Data Science, AI & Automation Services

Turn Business Data Into Insight, Prediction and Action

Data Science and AI services help businesses organize information, analyze performance, identify patterns and automate defined processes. Projects may involve data analysis, Machine Learning, dashboards, generative AI, connected reporting or workflow automation.

The technology should be selected only after the business problem is understood. Some requirements need an advanced predictive model, while others are better addressed through improved tracking, cleaner data, clearer reporting or a rules-based workflow.

I work with startups, established companies and internal teams that need to connect disconnected systems, improve measurement, explore a practical AI use case or reduce time spent on repetitive reporting and administration.

Connect, Understand, Predict and Automate

Connect
Bring together useful information from websites, advertising platforms, CRM systems, spreadsheets, databases and business applications.
Understand
Use Data Science, analytics and business intelligence to identify trends, gaps, customer behavior and performance changes.
Predict
Apply statistical methods and Machine Learning where reliable historical data can support forecasting, classification or prioritization.
Automate
Create controlled workflows that move information, prepare reports, process documents or support teams with AI-assisted outputs.

Common Starting Points

Data and AI opportunity assessment
Data-quality and reporting review
Dashboard or integration planning
Predictive analytics feasibility review
AI chatbot or knowledge-assistant prototype
Marketing tracking and automation audit

Core Data Science Services

Data Science services turn raw or fragmented information into findings that can support commercial, operational and marketing decisions. The scope depends on the question being answered, the available data and how the output will be used.

Data Analysis and Business Insights

Data analysis can help explain what is happening across customers, campaigns, sales, products or operations.

Capabilities may include:

Data cleaning and preparation
Exploratory and statistical analysis
KPI and trend analysis
Customer or business segmentation
Performance comparison
Decision-support reporting

The output may be an analysis, report, dashboard or structured set of findings. The emphasis remains on answering a business question rather than producing technical analysis without a practical purpose.

Predictive Analytics and Forecasting

Predictive analytics uses historical information to estimate likely future outcomes. It can support planning and prioritization where sufficient relevant data is available.

Potential applications include:

Demand and revenue forecasting
Customer churn prediction
Lead or customer scoring
Customer behavior modeling
Risk categorization
Anomaly detection

Forecasts are estimates, not guarantees. Their usefulness depends on data quality, historical coverage, changing market conditions and how accurately the target outcome has been defined.

Statistical Analysis and Segmentation

Statistical analysis can help compare groups, test assumptions and identify meaningful relationships in business data.

Segmentation may be used to:

Understand different customer groups
Compare behavior or performance
Improve targeting and prioritization
Identify retention opportunities
Support product or service planning

The analytical method is selected around the decision being made rather than the complexity of the technique.

Not sure which data service fits?Share the business problem and the information currently available.

Discuss My Data and AI Project

Machine Learning and Predictive Solutions

Machine Learning services can help identify patterns, classify information and support repeatable predictions. A suitable project needs a clearly defined objective, relevant historical data and a practical way to use the result.

Custom Machine Learning Models

Potential Machine Learning solutions include:

Classification models
Regression and forecasting models
Recommendation systems
Lead or customer scoring
Churn prediction
Anomaly detection

A project may involve data preparation, feature development, model selection, evaluation, optimization and implementation planning.

Advanced modeling or engineering may involve specialist technical expertise where required. I remain the primary contact for the business requirements, project communication and delivery coordination.

Model Evaluation and Practical Use

A technically accurate model is not automatically a useful business solution. Evaluation should consider how different errors affect the actual decision or workflow.

Relevant checks may include:

Performance against an appropriate baseline
False positive and false negative impact
Stability across different data groups
Potential bias
Explainability requirements
Integration and monitoring needs

A simpler analytical or rules-based approach may be recommended when it can solve the problem without unnecessary technical complexity.

Practical Model Workflow

Business DataRelevant historical inputs
Model DevelopmentPrepared around the defined objective
EvaluationBaseline, errors and limitations reviewed
Decision SupportResults connected to a practical use

Generative AI, LLMs and Intelligent Applications

Generative AI and large language model applications can help businesses search, summarize, classify and process information through natural-language interfaces and controlled workflows.

Approved DataAI or LLM SystemBusiness RulesHuman ReviewFinal Output

AI Chatbots and Knowledge Assistants

A custom AI chatbot or internal knowledge assistant may use approved business documents, product information, policies or connected data sources.

Potential applications include:

Customer or employee question answering
Internal policy search
Product-information retrieval
Document comparison
Training-material access
Support workflow assistance

Retrieval-augmented generation can be used to ground responses in approved sources instead of relying only on a model’s general knowledge. Source checking and human escalation may still be required.

Document Processing and Information Extraction

AI can support businesses that need to work with large volumes of documents, emails or written information.

Potential capabilities include:

Document summarization
Field and entity extraction
Text classification
Search and retrieval
Ticket or enquiry categorization
Feedback and sentiment analysis

The suitability of the solution depends on document quality, required accuracy, privacy considerations and how the extracted information will be reviewed.

AI Agents and Workflow Assistants

AI agents can perform defined multi-step tasks using approved tools, information and instructions.

Suitable workflows may include:

Preparing recurring reports
Categorizing incoming enquiries
Updating approved CRM fields
Extracting information from documents
Routing tasks
Drafting outputs for approval

Agents should not be treated as unrestricted autonomous workers. Sensitive actions may require controlled permissions, activity logging, error handling and human approval.

Natural Language Processing and Computer Vision

Specialist NLP or computer vision capabilities may be appropriate when a project involves large amounts of text, documents or images.

Potential use cases include:

Text and sentiment classification
Entity extraction
Document analysis
Image classification
Object detection
Visual inspection
Document-image processing

These capabilities are recommended only where they support a defined business objective and suitable data is available.

Data Engineering, Business Intelligence and Dashboards

Reliable analytics and AI depend on connected, correctly prepared and consistently defined data. Data engineering and business intelligence services create the foundation for dependable reporting, modeling and automation.

Data Integration and Preparation

Support may include:

Connecting multiple data sources
API and database integration
Data pipelines
Data transformation
Data-quality checks
Scheduled data refreshes

The appropriate structure depends on data volume, source access, refresh frequency, security requirements and how the finished system will be maintained.

Executive KPI OverviewConnected reporting

MarketingSalesCustomers

Pipeline reportingOperational reporting

Business Intelligence and Dashboard Development

Business intelligence systems organize important information into a consistent view of performance.

A dashboard is only dependable when its tracking, source data, metric definitions and calculations are also dependable. These elements should be reviewed together rather than treating visualization as a separate task.

Dashboard services may include:

Executive KPI dashboards
Marketing dashboards
Sales and pipeline reporting
Customer-performance dashboards
Operational reporting
Automated recurring reports

Depending on the project, reporting may use Looker Studio, Power BI, Tableau, spreadsheets or another suitable environment.

Discuss My Data and AI Project

Marketing Data and AI Automation

Marketing Data and AI Automation connects website tracking, advertising platforms, CRM information, reporting and repeatable workflows.

I directly manage the marketing, website and measurement requirements of these projects. This helps connect technical data work with the way campaigns generate enquiries, leads and customer activity.

WebsiteGA4Google AdsMeta AdsCRM

Connected Reporting Insights and Automation

Tracking and Conversion Measurement

Support may include:

GA4 setup and review
Google Tag Manager implementation
Website and form-event tracking
Google Ads conversion tracking
Meta Ads conversion tracking
Ecommerce measurement

Tracking can be reviewed for missing events, duplicated conversions, incorrect triggers and inconsistent results across platforms.

Website implementation requirements may connect naturally with Website Design, while advertising measurement can support PPC & Paid Ads projects.

Cross-Channel Marketing Dashboards

Marketing information can be combined from sources such as GA4, Google Ads, Meta Ads, SEO platforms, CRM systems and spreadsheets.

A connected reporting view may help teams compare:

Campaign spend and activity
Leads and conversions
Cost per lead or acquisition
Channel performance
Lead quality
Pipeline or revenue outcomes

The goal is not to display every available metric. It is to organize the information around the decisions the business needs to make.

Lead-Source Tracking and Attribution

Marketing attribution can improve visibility into which channels, campaigns and touchpoints contribute to conversions.

The work may include:

UTM and campaign naming structures
Lead-source capture
Platform-data comparisons
CRM outcome integration
Offline-conversion feedback
Funnel-stage reporting

Attribution is not perfect. Consent choices, device changes, offline activity and different platform attribution rules can affect the reported results.

Marketing measurement may also connect with AI SEO when organic visibility, AI-search performance, website activity and conversions need to be reviewed together.

Automated Reporting and AI-Assisted Analysis

Automated workflows can reduce the time spent collecting and organizing recurring marketing data.

A workflow may:

Retrieve data from approved sources
Standardize recurring metrics
Refresh dashboards
Prepare scheduled summaries
Highlight unusual changes
Draft observations for review

AI-assisted analysis can help organize information and surface questions. It should not be treated as a guaranteed explanation of why performance changed.

CRM and Lead Automation

Marketing data becomes more useful when it connects with the CRM and sales process.

Potential workflows include:

Preserving campaign-source information
Updating CRM fields
Categorizing or scoring leads
Routing enquiries
Sending internal notifications
Reporting lead stages and outcomes

These workflows are designed to improve visibility and consistency. They do not guarantee lead quality, sales or campaign performance.

Want clearer tracking and less manual reporting?Share the platforms, reports and recurring work you currently manage.

Discuss My Data and AI Project

Business Problems These Services Can Address

Data Science, AI and automation can support different business problems depending on the data, tools and processes already in place.

Disconnected Data

Problem: Information is spread across platforms, spreadsheets and databases.

Solution direction: Connect or standardize relevant sources to create more consistent reporting, analysis or automation.

Manual Reporting

Problem: Teams repeatedly collect and organize the same information.

Solution direction: Build connected dashboards or scheduled reporting workflows that may reduce repetitive preparation.

Unreliable Tracking

Problem: Website and advertising conversions are missing, duplicated or inconsistent.

Solution direction: Review GA4, GTM, platform tags, event definitions and lead-source tracking before relying on the reports.

Limited Forecasting

Problem: Planning depends heavily on assumptions or manual estimates.

Solution direction: Assess whether historical data can support forecasting, scoring or predictive modeling.

Repetitive Administrative Work

Problem: Staff spend time categorizing, transferring or summarizing information.

Solution direction: Use rules-based or AI-assisted workflows where tasks and review requirements can be clearly defined.

Inaccessible Business Documents

Problem: Useful information is difficult to find across documents and knowledge sources.

Solution direction: Create document search, question-answering or information-extraction workflows using approved sources.

Unclear AI Use Case

Problem: The business wants to use AI but does not know where it will provide practical value.

Solution direction: Review business processes, data availability, risks and expected outcomes before selecting a technology.

How the Process Works

Each project begins with the business problem rather than a predetermined platform, model or software tool.

01
Business Problem and Goals
I clarify the decision, reporting issue, workflow or customer problem the project needs to address and the practical result required.
02
Data and System Review
Available data, documents, platforms, tracking and workflows are reviewed for relevance, access, quality and limitations.
03
Solution Planning
The most suitable approach is defined. This may involve analysis, dashboards, integration, automation, predictive modeling or a custom AI application.
04
Prototype or Initial Analysis
A focused prototype, data review or initial workflow may be used to test feasibility and refine requirements.
05
Development and Validation
The approved solution is created and reviewed against defined business and technical requirements, with specialist expertise involved where advanced implementation is required.
06
Delivery and Integration
The work is integrated or prepared for handover, with outputs, dependencies, limitations and future monitoring requirements reviewed.

Have a defined problem or an early idea?A complete technical brief is not required before starting the discussion.

Discuss My Data and AI Project

Data Quality, Security and Responsible AI

AI and analytical systems depend on the quality of their data, assumptions and operating controls. These factors should be considered before development begins.

Data Quality

Relevant information is reviewed for completeness, accuracy, consistency, historical coverage and potential bias.

Access and Confidentiality

Data access, transfer, storage and permissions should be agreed according to the approved scope, selected platforms and sensitivity of the information.

Accuracy and Limitations

Predictions and generative AI outputs may contain errors. Results should be evaluated against defined requirements and appropriate business baselines.

Human Review

Important recommendations, generated content or automated actions may require human approval before they are used.

Monitoring and Responsible Use

Models and workflows may need monitoring when source data, customer behavior or business conditions change.

No model should be presented as completely accurate, unbiased or suitable for every decision. A simpler solution may be recommended where it provides sufficient value with less risk and complexity.

Who These Services Are For

These services are suitable for businesses that need clearer reporting, connected information, predictive insights or more efficient workflows.

Startups and SaaS businesses

KPI planning, customer analysis, forecasting, product and marketing data integration or AI prototypes.

Ecommerce and product companies

Customer segmentation, demand forecasting, ecommerce tracking and marketing dashboards.

Marketing and growth teams

GA4, GTM, conversion tracking, attribution, campaign reporting and CRM automation.

Professional service firms

Lead-source tracking, pipeline reporting, document processing and workflow automation.

Operations and finance teams

Forecasting, anomaly detection, management reporting and process analysis.

Businesses with disconnected systems

Integration and reporting across multiple platforms, spreadsheets or databases.

Pawan Kumar, digital marketing consultant behind Hey Pawan

Why Work Directly With Pawan

You have one clear point of communication for the business requirements, marketing context, tracking strategy, dashboard planning and project coordination.

Direct communication throughout the project
Business requirements translated into a clear technical scope
Website, SEO and paid-media context considered
Practical tracking and automation experience
Specialist technical expertise involved where required

I remain responsible for communication and project continuity from the initial requirements through planning, coordination and delivery.

Discuss My Data and AI Project

Frequently Asked Questions

What Data Science and AI services are available?

Services may include data analysis, predictive analytics, Machine Learning, generative AI, dashboards, data integration, marketing analytics, conversion tracking and workflow automation. The final scope depends on the business problem, available data and current systems.

What data is required to begin?

The required data depends on the use case. It may include spreadsheets, CRM records, campaign data, transactions, website analytics, databases, documents or application information. An initial review helps determine whether the available data is suitable.

Does every project require Machine Learning?

No. Some problems are better addressed through improved tracking, data analysis, dashboards or rules-based automation. Machine Learning is recommended only when the data, objective and expected value justify the additional complexity.

Can predictive models be developed?

Predictive modeling may be suitable for forecasting, classification, churn prediction, lead scoring, segmentation or anomaly detection. Feasibility depends on historical coverage, data quality, accuracy requirements and how the prediction will be used.

Can you create an AI chatbot or knowledge assistant?

A chatbot or knowledge assistant can be planned around approved documents, product information or connected business sources. The project may include retrieval, access controls, source checking, output evaluation and human escalation.

What is an AI agent?

An AI agent is a system designed to complete defined multi-step tasks using approved tools, information and instructions. It may prepare reports, update systems or route work, but sensitive actions should include suitable permissions and review.

Can business and marketing dashboards be automated?

Dashboards can often be connected to approved data sources and refreshed on a schedule. The implementation depends on platform access, source reliability, metric definitions and the frequency at which the information needs to be updated.

Can data from multiple platforms be combined?

Often, yes. Website, advertising, analytics, CRM, database and spreadsheet data may be combined where suitable access, identifiers and integration options are available. Data quality and platform limitations are reviewed before the reporting structure is confirmed.

Can marketing tracking and CRM workflows be improved?

GA4, GTM, advertising tags, lead-source capture and CRM workflows can be reviewed for gaps or inconsistencies. Automation may support lead routing, field updates, notifications and reporting where the connected tools allow the required actions.

How are project pricing and timelines determined?

Pricing and timing depend on the objective, condition of the data, number of connected systems, integration requirements, technical complexity and expected deliverables. These factors are reviewed before the project scope is confirmed.

How is data security handled?

Access, confidentiality, transfer, storage and permissions are considered according to the project requirements, selected tools and sensitivity of the information. No universal security or compliance claim is made without reviewing the specific environment.

Do I work directly with Pawan?

Yes. I manage communication, business requirements, marketing context, tracking strategy, dashboard planning and project coordination directly. Advanced modeling, engineering or AI implementation may involve specialist technical expertise where required.

Data Science, AI and Automation Services

Turn Business Data Into Insight, Prediction and Action

Connect, Understand, Predict and Automate

Common Starting Points

Data Analysis and Business Insights

Predictive Analytics and Forecasting

Statistical Analysis and Segmentation

Machine Learning and Predictive Solutions

Custom Machine Learning Models

Model Evaluation and Practical Use

AI Chatbots and Knowledge Assistants

Document Processing and Information Extraction

AI Agents and Workflow Assistants

Natural Language Processing and Computer Vision

Data Integration and Preparation

Business Intelligence and Dashboard Development

Tracking and Conversion Measurement

Cross-Channel Marketing Dashboards

Lead-Source Tracking and Attribution

Automated Reporting and AI-Assisted Analysis

CRM and Lead Automation

Disconnected Data

Manual Reporting

Unreliable Tracking

Limited Forecasting

Repetitive Administrative Work

Inaccessible Business Documents

Unclear AI Use Case

Business Problem and Goals

Data and System Review

Solution Planning

Prototype or Initial Analysis

Development and Validation

Delivery and Integration

Data Quality

Access and Confidentiality

Accuracy and Limitations

Human Review

Monitoring and Responsible Use

Startups and SaaS businesses

Ecommerce and product companies

Marketing and growth teams

Professional service firms

Operations and finance teams

Businesses with disconnected systems

Why Work Directly With Pawan

Discuss Your Data, AI or Automation Project