Enterprise AI & Data Analytics

Decisions That Move With the Business, Not Behind It

Turning enterprise data into forecasts, insight and production AI that reach the people and workflows that act on them.

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Adobe
Autodesk
AWS
BD
BMC
Ciena
Cisco
Citrix
ClickUp
Cognizant
Dell
DocuSign
Dropbox
Emerson
Ericsson
Gainsight
ClickUp
Cognizant
Dell
DocuSign
Dropbox
Emerson
Ericsson
Gainsight
Data Strategy Data Engineering Business Intelligence Predictive Analytics MLOps Generative AI

When Business-Critical Data Tells Different Stories

Finance closes on ERP. Sales reports from CRM. Operations uses a separate warehouse. Each system works as designed — the numbers still disagree.

Definitions

Conflicting Business Metrics

Revenue, customer, inventory and operational measures are calculated differently across functions.

Timing

Insights That Arrive Too Late

Reporting explains what happened after the opportunity to adjust a plan has passed.

Delivery

Analytics Outside the Workflow

Forecasts remain in separate dashboards instead of reaching the ERP, CRM or planning process.

Ownership

AI Without Production Ownership

Responsibility for quality, monitoring, validation and continuous improvement remains undefined.

From Data to Production Intelligence

A successful program begins with a decision, not a technology choice

Step 01

Define the Decision

Identify who makes the call, what information supports it, and how improvement will be measured.

Step 02

Prepare the Data

Address integration, quality, lineage, access and governance before modelling begins.

Step 03

Engineer the Intelligence

Apply the lightest method that answers the question — BI, analytics, machine learning or generative AI.

Step 04

Integrate the Workflow

Place forecasts and recommendations where teams already work, not in a separate dashboard.

Step 05

Operate and Improve

Establish monitoring, controls and feedback loops so production learning returns to step one.

AI and Data Capabilities Built for Real Environments

We retain what still creates value, modernize what limits performance, and introduce AI where there is a clear operational purpose

Data Foundation

Making enterprise data trustworthy before anything is built on it

Data Strategy & AI Readiness

Prioritize viable use cases, assess data quality, define ownership and establish success measures before major engineering begins.

  • Use-case prioritization
  • Data quality assessment
  • Ownership and lineage
  • Success measures

Data Modernization & Engineering

Connect ERP, CRM, cloud and operational systems through governed pipelines, warehouses, lakes and lakehouse environments.

  • Governed pipelines
  • Warehouse & lakehouse
  • Real-time integration
  • Master data management

Business Intelligence Platforms

Executive dashboards, self-service analytics and embedded decision platforms built around consistent business definitions.

  • Executive dashboards
  • Self-service analytics
  • Embedded decision platforms
  • Consistent definitions

Applied Intelligence

Turning trusted data into forecasts, decisions and recommendations

Predictive & Prescriptive Analytics

Anticipate demand, risk and operating conditions, then evaluate possible actions and their likely consequences.

  • Demand forecasting
  • Risk and anomaly detection
  • Scenario evaluation
  • Recommended actions

Machine Learning & MLOps

Develop, validate, deploy and monitor models with version control, retraining and clear production ownership.

  • Model development
  • Validation and testing
  • Version control and retraining
  • Production monitoring

Computer Vision

Apply image and video intelligence to inspection, detection and monitoring under real operating conditions.

  • Visual inspection
  • Detection and counting
  • Condition monitoring
  • Edge deployment

Language & Knowledge

Making unstructured enterprise information usable and governed

NLP & Document Intelligence

Classify content, extract entities, improve semantic search and route unstructured information to the right process.

  • Document classification
  • Entity extraction
  • Semantic search
  • Exception routing

Generative AI & Knowledge Systems

Knowledge assistants and Retrieval-Augmented Generation applications that answer only from approved sources, under the permissions each person already holds.

  • Enterprise knowledge assistants
  • RAG applications
  • Source attribution
  • Human review and escalation

Ignis AI Platform

Ignis brings enterprise knowledge, data and workflows into a governed intelligence environment, designed around your existing systems.

  • Governed knowledge discovery
  • Contextual assistance
  • Workflow-based AI
  • ERP and CRM integration

Where AI and Analytics Improve Performance

Prioritized by business value, data readiness, implementation risk and the ability to act on the result

Supply Chain

Demand Forecasting

Combine sales, inventory, promotions, capacity and external signals for better resource planning.

Risk

Anomaly Detection

Identify signals that precede failure, delay or disruption and prioritize intervention.

Customer

Customer Analytics

Connect commerce, service, marketing and account data for segmentation and churn insight.

Documents

Document Intelligence

Classify claims, contracts, invoices and reports, extract details and route exceptions.

Knowledge

Enterprise Assistants

Ground answers in approved knowledge repositories with access controls and source attribution.

Workflow

Decision Intelligence

Integrate forecasts, alerts and recommendations into ERP, CRM and planning applications.

How an AI and Data Engagement Works

Start With the Decision

Define and Assess Readiness

The decision first, the technology second

We identify who makes the decision, what information supports it, and how improvement will be measured. In parallel we evaluate data availability, quality, lineage, architecture, security and governance.

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Start With the Decision
Engineer and Validate

Build the Data and AI Solution

Pipelines, models and workflow integration

We build the data pipelines and AI capability, then test performance, relevance and usability — applying the lightest method that answers the question, whether that is BI, machine learning or generative AI.

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Engineer and Validate
Move It Into Production

Deploy, Monitor and Own

Version control, monitoring, retraining, ownership

Production requires reliable pipelines, enterprise integration, security controls, user adoption and clear ownership. We establish validation, model monitoring, feedback loops and appropriate MLOps.

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Move It Into Production

Enterprise AI and Data FAQs

The questions enterprise teams ask before committing to production

They help organizations prepare, connect and analyse enterprise data, then apply the resulting intelligence to business decisions and workflows. They can include data strategy consulting, data engineering, business intelligence, predictive analytics, machine learning, Natural Language Processing, computer vision and generative AI implementation.
Traditional business intelligence primarily explains what has happened through reports, dashboards and performance metrics. AI analytics can identify patterns, forecast likely outcomes, detect anomalies and recommend possible actions. The right approach often combines both.
We evaluate the business decision, expected value, available data, implementation risk and the organization’s ability to act on the result. Technical feasibility alone is not enough to justify production investment.
More than data volume. We examine data quality, ownership, lineage, architecture, security, integration requirements and the controls needed to use that data responsibly.
Yes. We assess which systems should be retained, integrated, improved or retired, strengthening data flows and AI readiness while preserving technology investments that continue to serve the business.
Production requires reliable data pipelines, enterprise integration, security controls, user adoption and clear ownership. We also establish validation, model monitoring, feedback loops and appropriate MLOps services.
Through dashboards, APIs, or directly within ERP, CRM, planning and operational applications — wherever the decision is actually made.
We design generative AI solutions around approved enterprise information, defined users and specific workflows. RAG applications ground responses in authorized knowledge sources with access controls, source attribution, evaluation and human review.
Measures are tied to the target workflow and may include forecasting accuracy, reporting efficiency, response time, exception rates, manual effort, user adoption, operating cost or earlier identification of risk.
It depends on data readiness, integration complexity, governance requirements and deployment scale. We define phases and dependencies after assessing the environment and intended outcome.
Begin with a focused discussion about the decision, workflow or performance problem you want to improve. We will recommend a practical next step such as an AI readiness assessment, use-case workshop or phased roadmap.

Our Leadership Team

Visionary experts guiding innovation and excellence

Leonard Maganza

Leonard Maganza

Advisory Board Member

Leonard Maganza

Advisory Board Member

Leonard Maganza is a data management executive with over 20 years of experience in enterprise data migration and digital transformation. He currently serves as Head of Business Development at Mareana and formerly as Chief Customer Officer at Syniti. Leonard has guided Fortune 500 companies through complex SAP implementations across manufacturing and life sciences industries

Nilesh Shirsat

Nilesh Shirsat

Chief Delivery and Innovation Officer

Data Analytics Cloud Data Data Engineering

Nilesh Shirsat

Chief Delivery and Innovation Officer

Passionate Cloud Data Platform expert with 13 Plus years of progressive experience in delivering analytics transformation across organizations.

Vijay Naraharishetty

Vijay Naraharishetty

Executive Director

SAP consultant Leadership Digital Transformation

Vijay Naraharishetty

Executive Director

Executive Director, Vijay Naraharishetty was a platinum level SAP Consultant helping many organizations thrive in their SAP Data Migration and Data Analytics. He is profoundly passionate about operationalizing SAP solutions for customers and energizes his team to make an impact via remarkable experiences for customers.

Chetan Sarangale

Chetan Sarangale

AI Strategy & Innovation Lead

AI Strategy Digital Transformation Innovation

Chetan Sarangale

AI Strategy & Innovation Lead

Chetan is an advanced AI system developed to support innovation, strategy, and digital transformation initiatives.

Srinivas Vinti

Srinivas Vinti

Head of India Operations

Head of India Operations

Srinivas Vinti

Head of India Operations

Srinivas Vinti is a seasoned technology leader specializing in Infrastructure Management and DevOps, with a strong track record in team building and strategic planning. He leads IDWTEAM's India operations and is also a private real estate investor.

Lakshmi Avirneni

Lakshmi Avirneni

HR Head India

Lakshmi Avirneni

HR Head India

Lakshmi Avirneni leads Human Resources for iDwteam's India operations, driving talent strategy, employee engagement, and organizational development. With expertise in building high-performance teams across technology and consulting sectors, she is instrumental in scaling iDwteam's India presence while fostering a culture of innovation and excellence.