We Provide Data And Analytics Services, Implementation Assistance, And Ongoing Support For The Life Cycle Of Information And Insights. Organisations Can Use Our Data And Analytics Services Independently To Improve Their "Data-to-value" Cycle. This Ensures That The Digital Transformation Strategies We Develop For Our Clients Properly Leverage The Potential Of Data And Analytics Consulting Services.
Any Business Can Benefit From Our Data And Analytics Services And Solutions To Expand And Differentiate. We Identify Use Cases That Can Help You Achieve Your Business Goals And Develop Analytics Solutions With The Right Expertise And Tools. Over Many Years, Your Data Will Be Used To Improve Performance, Resilience And Growth.
Our Mission Is To Provide Practical Solutions To Grow Your Business. We Provide End-to-end Services, From Data Development In The Cloud To Data Processing, Advanced Analytics, Deep-learning Model Building, And Data Visualisation To Help Businesses Spot Trends, Identify Vulnerabilities, And Envision The Future.
In Data Management, Analysis, And Research Combines Context With Data To Deliver Valuable Business Insights. We Use NLP - Based Algorithms To Manage Both Structured And Unstructured Data. We Implement Appropriate End-to-end Processes For Data Collection And Management Across All Business Units.
We Provide Companies With Insights That Enable Better Decision Making, And By Examining Social Media Interactions Across Multiple Platforms, We Provide Customer Insights. To Increase Customer Acquisition And Conversion Rates, We Use Ml To Research New Customers And Create Personas That Meet Their Needs.
AI ACME 's Experienced Data Analytics Consulting Services Help You Turn Data Into Actionable Intelligence, Ensure Operational Excellence, And Give You A Competitive Advantage. Our Strategic Data Analytics Consultants Can Help You Solve Your Business Problems In A Matter Of Weeks Because They Have Acknowledged Expertise Across The Data Stack.
AI ACME Supply Chain Analytics Services Help You Optimise Your Supply Chain To Increase Profits And Reduce Costs. Our Knowledgeable Experts Leverage The Full Range Of Supply Chain Data Analytics Technologies And Implement Purpose-built Solutions To Address Supply Chain Issues Specific To Your Business.
Whether Your Data Warehouse Is On-premise or In The Cloud. To Create A Custom Data Warehouse With A Strong Bi Framework, Data Model, Data Integration Architecture, And Intelligent Database That Enables Faster Decision Making And Competitive Advantage, AI ACME Assesses Your Business Needs & Management.
Data Analysis Strategies And Techniques Must Be Used In Order To Uncover Insights From Data, Including Measurements, Facts, And Statistics. The Two Primary Methods For Data Analysis Are Qualitative And Quantitative Techniques.
Due To The Numerical Nature Of Quantitative Data, We Work With Numerical Variables Such As Statistics, Percentages, Calculations, Measurements, And Other Data Required For Quantitative Data Analysis. Quantitative Data Analysis Often Involves The Use Of Algorithms, Mathematical Analysis Tools, And Software To Manipulate Data And Produce Insights That Provide Economic Value.
For Example, A Financial Data Analyst May Change One Or More Variables In A Company's Excel Balance Sheet To Forecast The Company's Future Financial Performance. Quantitative Data Analysis Can Also Be Used To Evaluate Market Data To Help A Company Decide On The Appropriate Price For A New Product.
Typically, Non-numerical Information Is Described By Qualitative Data. Working With Unique Identifiers, Such As Labels And Numbers, Is An Essential Part Of The Qualitative Data Analysis Approach Statistics, Percentages, And Categorical Variables Such As Measures And Measurements. We Are Involved In Numerous Business Processes That Can Benefit From Qualitative Data Analysis. Qualitative Data Analysis Techniques.
For Example, We Are Often Used In The Software Development Process. Our Software Testers Track Issues, Which Can Range From Serious To Minor And Include Everything From Functional Errors To Typos. This Information Can Help Improve The Finished Product.
Data Analysis Strategies And Techniques Must Be Used In Order To Uncover Insights From Data, Including Measurements, Facts, And Statistics. The Two Primary Methods For Data Analysis Are Qualitative And Quantitative Techniques.
Due To The Numerical Nature Of Quantitative Data, We Work With Numerical Variables Such As Statistics, Percentages, Calculations, Measurements, And Other Data Required For Quantitative Data Analysis. Quantitative Data Analysis Often Involves The Use Of Algorithms, Mathematical Analysis Tools, And Software To Manipulate Data And Produce Insights That Provide Economic Value.
For Example, A Financial Data Analyst May Change One Or More Variables In A Company's Excel Balance Sheet To Forecast The Company's Future Financial Performance. Quantitative Data Analysis Can Also Be Used To Evaluate Market Data To Help A Company Decide On The Appropriate Price For A New Product.
Typically, Non-numerical Information Is Described By Qualitative Data. Working With Unique Identifiers, Such As Labels And Numbers, Is An Essential Part Of The Qualitative Data Analysis Approach Statistics, Percentages, And Categorical Variables Such As Measures And Measurements. We Are Involved In Numerous Business Processes That Can Benefit From Qualitative Data Analysis. Qualitative Data Analysis Techniques.
For Example, We Are Often Used In The Software Development Process. Our Software Testers Track Issues, Which Can Range From Serious To Minor And Include Everything From Functional Errors To Typos. This Information Can Help Improve The Finished Product.
We Start With An Analysis Of Your Company's Needs. After A Few Meetings, Our Staff Will Review Your Data, Ask The Necessary Questions And Define The Project Goals
Our Support Doesn't Stop With Deployment. We Make Sure That You Obtain Model Runs That Are Successful And That You Receive Ongoing Technical Assistance As Part Of Our Post-deployment Services.
Our Team Of Data Engineers Examines The Data Sets You Have Provided After The Goals Have Been Established. Data Mining And Analysis Are Done By Our Team Using The Agile Methodology.
The Model Is Deployed By The Team On A Test Server, Where It Begins To Operate With Actual Data And We Can Track Outcomes. We Deploy The Model To The Production Server If It Functions Properly On The Test Server.
Using The Processed Data, Our Team Now Starts Developing And Training Models. The Team Ensures That The Data Analysis Models Are As Accurate As Possible And Meet Your Business Objectives.
Our Data Scientists And Engineers Continue To Optimise The Selected Analytics Model After The Initial Modelling Of The Raw Data. Our Team Evaluates The Values Of Each Metric As Well As The Performance Of The Model To Ensure That Overall Accuracy Has Been Significantly Increased.
We Start With An Analysis Of Your Company's Needs. After A Few Meetings, Our Staff Will Review Your Data, Ask The Necessary Questions And Define The Project Goals.
Our Team Of Data Engineers Examines The Data Sets You Have Provided After The Goals Have Been Established. Data Mining And Analysis Are Done By Our Team Using The Agile Methodology.
Using The Processed Data, Our Team Now Starts Developing And Training Models. The Team Ensures That The Data Analysis Models Are As Accurate As Possible And Meet Your Business Objectives.
Our Data Scientists And Engineers Continue To Optimise The Selected Analytics Model After The Initial Modelling Of The Raw Data. Our Team Evaluates The Values Of Each Metric As Well As The Performance Of The Model To Ensure That Overall Accuracy Has Been Significantly Increased.
The Model Is Deployed By The Team On A Test Server, Where It Begins To Operate With Actual Data And We Can Track Outcomes. We Deploy The Model To The Production Server If It Functions Properly On The Test Server.
Our Support Doesn't Stop With Deployment. We Make Sure That You Obtain Model Runs That Are Successful And That You Receive Ongoing Technical Assistance As Part Of Our Post-deployment Services.
AI / ML FRAMEWORK
PYTORCH
TENSOR FLOW
KERAS
SCIKIT LEARN
DATA MANAGEMENT
GIT
DATA LED
MARIA DB
MONGO DB
REDIS
CLUSTER ORCHESTRATION
KUBE FLOW
SLURM
BACK END
NODE
PYTHON
GOLANG
FRONT END
REACT NATIVE
JS
REACT
SYSTEM ENVIRONMENT
DEBIAN
AZURE
CENTOS
KUBERNET
DOCKER
