Zestminds

Zestminds supports the full product lifecycle, including early planning, greenfield development, launch, iteration, modernization, and scale. Its leadership team includes:

• Keshwani Sharma, Founder and CEO
• Shivam Sharma, Founder and CTO
• Amit Rana, Head of Delivery and Client Success
• Rajat Sharma, Head of Engineering

Zestminds states that it has more than 14 years of engineering experience and has served teams in more than 18 countries.

• AI-assisted product workflows
• Chat and assistant experiences
• Search, summarization, and recommendations
• LLM and model-backed interfaces
• Document extraction and processing
• Workflow routing and decision support
• CRM and support automation
• Operational process intelligence
• Backend and API integration
• System orchestration and control logic
• Production-ready AI implementation

Zestminds approaches AI as product and systems work rather than as a prompt experiment. The team considers the product or workflow problem, data, risk level, user experience, technical constraints, integration points, and reliability requirements.

AI project process

1. Use Case and Workflow Discovery: Determine whether AI is useful, where it fits, and what approach makes sense.
2. Architecture, Orchestration, and Evaluation: Define model logic, workflow architecture, data needs, integrations, and control points.
3. Build, Integrate, and Improve: Implement the system with attention to usability, product fit, measurable value, and long-term evolution.

Not every AI project requires an LLM or agent system. Depending on the use case, simpler automation logic, structured decision workflows, recommendation systems, or other AI-supported features may be more appropriate.

The website invites visitors to schedule an architecture review, discuss an AI use case, discuss a workflow, or talk with the team about a product or platform. The architecture review is described as a 30-minute technical discussion with no obligation.

• Internal process automation
• Task routing and workflow triggers
• Operational assistant systems
• AI-supported process execution
• CRM and lead workflow automation
• Support workflow improvement
• Response and action assistance
• Business process orchestration
• Document processing workflows
• AI-assisted data handling
• Summarization and extraction systems
• Decision-support workflow layers

Automation systems can connect AI to business logic, tools, triggers, documents, databases, CRM systems, support platforms, internal tools, and operational software. The goal is to move work forward, not simply answer questions.

Automation process

1. Workflow Review: Identify where manual effort is accumulating and which steps are suitable for AI support.
2. Automation Design: Define logic, data flow, control points, integrations, and user interactions.
3. Build, Test, and Improve: Implement the workflow system with a focus on reliability, usability, and measurable operational improvement.

Zestminds designs controls, fallback behavior, integrations, and user review points around the actual process. Some workflows may use LLMs or agent-style logic, while others may be better served by simpler automation, structured decision flows, or hybrid systems.

• API and backend system design
• Service evolution planning
• Observability and reliability
• Infrastructure built for scale
• Backend restructuring
• Database redesign
• Performance stabilization
• Long-term scalability planning

Zestminds can integrate AI directly into platform architecture through:

• LLM integrations, including RAG and agents
• Intelligent workflow automation
• Production-ready inference layers
• AI integrated into core backend logic

Delivery approach

1. Architecture Review: Analyze the current platform, identify scaling risks, and review architecture decisions affecting performance.
2. Stabilization Strategy: Design improvements across backend systems, infrastructure, and data layers.
3. Engineering Execution: Implement scalable architecture with attention to performance, maintainability, and operational stability.

Zestminds also supports responsible modernization. Legacy systems can be improved through structural changes rather than reckless rewrites.