Contextual NLP Chatbot for Neurodivergent Employees
An NLP-powered chatbot that provides personalized workplace guidance and real-time support for employees on the autism spectrum.
At a Glance
The client wanted to create an inclusive workplace support system for employees on the autism spectrum. Maruti Techlabs developed a contextual NLP chatbot that understands employee intent, retains conversation context, and provides personalized guidance.
Industry
Healthcare
Function
Chatbot
Solution
Contextual NLP Chatbot
Engagement
AI/ML Development
The Client
Our client is a B2B SaaS platform that improves traditional hiring practices through ethical AI and data science. Its platform aims to reduce unconscious bias, improve workplace happiness, and create better matches between employees and employers.
The Challenges
Creating More Context-Aware Workplace Conversations
The chatbot needed to understand what an employee meant within the context of an ongoing conversation. A response based only on the latest message would not be sufficient for situations involving multiple questions or follow-ups.
Employee Queries Needed Context
The chatbot had to understand each user's situation and use relevant information from earlier in the conversation.
FAQ Responses Had Limited Relevance
The existing chatbot was built around standard questions and couldn't effectively address the more specific challenges autistic employees face.
Workplace Topics Varied Widely
Onboarding, teamwork, communication, work arrangements, and health-related concerns required different intents and conversational responses.
Conversations Needed to Feel Natural
The chatbot needed to move beyond scripted interactions and respond in a way that felt relevant, supportive, and less robotic.
Create AI chatbots that understand user intent and respond with greater contextual relevance.
Build conversational experiences that support users across complex, multi-step interactions.
The Solution
Building Context Into Conversational Support
Maruti Techlabs built a text-based NLP chatbot from scratch, focusing on intent recognition, conversation context, and personalized responses for workplace situations.
Maruti Techlabs built a text-based NLP chatbot from scratch, focusing on intent recognition, conversation context, and personalized responses for workplace situations.
Maruti Techlabs built a text-based NLP chatbot from scratch, focusing on intent recognition, conversation context, and personalized responses for workplace situations.
The Journey
Concept to Deployment
Business Outcomes
Making Workplace Support More Accessible
150+ Employees Supported
The chatbot serves as a virtual mentor for more than 150 neurodivergent employees.
Personalized Workplace Guidance
Employees receive relevant insights and real-time support as they navigate workplace challenges.
Greater Sense of Belonging
The chatbot helps employees with varying degrees of autism improve their social skills, feel more welcomed, and develop a stronger sense of belonging at work.
More Inclusive Hiring
Dedicated support can encourage organizations to hire more neurodiverse talent and create more inclusive workplaces.
30+ Organizations Reached
The chatbot was rolled out to more than 30 organizations during the first phase of the project roadmap.
Expanded Employment Opportunities
The initiative supports greater employment opportunities for differently-abled candidates by helping organizations create more accommodating workplace environments.
Conclusion
The NLP chatbot created a more personalized support channel for neurodivergent employees, helping them address workplace situations through contextual conversations and real-time guidance.
Tech Stack
Build contextual NLP chatbots that understand user intent and deliver more relevant conversations.
Create inclusive digital experiences with AI engineering expertise, structured model training, and context-aware conversational design.
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Frequently Asked Questions
A contextual chatbot considers the user's current input alongside relevant information from previous interactions. This allows the system to identify intent within the broader conversation rather than treating every message as an independent query.
There is no fixed number because intent requirements depend on the number and complexity of supported scenarios. For this solution, we trained more than 160 intents to cover different employee needs across multiple workplace themes.
Sensitive use cases require carefully defined conversation boundaries, controlled responses, and clear escalation paths. A chatbot should provide appropriate guidance within its intended scope rather than attempting to replace qualified human professionals.
An NLP PoC allows teams to test whether the model can recognize the required intents and produce appropriate responses using a limited set of conversations. It also gives stakeholders an opportunity to validate the conversational approach before expanding development.
Start by separating the required interactions into distinct conversation themes, then identify the intents and representative utterances within each theme. Training across these variations helps the model distinguish between similar inputs while maintaining the broader conversation context.
Conversation data can reveal missed intents, ambiguous user statements, and situations where the chatbot loses context. Organizations can use these findings to introduce new intents, refine utterances, update context logic, and retrain the model as new scenarios emerge.