnecessary operational cost.
Competitor Analysis

How ShrinkGuard Twin AI Compares

No other platform integrates a predictive store layout digital twin, Product–Zone–Time risk modelling, margin-at-risk calculation, and ROI-ranked intervention selection in one place, accessible to SME retailers. Formally confirmed novel by patent search.

Capability Auror Sensormatic Checkpoint Lightspeed/Vend Zebra Analytics ShrinkGuard Twin AI
Predictive shrinkage digital twin✗✗✗✗✗✓ Patent-Pending
Product–Zone–Time risk modelling✗✗✗✗✗✓ 3D Risk Graph
Margin-at-Risk engine✗✗✗✗✗✓ XGBoost MAR
Intervention ROI optimiser✗✗✗✗Partial✓ PuLP Optimiser
Layout-derived opportunity scoring✗✗✗✗✗✓ PostGIS Spatial
Software-first, no hardware✓✗ Hardware✗ Hardware✓✓✓ CSV Onboarding
SME-accessible pricing✗ Enterprise✗ £10K–£100K+✗ 5–6 figures✓✗✓ From £99/mo
Formally confirmed novel by patent search✗✗✗✗✗✓ Confirmed Novel
Market Analysis

A £4.2 Billion Problem with No Intelligent Solution.
ShrinkGuard Twin AI Fills the Gap.

The UK retail sector reached £114.7 billion in economic output in 2024 4.4% of total UK GDP. With 304,560 retail businesses, the majority being SMEs with no dedicated loss-prevention analyst, and shoplifting at a 20-year high, ShrinkGuard Twin AI targets a massive, underserved segment with no direct competitor in the market.

Market Data & Intelligence

The Numbers Behind the Opportunity

UK SME Retail Market by Sector (Primary Target Segments)

Estimated SME retail businesses across ShrinkGuard's primary target sectors

UK Retail Crime Cost Distribution 2023/24 £4.2bn Total

Annual cost breakdown: direct theft losses, security investment, insurance premiums

ShrinkGuard Twin AI vs Competitors Capability Radar

Shrinkage intelligence capabilities across key dimensions vs existing market alternatives

UK Shoplifting Offences 2020–2025 (England & Wales)

Recorded shoplifting offences now at the highest level since records began in 2003

Pilot Survey: Retailer Demand for ShrinkGuard Features (n=35 SME Retail Professionals)

Feature interest % from 35 independent & SME retail professionals across London, Manchester and Birmingham

Financial Projections

3-Year Revenue Trajectory

£81.8K
Year 1 Revenue · ~66 active paying customers · First revenue Month 4 · Partial commercial year · £50K founder equity launch
£389.7K
Year 2 Revenue · ~173 customers · First full commercial year · £49,421 net profit · POS integrations launched
£726.1K
Year 3 Revenue · ~310 customers · £109,283 net profit · National UK presence · Ireland international entry
8–9
UK-based skilled tech, commercial and operational roles created by Year 3 development, ML, sales, marketing and customer success
How It Works

From Reactive Guesswork
to Predictive Margin Intelligence

ShrinkGuard Twin AI operates as a closed-loop shrinkage intelligence lifecycle ingesting, twinning, scoring, calculating, optimising, and recommending so every loss-prevention decision is financially grounded and store-specific, not reactive guesswork applied uniformly.

The 6-Phase Shrinkage Intelligence Lifecycle

A Complete Margin Protection Loop Not Just a Risk Alert System

Phase 01 · Ingest

Upload Your Data No Hardware Required

ShrinkGuard Twin AI ingests your stock file, sales data, incident logs, staff rotas, and refund records via simple CSV upload. The data-ingestion layer uses Pandas and NumPy validation routines to clean, parse, and structure your data handling missing values, format errors, and inconsistencies before any risk output is generated.

No IT integration required in Phase 1. No RFID infrastructure. No POS API. Just the data retailers already hold, and a simple store map drawn in the browser in under 30 minutes.

CSV Stock & Sales Upload Browser Store Map Input Zero Hardware Required
Data upload and retail analytics setup
Store layout digital twin mapping
Phase 02 · Twin

Build Your Store's Digital Twin

The Store Layout Digital Twin Engine converts your floor plan into a spatial risk model using PostGIS geometry and NetworkX graph relationships. Every zone entrance, exit, fitting room, blind spot, rear aisle, checkout becomes a node with risk attributes: exit proximity, staff visibility, concealment potential, and incident density.

This is not a retail design tool. It is a margin-leakage modelling environment that scores structural theft opportunity even before a single incident has occurred.

PostGIS Spatial Modelling NetworkX Zone Graph Day-One Risk Scoring
Phase 03 · Score

Calculate Product–Zone–Time Risk Dynamically

The Neo4j-powered Product–Zone–Time Risk Graph connects every SKU to its store zone and time window. Risk is calculated across six simultaneous factors product value, gross margin, zone location, exit proximity, staff visibility, and time-of-day patterns. Risk changes as conditions change. Frequently accessed scores are cached in Redis for sub-millisecond retrieval.

6-Factor Dynamic Scoring Neo4j Graph Database Redis Real-Time Cache
Risk scoring analytics and heatmap
Financial margin exposure analysis
Phase 04 · Calculate

Compute Margin-at-Risk Financially

The XGBoost-powered Margin-at-Risk Engine integrates theft probability scores, gross margin data, stock volume, zone risk weights, and time-window multipliers into a probability-weighted financial exposure figure. Facebook Prophet handles seasonal and cyclical patterns school holiday periods, promotional uplifts, payday cycles.

Output: "£3,200 of margin-at-risk in Zone C between 5pm and 8pm on Friday." Not a risk rating. A financial number that demands a decision.

XGBoost MAR Engine Prophet Forecasting Monte Carlo Confidence Range
Phase 05 · Optimise

Rank Interventions by Financial Return

The PuLP-powered Intervention ROI Optimiser frames prevention as a constrained optimisation problem: given available interventions each with a cost, forecast margin reduction, and operational burden find the mix that maximises protected margin within the retailer's constraints and budget. Results in seconds, not spreadsheet hours.

PuLP Linear Optimisation Budget-Constrained Ranking Operational Burden Scoring
Business intelligence and intervention ranking
Retail precision security recommendations
Phase 06 · Recommend

Deliver Precision Recommendations Stop Blanket Security

The Selective Protection Recommendation System outputs a specific, ranked action plan: which exact products to tag, which zones to staff at which times, which displays to reposition. Stop over-investing in blanket coverage. Start precision-protecting the margins that matter.

SKU-Level Recommendations Zone & Time Prioritisation Weekly Action Plan Dashboard
FAQ

Frequently Asked Questions

Everything you need to know about ShrinkGuard Twin AI and what it means for your retail store's margin protection, loss prevention decisions, and operational efficiency.

What is ShrinkGuard Twin AI and why does my store need it?

+
ShrinkGuard Twin AI is a predictive shrinkage intelligence platform designed for UK independent and mid-market retailers with 1 to 20 stores. Unlike CCTV, RFID tagging, or incident management tools that only react after a loss, ShrinkGuard Twin AI predicts where theft will impact profitability before it happens by product, zone, and time window. It constructs a virtual replica of your store, links it to your gross margin, and tells you exactly where your margin is at risk and which actions will protect the most at the lowest cost. For a retailer with £800,000 turnover and a 2% shrinkage rate, that's £16,000 per year recovered through precision decisions rather than blanket security spend.

How quickly can we go live with the platform?

+
Onboarding is deliberately lightweight. A new retailer moves from sign-up to first insight within days, not weeks. The process: upload your stock, sales, and incident data via CSV; draw your store map in the browser in under 30 minutes; the platform generates your first Risk Graph and Margin-at-Risk output within 24–48 hours. No hardware installation. No IT department involvement. No waiting on POS API approvals. You start with data you already hold. POS and inventory system integrations are introduced from Month 12 onwards to deepen accuracy, but they are never prerequisites for generating valuable shrinkage intelligence.

How does the Product–Zone–Time Risk Graph work?

+
The risk graph calculates a dynamic, three-dimensional risk score for every product-zone-time combination in your store. Six factors are evaluated simultaneously: product value, gross margin, store zone, exit proximity, staff visibility, and time-of-day and day-of-week patterns. Built in Neo4j a native graph database which enables millisecond traversal of complex risk relationships. A premium jacket near the exit on a Friday evening rush carries a very different risk score from the same item at the till on Monday morning. No other retail loss-prevention or analytics product models risk in this three-dimensional way.

What is the Margin-at-Risk Engine and how is it different from a risk score?

+
A risk score tells you a product is "high risk." The Margin-at-Risk Engine tells you that there is £4,800 of gross margin exposed in Zone C between 4pm and 8pm on Friday. It integrates theft probability, gross margin percentage, stock volume, zone risk weights, and time multipliers into a single probability-weighted financial exposure figure powered by XGBoost gradient boosting. Monte Carlo simulation then generates a confidence range rather than a single-point estimate: "£2,800–£4,100 across 10,000 scenarios." It's a financial tool, not just a security alert system.

Do we need to buy any hardware?

+
No hardware is required to start. ShrinkGuard Twin AI is a software-only, cloud-hosted platform. Your existing stock records, sales data, and a browser-drawn store map are sufficient to generate risk intelligence from day one. Optional integrations with POS systems, inventory platforms, and CCTV metadata inputs deepen accuracy from Month 12 onwards, but are never prerequisites. The platform is specifically designed to work with data retailers already hold not to create a new data collection infrastructure.

What if my store has no theft incident history?

+
ShrinkGuard Twin AI is specifically designed to provide meaningful output even without historical incident data typical for SMEs. The Layout-Derived Shrinkage Opportunity Score measures structural risk based purely on layout factors: exit proximity, staff visibility gaps, concealment potential, shelf height, and distance from staffed areas. A display can score high purely from its structural location even if theft has never been recorded there. Essential for new store openings. Monte Carlo confidence ranges explicitly reflect data quality, so you always know how much to rely on the output.

What are the pricing tiers?

+
ShrinkGuard Twin AI offers three SaaS subscription tiers: Starter at £99/month for single-store retailers (store layout twin, product risk scoring, basic margin-at-risk, dashboard); Professional at £299/month for single stores needing full capability (full platform including ROI optimiser, forecasting engine, shrinkage analytics layer); and Multi-store at £499 per store per month for retailers with 2–20 locations (all features plus multi-store benchmarking, priority support, integration access). A one-time setup fee of £300 covers store mapping, data import, and configuration. Annual billing at 15% discount available.

Is ShrinkGuard Twin AI compliant with UK GDPR?

+
Yes. ShrinkGuard Twin AI operates within UK GDPR and the Data Protection Act 2018 from day one. The platform works with operational retail data stock records, sales transactions, staff rotas, refund data, and incident logs not personal customer information. All data is stored on AWS infrastructure compliant with UK/EEA standards. Data Processing Agreements are signed with all customers before data upload. ICO registration completed in Month 1. All data at rest encrypted with AES-256, all data in transit via SSL/TLS, role-based access controls throughout. Cyber Essentials certification targeted in Year 1.
Pricing

Transparent SaaS Pricing.
ROI Validated from Month One.

Every tier delivers measurable financial return reducing unnecessary prevention spend, recovering margin from precision-targeted interventions, and giving retailers the financial clarity their business has never had. For an £800K turnover retailer with 2% shrinkage, the platform pays for itself within weeks.

Tier 01
Starter
£99/month

Shrinkage intelligence for single-store independent retailers who need to start protecting margins without a specialist LP team.

  • Store layout twin (browser-mapped)
  • Product risk scoring by zone
  • Basic margin-at-risk output
  • Risk heatmap dashboard
  • CSV data ingestion
  • Top-10 high-risk SKU ranking
  • Weekly action summary report
Most Popular
Tier 02
Professional
£299/month

Full predictive shrinkage intelligence for single-store retailers who need the complete platform including ROI-ranked intervention recommendations.

  • Full Product–Zone–Time Risk Graph
  • Complete Margin-at-Risk Engine
  • Intervention ROI Optimiser
  • Forecasting engine (Prophet + XGBoost)
  • Shrinkage analytics layer
  • Refund & return anomaly detection
  • Customer experience constraint engine
  • Full dashboard v2 with action plans
Tier 03
Multi-Store
£499/store/mo

All Professional features plus multi-store benchmarking and priority support for retailers operating 2–20 locations.

  • All Professional features included
  • Multi-store benchmarking dashboard
  • Cross-store margin-at-risk comparison
  • Priority customer success support
  • Integration access (POS, inventory)
  • Dedicated onboarding specialist
  • Annual account review & model refinement
Professional Services

One-Time Professional Services

Service 01
Platform Setup & Onboarding
£300 one-time

Complete store mapping, data import configuration, risk model calibration, and initial dashboard walkthrough live in under a week.

  • Browser store map session (guided)
  • CSV data intake & validation setup
  • Initial risk model calibration
  • Dashboard training for store managers
Service 02
Shrinkage Audit & Strategy Report
£500 one-time

Retrospective audit of stock discrepancy history, refund anomaly patterns, and layout vulnerabilities with a written shrinkage reduction strategy report.

  • Historical incident & discrepancy review
  • Layout vulnerability assessment
  • Personalised shrinkage reduction strategy
  • Manager-level briefing session included
Contact & Demo

Request a Platform Demo.
See ShrinkGuard Twin AI Live on Your Store Data.

Our personalised demos show your store's actual Margin-at-Risk profile, live risk heatmap, and ROI-ranked intervention recommendations using your own data and store layout context, not generic examples. See exactly how the platform recovers margin you are currently losing.

Book Your Platform Demo

Fill in your details and our team will contact you within 24 hours to arrange a personalised walkthrough.

Why Book a Demo?

Our personalised demos show your store's actual margin-at-risk profile, live risk zone heatmap, and ranked intervention recommendations using your own retail context. We demonstrate how the Product–Zone–Time Risk Graph, Margin-at-Risk Engine, and Intervention ROI Optimiser work together to recover lost margin and give your business the financial precision it has never had.

Websiteshrinkguardtwinai.co.uk
Founder & CEOHassan Asghar · MSc International Business, Ulster University UK
UK HeadquartersUnited Kingdom
Response TimeWithin 24 hours · Mon–Fri 9am–6pm GMT
IP StatusUK Patent Application Filed · PCT International In Progress · UKIPO Trademark Registered
Data PrivacyUK GDPR · ICO Compliant · AES-256 Encryption · AWS Infrastructure
Pilot Programme3 Months Free · Structured Feedback · Written Case Study