AI Engineering Services
Seamless AI Integration and Deployment for Enhanced Organizational Efficiency.
About
Avasant AI
AI Strategy
& Consulting
AI Engineering
Services
Pre-Trained
AI Agents
AI COE and
Governance
AI Engineering Services
Avasant AI Engineering Services specializes in AI design and implementation, delivering end-to-end engineering solutions that seamlessly integrate AI into business operations. Our team builds custom AI solutions – whether large scale enterprise solutions, or tactical AI Agents — optimizes existing AI infrastructure, and develops scalable AI applications tailored to industry-specific challenges. From data pipeline design to model deployment, we ensure AI solutions are built for efficiency, security, and long-term impact.
Our Offerings
AI Solution Design
The design of AI solutions is driven by a deep contextual understanding of industry-specific challenges, data ecosystems, and enterprise architecture. Avasant’s approach prioritizes architectural integrity, ensuring each solution operates seamlessly within existing infrastructures while remaining adaptable to future needs. AI Model selection and system design are guided by performance objectives, compliance requirements, and interoperability standards. Emphasis is placed on creating solutions that not only automate tasks but also enhance decision-making at scale—allowing enterprises to unlock actionable insights from complex datasets with precision and speed.
AI Integration and Deployment
Successful AI integration requires more than technical implementation; it demands engineering discipline that aligns AI systems with enterprise workflows, data pipelines, and governance frameworks. Avasant’s deployment strategies ensure that AI models become native to the operating environment—integrated at both process and data layers. AI services are optimized for performance, supporting real-time processing, dynamic scaling, and regulatory compliance. The result is a production-grade AI ecosystem capable of delivering consistent, reliable outcomes while minimizing operational disruptions and preserving data integrity across interconnected systems.
Organizational Change Management
The adoption of AI at scale necessitates deliberate, structured organizational change. Avasant’s approach to change management addresses the systemic impact of AI on people, processes, and decision hierarchies. Change programs are designed to recalibrate enterprise workflows, align leadership and stakeholder expectations, and build institutional capability for AI-driven operations. This includes redefining roles, implementing governance structures, and fostering a culture that is not just receptive to AI but actively leverages its potential. The objective is to ensure that AI adoption translates into measurable business outcomes, rather than remaining a siloed technological experiment.
Maintenance and Enhancement Support
AI systems require continuous oversight to maintain relevance, accuracy, and alignment with evolving business environments. Avasant’s maintenance and enhancement frameworks are built around proactive monitoring of model performance, detection of data drift, and rigorous quality controls. Regular health checks, updates, and system-level refinements are embedded to mitigate emerging risks, prevent degradation, and ensure compliance with shifting regulatory landscapes. This ongoing support enables enterprises to sustain the operational integrity of AI deployments while adapting to new challenges, market conditions, and technological advancements.
Industries We Serve
Ready to Build Your AI Solution?
Case Studies & Success Stories
AI-Enabled Contact Center Transformation in Healthcare
Avasant supported a major U.S. healthcare organization in transforming its contact center operations to handle significantly increased patient interaction volumes, driven by shifts in consumer behavior during and after the pandemic.
The healthcare provider faced a 90% surge in call volumes, outpacing its current capacity and legacy systems. Key performance metrics—such as speed of response, abandonment rate, and resolution time—fell below benchmarks. Additionally, the existing telephony infrastructure was reaching end-of-life, requiring a strategic upgrade aligned with modern patient engagement expectations.
