Valuation: NetraMark Holdings Inc.

Market Cap 61.51M 44.28M 38.24M 35.98M 32.7M 4.24B 62.41M 422M 165M 2.12B 166M 163M 7.07B P/E 2026 *
-
P/E 2027 * -
Enterprise Value 61.51M 44.28M 38.24M 35.98M 32.7M 4.24B 62.41M 422M 165M 2.12B 166M 163M 7.07B EV / Sales 2026 *
153x
EV / Sales 2027 * -
Free-Float
91.14%
Yield 2026 *
-
Yield 2027 * -
1 day+4.06%
1 week+2.54%
Current month-11.78%
1 month-11.35%
3 months-28.21%
6 months-32.96%
Current year-36.43%
1 week 0.49
Extreme 0.4858
0.51
1 month 0.47
Extreme 0.4702
0.6
Current year 0.4
Extreme 0.40134
0.89
1 year 0.4
Extreme 0.40134
1.26
3 years 0.11
Extreme 0.114
1.26
5 years 0.1
Extreme 0.1045
1.61
10 years 0.1
Extreme 0.1045
1.61
Manager TitleAgeSince
Chief Executive Officer - 16/02/2022
President - 03/07/2022
Director of Finance/CFO 57 17/07/2022
Director TitleAgeSince
Chairman 40 08/06/2025
Director/Board Member - -
Director/Board Member - 15/06/2022
Change 5-day change 1-year change 3-year change Capi.($)
+4.06%+2.54% - - 44.28M
-0.03%-4.40%-6.86%+52.18% 3,576B
+2.16%-1.94%-1.43%+1,091.25% 412B
+0.08%-3.36%-11.15%+43.51% 87.17B
-1.22%-0.32%+90.59%+162.74% 88.33B
+0.11%-1.58%-35.33%-5.07% 77.44B
-0.62%-11.60%-34.42%+200.57% 71.21B
+0.49%-3.23%+4.24%+76.34% 45.32B
+2.76%+5.79%-3.65%-12.51% 41.26B
+0.24%-9.32%+120.94%+291.33% 35.64B
Average +1.09%-3.33%+13.66%+211.15% 492.77B
Weighted average by Cap. +0.38%-4.07%-4.27%+153.74%

Financials

2026 *2027 *
Net sales 401K 289K 249K 235K 213K 27.65M 407K 2.75M 1.08M 13.83M 1.08M 1.06M 46.07M -
Net income - -
Net Debt - -
Logo NetraMark Holdings Inc.
NetraMark Holdings Inc. is a Canada-based company, which is focused on the development of Generative Artificial Intelligence (Gen AI)/Machine Learning (ML) solutions targeted at the pharmaceutical industry. The Company’s product offering uses a novel topology-based algorithm that has the ability to parse patient data sets into subsets of people that are strongly related according to several variables simultaneously. This allows the Company to use a variety of ML methods, depending on the character and size of the data, to transform the data into powerfully intelligent data that activates traditional AI/ML methods. The result is that it can work with smaller datasets and accurately segment diseases into different types, as well as accurately classify patients for sensitivity to drugs and/or efficacy of treatment. The typical molecular data used is RNASeq, microarray, single nucleotide polymorphism (SNP) and methylation.
Employees
-
Date Price Change Volume
14/08/26 US$0.5055 +4.06% 500
13/08/26 US$0.4858 +3.32% 500
12/08/26 US$0.4702 -4.62% 7,050

Quarterly revenue - Rate of surprise