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.
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.
Data privacy and security
Protecting sensitive data and breaches and unauthorized access
Dependency on external providers
Reliance on third-party LLM services and their limitations
Contextual limitations
LLMs may struggle with domain-specific context
Ethical and legal considerations
Responsible AI use and legal liabilities
Customization and integration
Adapting LLMs to enterprise needs and integrating them into workflows
User training and adoption
Ensuring users understand and effectively use LLMs
Quality control
Ensuring reliability, consistency, and addressing biases in LLM-generated content
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
Scalability
Ability to handle increasing volumes of user queries and data
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
Exploratory
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
Foundational
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
Functional
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
Cross-functional
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
Strategic
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
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
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.
Our solutions
Navigating the constantly evolving landscape of AI can be challenging. Let us guide you with our proven solutions.
AI governance
Ensure effective and ethical use of AI technologies.
• Improve the explainability and understanding of your AI technologies capability
• Measure your AI technologies effectiveness
• Clarify how your AI technology complies with the EU AI Act
• Ensure regulation compliance and avoid potential fines by having proactive governance
AI Agentic Workflow
Build safe and secure AI solutions that deliver on value.
• Handle unstructured data, processes and complex tasks
• Enhance productivity, accuracy and scalability
• Adapt and correct errors autonomously
• Enable flexible reasoning and decision-making
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 Datamesh meetup community.
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.
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