Invest in high-quality data
Maintaining high-quality data is crucial for the success of any chat data platform.
Humanitarian aid operations often face significant challenges like delays and resource constraints, making it harder to respond quickly and effectively during crises. Traditional methods can be slow due to fragmented data that requires greater manual effort, and are also prone to human error, limiting the scale and impact of relief efforts. Moreover, a lack of humanitarian funding often exacerbates these challenges.
In today’s interconnected world, the complexity and frequency of humanitarian crises require a new approach—one that is agile, data-driven, and scalable. This is where innovative technologies like Gen AI can make a profound difference. By leveraging tools such as AI-powered chat interfaces, automated data processing, and predictive analytics, humanitarian organizations can streamline their operations, accelerate decision-making, and improve service delivery to those in need.
When response time needs to improve, real-time insights, streamlined communication, and intelligent automation becomes more critical than ever. By embracing Gen AI, humanitarian organizations can enhance their operational efficiency, future-proof their efforts, and, most importantly, help more people in need, with greater speed and precision.
Our client faced significant challenges in delivering timely and accurate information to its large field force, which constituted more than 90% of its employee base. The lag in policy information, interpretation, and decision-making impacted millions of lives. This delay was primarily due to multiple revisions in policy documentation over the years and references to myriad sections and annexures. Some of the challenges included:
LTM developed a scalable Gen AI solution, that leveraged Azure OpenAI and other AI services, to enhance information retrieval and operational efficiency for the client. It provided structured information and assistance, fast-tracking fast tracking services that impact human lives services. Key elements of the solution included:
Conversational AI
For efficient knowledge management and real-time insights
Center of excellence
Improved the GenAI solution’s accuracy using innovative technology
Feedback loop mechanism
With Microsoft Azure OpenAI product team to improve search accuracy
Real-time insights and decision support
Improved data accuracy and reliability for decision-making
Telemetry-enabled solution
For real-time usage monitoring
User queries
Focused search and consume data mechanism to eliminate delays
Multilingual data
Both in terms of agents and solutions
Auto indexing
Reflecting any changes in documentation immediately
Research team setup
Explored product capabilities and enabled upcoming requirements
As a chat platform, the solution is multipurpose and has been adopted by various other functions like the HR, finance, external relations, innovation, and IT teams to get an in-depth understanding of spend patterns anomalies and basic policy Q&A, respectively. The beauty of the solution is that it has been built with a customer-first mindset with emphasis on having high quality data in place which is key for success of any chat data platform.
To successfully transition the five Generative AI proof of concepts (POCs) into a production environment, the team adopted an Agile delivery model structured around 32 sprints, each spanning two weeks. This iterative approach enabled continuous development, testing, and refinement of features, ensuring alignment with evolving business requirements and technical feasibility.
Throughout the 64-week timeline, cross-functional collaboration between application architects, program managers, data scientists, Gen AI engineers, infra developers, and DevOps teams facilitated seamless integration of Gen AI capabilities into the enterprise ecosystem.
Regular sprint reviews and retrospectives ensured transparency, adaptability, and incremental value delivery, ultimately culminating in a robust, scalable, and production-ready Gen AI solutions for each BU.

Figure 1: Solution architecture with SharePoint integration
The solution integrated Azure Cognitive Search, Azure OpenAI LLM, and responsible AI practices to ensure a secure, scalable, and user-friendly experience. It provided accurate, reliable, and efficient document interaction capabilities, setting a new benchmark for AI-driven productivity tools in the enterprise landscape.
The solution was architected using a comprehensive and enterprise-aligned technology stack, tailored to fit the business environment and methodologies.
| Python | Quart Web Microframework |
| Azure OpenAI | GPT-3.5/4, Cognitive Search, Semantic Search, Form Recognizer |
| Microsoft | Entra ID, 365 Services, SharePoint Online, Power Automate |
| Azure | Web Apps, DevOps |
The implementation of LTIMindtree’s Gen AI solution has improved the client’s operational efficiency with significant benefits:
Policy Interpretation and Document retrieval process saw huge efficiency
Data analysis and augmented data insights enabled faster and improved decision-making.
Saving manual hours enabled a more efficient and timely response towards human lives and communities.
Reduction in support for complex queries, responding to complex cases, queries, emails, and recurring daily questions.
The implementation of LTM’s GenAI solution has profoundly transformed our client’s operations by streamlining access to critical information and automating key processes, the solution has significantly enhanced the client’s ability to respond to people in need , reducing time spent on administrative tasks and freeing up resources to focus on direct impact. The Gen AI powered approach has also expedited decision-making, and providing 24/7 assistance across time zones, ensuring that help is always available when needed. In an era where every second counts, the ability to leverage real-time insights and intelligent automation is no longer a luxury but a necessity. This innovative solution not only empowers humanitarian organizations to work more effectively but also sets a new standard for how technology can drive sustainable change in the sector.
To help customers start and scale with similar solutions, here are the top five learnings and takeaways:
Maintaining high-quality data is crucial for the success of any chat data platform.
Implementing a PoC, productionizing, and scaling the solution ensures flexibility and adaptability.
Effective search capabilities and prompt engineering are key to obtaining accurate and relevant information.
Managing human interactions with large language models (LLMs) and setting clear expectations are essential for user adoption.
Regularly monitoring the model to prevent it from becoming outdated and keeping track of outcomes via user feedback and performance metrics.
“Our data and integration team is doing great! The second chatbot is out, running on Azure. Great reception and utility across the organization, enabling staff to chat with HR and finance policies, job aids and other documents. It’s been an interesting journey but with a great team and business partners, all so worth it! Kudos to the entire LTIMindtree team!”
Chief BRM Officer, Data and Integration Collaboration and Communications Solutions
“The business ’s Gen AI impementation is a paradigm shift for their organization and the greater humanitarian sector. The solution makes use of Azure OpenAI and other AI services to open new possibilities for improving efficiency, effectiveness, and innovation for similar organizations operating in the humanitarian aid sector.”
Global AI Strategy Lead