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Background
A consulting firm was looking to build their own private AI chatbot for internal business workflows. To do this, they needed to customize large language models (LLMs) to meet their specific research and content generation requirements while also adding an extra layer of security.
They hired Apriorit to develop and design a secure AI chatbot and a web-based app for corporate needs. We also deployed the product in the client’s infrastructure and conducted training for their team on code, deployment, and further improvements.
This AI chatbot case study shows the entire journey, from project discovery to acceptance testing. As a result of our collaboration, we ensured the chatbot’s smooth and secure operation and helped our client stay on top of the AI trend.
Contents:
The client
Our client is an international consulting firm that offers various services including business research, data analytics, content services, and graphic design.
| Our client: | NDA-protected |
| Location: | UAE |
| Industry: | Consulting |
| Solution we delivered: | An AI-based chatbot and a web app for internal research needs |
| Services we provided: | ✅ AI chatbot development ✅ Generative AI development ✅ Web development ✅ Quality assurance ✅ DevOps assistance |
The challenge
To streamline their research processes, our client wanted a custom AI chatbot for corporate needs that could work with uploaded files.
Since they deal with lots of sensitive data, the client was also looking to ensure proper data protection and enable their employees to use AI-based tools within secure corporate infrastructure. Additionally, they wanted to make sure that all search history and generated information remained private and was never shared with third-party LLM providers in order to safeguard their business advantage.
They entrusted Apriorit with the following tasks:
- Develop an AI chatbot that supports file uploading
- Establish data privacy and security measures for the chatbot
- Ensure the chatbot can flawlessly support up to 15 users simultaneously
- Create a simple but user-friendly web interface for the chatbot and admin dashboard
- Deploy the project in the client’s Google Cloud Platform infrastructure
While our client had explicit requirements for project features and privacy options, they asked Apriorit to choose the technology stack.
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The result
Our client received a corporate AI chatbot that enhances employee performance during research activities.
The Apriorit team successfully delivered a stable and scalable chatbot solution with necessary security measures in place. We also smoothly integrated the project into the client’s existing infrastructure.
Being able to share files with the chatbot allows the client’s employees to improve the research process and achieve even more insightful summarizations. Yet all prompts, files, and generated information remain private and stay within the client’s corporate environment.
They [Apriorit] delivered a working product and helped us deploy it in our infrastructure. Once we put the project live, it was working perfectly.
How we did it
After discussing project details with stakeholders, Apriorit gathered a development team and outlined the work plan. We decided to go with Python, Gemini LLM, LangFuse, and other supplementary technologies that would be the best fit considering the client’s requirements.
The client collaborated with us using the fixed price model.
During this project, Apriorit performed several demo presentations to give a detailed overview of new features. We also conducted QA sessions while developing and fine-tuning the solution.
Stage 1. Project discovery
We started work with essential project discovery activities:
- Conducted meetings with stakeholders to understand the project vision and main requirements
- Identified key use cases for the chatbot: research, summary generation, etc.
- Defined users’ roles and pain points
- Outlined chatbot functionalities, workflows, and integrations in a clear software requirements specification and approved it with stakeholders
All information gathered during this stage helped us clearly understand how to align the corporate chatbot solution with customer expectations and business goals.
Stage 2. Architecture design
The developed specification was crucial for reducing project risks. It helped us clarify all possible details before moving to development and prepared us for the following activities:
- Designing the system architecture, including the AI model, APIs, and cloud infrastructure
- Ensuring that all prompts provided to and information generated by the chatbot will remain private
- Planning a foundation for future project enhancements and additional capabilities
As a result of these activities, our team provided the client with a clear project roadmap. We also approved the architecture design with stakeholders, confirming its feasibility and alignment with business goals.
Stage 3. Chatbot development
With all preparation done and documentation approved, the Apriorit team proceeded to project development:
- Built the first chatbot version with core conversational capabilities
- Implemented the file uploading feature
- Evaluated chatbot performance under different workloads (in the testing environment)
- Finalized a functional prototype to receive early feedback
- Presented a demo to stakeholders and answered their questions
When evaluating performance, we analyzed how the chatbot handled different numbers of simultaneous users and requests — and how much it cost to do so. This helped us ensure we met the client’s performance requirements.
Stage 4. Deployment
Once the Apriorit team had fine-tuned prompts and introduced other changes, according to the client’s post-demo requests, it was time to implement the developed solution. Here’s what we did:
- Deployed the chatbot in a live production environment
- Configured the LangFuse admin dashboard to track chatbot performance and user interactions
- Assisted the client’s team with continuous support for project infrastructure, deployment, and code
After that, the project was ready for the final touch ー quality assurance acceptance.
Stage 5. Acceptance & performance testing
To make sure the project performed as intended, our QA and development specialists:
- Conducted rigorous functionality, usability, and security testing
- Performed load testing to assess chatbot performance under high traffic volumes
- Ensured compliance with predefined acceptance criteria before the full rollout
- Addressed bugs and optimization opportunities based on test results
With robust QA testing and optimization, the final solution ensures reliable performance, efficiently handling peak loads and delivering a seamless user experience.
The impact
With our team’s efforts on their side, our client successfully kicked off their initiative to create their own corporate AI infrastructure. Now, they have a well-designed corporate AI chatbot that brings improved summarization and content generation efficiency to employees’ research workflows.
This custom solution permits our client to leverage generative AI capabilities without putting sensitive data at risk. Employees can now provide internal business information and documents to the client’s chatbot, knowing that all prompts and generated content remain private.
Apriorit’s availability and commitment to making things work stood out. […] If we need to work again and have the budget, Apriorit will definitely be one of our first companies to go to.
Apriorit ensured a perfect experience with up to 15 simultaneous chatbot users, as requested by the client. Our team also made sure the chatbot would be able to maintain stable performance even when working with up to 50 simultaneous users.
Since we knew the client’s plans for further project expansion, we designed the architecture to allow for adding new features down the line: RAG knowledge-base implementations, access management system integration, and support for more file formats.
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