PredictX Revenue and Competitors

UK

Location

N/A

Total Funding

Estimated Revenue & Valuation

  • PredictX's estimated annual revenue is currently $9.7M per year.(i)
  • PredictX's estimated revenue per employee is $130,500

Employee Data

  • PredictX has 74 Employees.(i)
  • PredictX grew their employee count by 4% last year.

PredictX's People

NameTitleEmail/Phone
1
SVP - Global SalesReveal Email/Phone
2
EVP Solution Engineering and ProductReveal Email/Phone
3
Head Client AnalyticsReveal Email/Phone
4
Director Product ManagementReveal Email/Phone
5
Data ScientistReveal Email/Phone
6
DirectorReveal Email/Phone
7
Project ManagerReveal Email/Phone
8
Product ManagerReveal Email/Phone
9
HR ManagerReveal Email/Phone
10
Senior Implementations ConsultantReveal Email/Phone
Competitor NameRevenueNumber of EmployeesEmployee GrowthTotal FundingValuation
#1
$8.2M630%N/AN/A
#2
$3.8M33-33%N/AN/A
#3
$293.3M1445-12%N/AN/A
#4
$1.7M19-17%N/AN/A
#5
$13.1M90-11%N/AN/A
#6
$6.7M5138%N/AN/A
#7
$14.1M973%N/AN/A
#8
$5.1M4447%N/AN/A
#9
$16.7M115-78%N/AN/A
#10
$23.3M14626%N/AN/A
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What Is PredictX?

PredictX delivers big data, machine learning, and decision automation services to data-reliant industries, in particular financial services, procurement, retail, travel and healthcare. Using embedded intelligence and data visualisation technology, PredictX continuously drive cost savings, operational efficiencies and revenue growth. PredictX supports clients in capturing, cleaning and compiling their data from multiple sources from within and outside the business.

keywords:N/A

N/A

Total Funding

74

Number of Employees

$9.7M

Revenue (est)

4%

Employee Growth %

N/A

Valuation

N/A

Accelerator

PredictX News

2022-04-20 - Predictive Learning Market Set To Highest Growth Returns, Future Developments, Dynamic Platforms and Revenue E

Predictive Layer, Learn To Forecast, Prenostik, PredictX , Civitas Learning, Programmai, Intersect Labs, Gnowise Predictive Learning Market...

2022-04-20 - How is the Expectation-Maximization algorithm used in ...

... y_pred=model.predict(X) model.score(X) -0.7380311409986876. The score function returns the log-likelihood which the lower the better.

2022-04-06 - One vs One, One vs Rest with SVM for multi-class classification

yhat = o_vs_r.predict(X) yhat. Output: Here in the above, we can see that the support vector classifier is predicting 3 classes and we did...

Company NameRevenueNumber of EmployeesEmployee GrowthTotal Funding
#1
$7.5M7428%$824.7B
#2
$14.7M7412%N/A
#3
$6M74-6%N/A
#4
$7.5M749%N/A
#5
$15M747%N/A