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The rise of generative AI represents one of the most disruptive forces shaping enterprise technology today.

Leading organizations are exploring how large models including
LLMs and multi modals can enhance operations, reduce costs, and provide
competitive advantage.


Opportunity scans to identify high-potential 
AI applications


Vendor and technology evaluation


Data readiness 
and infrastructure assessments


Early adopters have already seen substantial benefits:


Reduced costs

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Increased revenues

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Improved NPS

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Reduced risks

Uncertainty remains around where to start and how to demonstrate tangible value from generative AI's capabilities.

This is where esynergy comes in.


Tackle business challenges head-on with esynergy's proven solutions.

With the help of genAI, we've been supporting our clients solve these 
business challenges.

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Data privacy and security

Protecting sensitive data and breaches and unauthorized access

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Dependency on external providers

Reliance on third-party LLM services and their limitations

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Contextual limitations

LLMs may struggle with domain-specific context

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Ethical and legal considerations

Responsible AI use and legal liabilities

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Customization and integration

Adapting LLMs to enterprise needs and integrating them into workflows

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User training and adoption

Ensuring users understand and effectively use LLMs

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Quality control

Ensuring reliability, consistency, and addressing biases in LLM-generated content

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Data quality and availability

Access to high-quality, relevant data to enhance the context


Cost and resource constraints

Financial investments in LLM development and infrastructure

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Ability to handle increasing volumes of user queries and data


We will help you in your GenAI journey and maturity

We use a 5-stage maturity model for evaluating enterprise readiness for adopting GenAI use cases


Stage 1 / 1-2 months

  • Conducting initial research into GenAI capabilities
  • Early experimentation with limited Proof of value
  • Assessing potential use cases and impact
  • Determining required data, skills and infrastructure


Stage 2 / 3-5 months

  • Establishing GenAI ethics guidelines and governance
  • Acquiring computational resources and platforms
  • Building specialised GenAI skills and teams
  • Developing intitial GenAI framework models and workflows


Stage 3 / 6-9 months

  • Implementing GenAI applications for focused functions
  • Integrating GenAI models into select processes
  • Monitoring and debugging GenAI systems
  • Developing workflows for model retraining and refinement


Stage 4 / Iterative

  • Expanding GenAI usage across business units
  • Achieving core GenAI workflows and pipelines
  • Ensuring explainability and algorithmic fairness
  • Measuring broad operational KPI improvements


Stage 5 / Iterative

  • Fully leveraging GenAI for competitive advantage
  • Strategic investment in capabilities and talent
  • Developing rapid iteration cycles for models
  • GenAI-powered transformation of products or services



  • Fully leveraging GenAI for competitive advantage
  • Strategic investment in capabilities and talent
  • Developing rapid iteration cycles for models
  • GenAI-powered transformation of products or services


esynergy couples technical expertise

our approach

Our rapid proof of value approach follows key phases:

As trusted navigators in the generative AI landscape, we partner with organizations to identify their most promising use cases through structured 2-month proof of value (POV) sprints.

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We conduct opportunity scans, assessing your needs and identifying high-potential applications.

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Next we design the AI solution, detailing required data, workflows, and success metrics.

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Our engineers then build a minimum viable prototype for validation.

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Finally, we measure performance and plan production deployment.

Our solutions

Navigating the constantly evolving landscape of AI can be challenging. Let us guide you with our proven solutions.

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Throughout the engagement, you'll work closely with our 
cross-functional team of AI strategists, data scientists, and machine learning engineers to ensure alignment with your business goals.

The outcome is an informed perspective on where generative AI can drive efficiency, automation, and competitive differentiation for your organization. Let our experts guide you on the most impactful applications of this emerging technology.

Our team

Discover our Generative AI experts

Prasad Prabhakaran

Head of AI

Prasad is a seasoned product manager with over 20 years of experience in creating data, Al, and technology products for industry giants such as Microsoft, Samsung, and leading banks like HSBC, ABN Amro, and Amex.

He excels at building user-friendly products, championing new ways of working, driving community engagement, and leveraging emerging technologies like GenAl to deliver value for both users and businesses. He is one of the founders of the London Al meetup group and the Datamest meetup community.

Sunny Jaisinghani, esynergy's Head of Data

Sunny Jaisinghani

Head of Private sector - Data, AI and Platforms

Sunny Jaisinghani, an accomplished thought leader with 18 years of experience in data. 
His career spans leadership and delivery roles at institutions like Northern Trust and HSBC, where he led transformative initiatives in digital and 
data domains.

His career also highlights his role as a DevOps and agile transformation lead, cultivating a culture of experimentation and value delivery across 
50+ delivery streams. He has expertise in diverse industries, including finance, healthcare, 
and education.

Adrian Gonzalez Rodriguez

Adrian Gonzalez Rodriguez

Principal Technology Consultant

Adrian has extensive experience as a Senior Technical Lead and Platform Engineer, Adrian brings a wealth of expertise in architecting and deploying diverse data platforms across various sectors.

Adrian's advanced technical skills encompass both engineering and infrastructure aspects, making him an invaluable asset in shaping and leading technical projects. His strong foundation in architecture and DevOps has enabled him to successfully deploy, develop, and define various data platforms in production environments.