Generative AI
Unlock the potential of Generative AI with esynergy
Explore high-impact use cases through rapid, low-risk POVs
About
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.
20%
Opportunity scans to identify high-potential AI applications
15%
Vendor and technology evaluation
50%
Data readiness and infrastructure assessments
Benefits
Early adopters have already seen substantial benefits:
Reduced costs
Increased revenues
Improved NPS
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.
Data privacy and security
Protecting sensitive data and breaches and unauthorized access
Contextual limitations
LLMs may struggle with domain-specific context
Customization and integration
Adapting LLMs to enterprise needs and integrating them into workflows
Quality control
Ensuring reliability, consistency, and addressing biases in LLM-generated content
Cost and resource constraints
Financial investments in LLM development and infrastructure
Dependency on external providers
Reliance on third-party LLM services and their limitations
Ethical and legal considerations
Responsible AI use and legal liabilities
User training and adoption
Ensuring users understand and effectively use LLMs
Data quality and availability
Access to high-quality, relevant data to enhance the context
Scalability
Ability to handle increasing volumes of user queries and data
challenges
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.
five-stage maturity model
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 01
Exploratory
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
1-2 months
Stage 02
Foundational
Establishing GenAI ethics guidelines and governance
Acquiring computational resources and platforms
Building specialised GenAI skills and teams
Developing intitial GenAI framework models and workflows
3-5 months
Stage 03
Functional
Implementing GenAI applications for focused functions
Integrating GenAI models into select processes
Monitoring and debugging GenAI systems
Developing workflows for model retraining and refinement
6-9 months
Stage 04
Cross-functional
Expanding GenAI usage across business units
Achieving core GenAI workflows and pipelines
Ensuring explainability and algorithmic fairness
Measuring broad operational KPI improvements
Iterative
Stage 05
Strategic
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
Iterative
Results
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
Governance
esynergy couples technical expertise
Regulations & Laws
Standards
Company Policies & Values
Best Practices
Governance Layer
End Users
Application Layer
UI Filters Monitoring
Models
open Source LLMs Proprietary LLMs
Infrastructure
Regulations & Laws
Standards
Company Policies & Values
Best Practices
End Users
-
Application Layer
UI Filters Monitoring
-
Models
open Source LLMs Proprietary LLMs
Infrastructure
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.
Explore
We conduct opportunity scans, assessing your needs and identifying high-potential applications.
Design
Next we design the AI solution, detailing required data, workflows, and success metrics.
Validate
Our engineers then build a minimum viable prototype for validation.
Scale
Finally, we measure performance and plan production deployment.
Clients
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
Gen Al Practice Lead
Bio
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
Head of Data Practice
Bio
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 Rodrguez
Principal Architect
Bio
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.
Resources
We have a deep understanding of generative AI.
Demystifying GenAI: Transforming ...the Insurance Landscape
In today’s ever-evolving technological landscape, ...the emergence of generative AI (GenAI) stands as a pivotal moment. Demystifying GenAI is a complex task as some view it as a mere trend, while others recognise it as a revolutionary force. In the history of AI, we now have two distinct eras: pre-GPT and post-GPT. We now live […]
Driving AI and Data Product ...Success: Ten Key Lessons Learned
Product management in the AI and Data Product ...era is a thrilling journey that presents unique opportunities and challenges. Over the years, my work has allowed me to gain valuable insights into the dynamics of building successful AI-driven solutions. In this piece, I intend to share my observations and lessons learned, focusing on the development […]