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Seed Round · April 2026

The app that turns diabetes data
into personal intelligence.

carbIQ is a diabetes management app that goes beyond tracking — it surfaces why blood sugar moves, using correlations across food, activity, sleep, mood and 11 further health streams. Built and ready for App Store launch.

12M
UK adults with diabetes or prediabetes¹
£25.3B
Global diabetes apps market by 2032²
15.5%
Projected market CAGR 2025–2032²
6.3M
UK prediabetics — largest untapped audience¹
The problem & our solution
The problem
4.6M diagnosed diabetics in the UK¹ have access to blood glucose apps that chart their data — but give no explanation of the personal why behind it
6.3M people with prediabetes¹ — 5–10% of whom progress to Type 2 each year³ — are typically told to "eat healthily" with no structured food intelligence tool
Leading apps (MySugr, Glooko, One Drop) track one or two dimensions; none cross-correlate food, activity, sleep, mood and glucose into a personal pattern engine
Glycaemic index tables are derived from population averages — individual glucose responses to the same food are known to vary significantly
carbIQ's answer
Personal Insights engine — detects correlations between food choices, activity, sleep, stress and glucose across 15 data streams, after a minimum of 2 weeks of logging
850,000-food database with glycaemic index, glycaemic load and full macro/fibre/salt breakdown — searchable and barcode-scannable
Multi-condition conflict detection — cross-references food choices against multiple health markers simultaneously (e.g. glucose and cholesterol)
Consent-based anonymised data asset built with every user — structured for research partnerships with pharma, food manufacturers, and academic institutions
Insight types surfaced
Representative engine outputs
These are illustrative examples of the correlation type the Insights engine produces after sufficient data has been logged. Actual values are personal to each user.
Evening walk reduces post-dinner glucose spike
−1.8 mmol/L
Activity + Glucose · illustrative personal pattern
88% model confidence
Short sleep associated with higher fasting glucose
+1.3 mmol/L
Sleep + Glucose · supported by clinical literature
76% model confidence
3-stream weekly heatmap
M
T
W
T
F
S
S
Glucose
Sleep
Mood
Market opportunity
A £1.7B UK segment in a £25.3B global market
£25.3B
Global diabetes apps market by 2032²
↑ 15.5% CAGR
12M
UK adults with diabetes or prediabetes¹
1 in 5 adults
£3.99
Monthly subscription · free tier always available
or £27.99/year
£384K
ARR projection at 8,000 paid subscribers
Year 1 target
Target audiences
Four addressable verticals
1
4.6M diagnosed diabetics (UK)¹ — core product-market fit. The Insights engine directly addresses the gap between raw CGM data and actionable personal patterns.
2
6.3M people with prediabetes¹ — largely unserved by diabetes apps. Research shows 5–10% progress to Type 2 annually³; lifestyle intervention is clinically effective.
3
GLP-1 medication users (Ozempic, Wegovy, Mounjaro) — a rapidly growing cohort needing food intelligence to complement medication. No existing GLP-1 tracker app provides GI/GL data.
4
Women with PCOS — insulin resistance affects 35–80% of those diagnosed. Reducing post-meal glucose spikes is shown to reduce PCOS symptoms. Approximately 1.1M diagnosed in the UK.
Revenue model
Freemium + data licensing (Year 2)
Free tier
Glucose log · basic food · activity · 14-day Insights
£0
Premium annual
All Premium features · saves 42% vs monthly
£27.99/yr
Data partnerships (Year 2)
Pharma · food manufacturers · academic research
TBD
Expansion opportunities
Three under-addressed market angles
🔥
PCOS + insulin resistance — insulin resistance is present in 35–80% of PCOS cases. Dietary glycaemic management reduces PCOS symptoms in clinical literature. Approx. 1.1M women diagnosed in the UK. No existing app targets this specifically.
🔥
GLP-1 food intelligence gap — GLP-1 drugs reduce post-meal glucose spikes, but users are not guided on which foods to prioritise. Existing GLP-1 tracker apps track injections and weight, not food quality or glycaemic impact. carbIQ fills this directly.
🔥
Prediabetes prevention — 73% of GP practices in England were found to not adequately code or manage prediabetic patients. carbIQ's food intelligence and Insights engine map onto NHS Diabetes Prevention Programme goals, supporting a potential commissioning pathway.
Product & technology
Built. Tested. App Store ready.
Insights engine
15 data streams. Personal correlations. 90 days of test data validated.
Food database
850,000+ foods with GI, GL, macros and barcode scanning.
Meal planner
Live daily nutritional totals: kcal, carbs, avg GI, glycaemic load.
15 health streams
Glucose, sleep, BP, cholesterol, weight, mood, hydration + more.
Barcode scanner
Scan packaged foods — full GI, GL and nutrition in two taps.
App Store ready
React Native / Expo. iOS & Android. Production build Q2 2026.
Year 1 roadmap
Staged launch across four audience verticals
50K
Year 1 user target
8K
Paid subscriber target
£384K
ARR at 8K paid subscribers
NHS
DPP partnership proposal Q4 Year 1
Q1
App Store launch · diabetes communities · creator partnerships
Q2
GLP-1 campaign · prediabetes FINDRISC onboarding · GP outreach
Q3
PCOS vertical launch · first academic data agreement
Q4
NHS DPP proposal submitted · data licensing pipeline
The data asset
A longitudinal dataset no academic study currently holds
With user consent, carbIQ builds a real-world dataset correlating food choices (GI/GL/macro), activity, sleep, mood, blood pressure, weight and glucose outcomes across 15 streams simultaneously. This combination at consumer scale does not exist in published research.
The UK government has committed £600 million to a Health Data Research Service. Commercial interest in real-world dietary and glucose datasets is active from pharma, food manufacturers and insurers.
Pharma · GLP-1 / T2D Food manufacturers Academic research NHS England
Use of investment
40%
Growth & acquisition
Community seeding, creator partnerships, GP outreach, PCOS and prediabetes campaigns
30%
Product & engineering
CGM integrations, Apple Health, Android parity, AI-driven insight refinement
20%
Clinical & regulatory
NHS DPP partnership, academic data agreements, NICE evidence pathway
10%
Operations & reserve
Infrastructure, legal, compliance, customer success, 6-month runway buffer
The product · live app screens
Built and ready. This is the real UI.
React Native / Expo · iOS & Android · light mode · App Store submission Q2 2026
Insights engine
9:41
Insights
Based on 52 days of your data
2 wks30 days90 days
79
Good progress
6 of 8 domains active
↑ +4 vs last snapshot
GlucoseMealsActivitySleepMoodCholesterol
Evening walk reduces post-dinner spike
−1.8 mmol/L
88% confidence · Activity + Glucose
Short sleep raises fasting glucose
+1.3 mmol/L
76% confidence · Sleep + Glucose
Weekly pattern · 3 streams
M
T
W
T
F
S
S
Glucose
Sleep
Correlations surfaced across 15 health data streams after 2+ weeks of logging
Food database
9:41
Food log
Today · Avg GI 38 · 1,840 kcal
Greek yoghurt (full fat, 150g)
Carbs 6gProtein 13g138 kcal
GI 11
GL: 1
Wholegrain sourdough (2 slices)
Carbs 32gFibre 4g
GI 54
GL: 17
White jasmine rice (cooked)
Carbs 57gGL 41
GI 72
High
Porridge oats · 100g dry
Carbs
60g
Protein
11g
Fibre
8g
55
GI
33
GL
375
kcal
8g
Fibre
850,000+ foods with GI, GL, full macros and barcode scanner
Meal planner
9:41
Meal planner
Wednesday · 3 meals planned
38
Avg GI
1,312
kcal
52
Total GL
142g
Carbs
Breakfast · 07:30312 kcal
Porridge + blueberriesGI 48
Lunch · 12:30480 kcal
Lentil soup + sourdoughGI 32
Dinner · 19:00520 kcal
Grilled salmon + vegGI 22
Insight: White rice causes your biggest personal glucose spikes — your response exceeds standard GI table predictions.
Live nutritional totals update as you plan — GI, GL, kcal and carbs in real time
Glucose logging
9:41
Blood glucose
Log a reading
5.9
mmol/L
In range
Reading type
Fasting
Pre-meal
Post-meal
Bedtime
Last 7 days
MonTueWedThuFriSatSun
78%
Time in range
5.4
Avg mmol/L
Above range
Save reading
Manual glucose logging with reading type, 7-day chart and time-in-range summary

All market and epidemiological figures in this document are sourced from peer-reviewed research, official NHS/ONS publications, or published market research reports. Year 1 financial projections (sources 6) are internal targets based on freemium conversion benchmarks in comparable health apps and are not guaranteed forecasts. Insight engine example figures (−1.8 mmol/L, +1.3 mmol/L) are illustrative of the type of correlation the engine surfaces — not published clinical claims.

1
12M UK adults with diabetes or prediabetes (4.6M diagnosed, 1.3M undiagnosed, 6.3M prediabetes)
Diabetes UK, February 2025. "One in five adults now live with diabetes or prediabetes in the UK." diabetes.org.uk →
2
Global diabetes apps market: £25.3B by 2032, 15.5% CAGR (2025–2032)
Coherent Market Insights, 2025. "Diabetes Apps Market Size & Opportunities, 2025–2032." Converted from USD $31.6B at April 2026 exchange rate. Market research estimate — range varies by analyst
3
5–10% of prediabetics progress to Type 2 diabetes annually
The Lancet Diabetes & Endocrinology, February 2025. "Prediabetes: much more than just a risk factor." doi:10.1016/S2213-8587(25)00034-8. Also cited in NHS DPP scoping reviews (PMC9321029).
4
Individual glucose responses to identical foods vary significantly between people
Zeevi D. et al. (2015). "Personalized Nutrition by Prediction of Glycemic Responses." Cell, 163(5), 1079–1094. doi:10.1016/j.cell.2015.11.001. This is the foundational paper establishing personal glycaemic variability.
5
Poor or short sleep is associated with higher fasting blood glucose levels
Reutrakul S. & Van Cauter E. (2018). "Sleep influences on obesity, insulin resistance, and risk of type 2 diabetes." Metabolism, 84, 56–66. doi:10.1016/j.metabol.2018.02.010. Direction of correlation well established; magnitude is personal
6
Year 1 targets: 50,000 users, 8,000 paid subscribers, £384K ARR
Internal projection. 50K total users at 16% paid conversion = 8K subscribers. 8K × £3.99/mo × 12 = £383K ARR. Freemium conversion benchmarks drawn from comparable health app cohorts (MySugr reported ~3M registered users with ~30% active). Forward-looking projection — not a guarantee
7
Insulin resistance affects 35–80% of women diagnosed with PCOS
Sirmans S.M. & Pate K.A. (2014). "Epidemiology, diagnosis, and management of polycystic ovary syndrome." Clinical Epidemiology, 6, 1–13. doi:10.2147/CLEP.S37559. Also supported by NIH publication March 2022 (PMC cited in Fortune Business Insights PCOS market report).
8
73% of GP practices in England were not adequately coding or managing prediabetic patients
NHS Digital report, 2017. Cited in: Gillies C. et al. (2022). "The English national health service diabetes prevention programme (NHS DPP): A scoping review." PMC9321029. PMC → 2017 data — coding practices have improved since
9
UK government commits £600M to Health Data Research Service
Wellcome Trust / UK Government announcement, April 2025. "National data service will simplify access to health data for research." wellcome.org →. Also reported in Pharmaphorum, April 2025.

A note on the "0 apps" claim (removed): We removed the previous claim that "0 apps combine food intelligence and a personal insights engine" as this is subjective and hard to substantiate definitively. What we can accurately say is that no leading competitor (MySugr, Glooko, One Drop, Libre-linked apps) currently offers cross-stream personal correlation analysis of the type carbIQ provides. This remains a genuine product differentiation.

Thu 7 May 2026 18:58:25 BST Thu 7 May 2026 18:58:38 BST