Reinvent
Reimagine
Reshape
everything*
* Responsibly
Our AI Philosophy
While buzzwords and notions of the next best thing is not new in the technology landscape, we can certainly say that’s not the case with Artificial Intelligence (AI) and Machine Learning (ML). They have been around for a very long time (long before ChatGPT became a household name) and with the emergence of Generative AI and Agentic AI, we are seeing wide-scale adoption of AI tools and technologies in almost all industries, from manufacturing to legal.
Here are the main guiding principles we follow when recommending a solution.
Product Comes First
We collaborate with you to adopt a Product-First strategy. AI should serve your product, not the other way around.
Think Business Continuity
Never develop solutions and products relying heavily on closed source vendor technologies.
Context over Quantity
Garbage In = Garbage Out. Data with context is what actually makes the difference. Focus on Data Quality and MLOps.
Adopt Responsibly
Don't fall for gimmicks and chest-thumping claims. Evaluate privacy, operating costs (hidden fees) and ROI before adopting something.
AI Consultation
For your AI adoption process to be successful, you will need to look beyond some quick AI features and widgets. This is a typical challenge faced by many Generative AI startups. How do you go from an initial feature to something more long-term? Having a long-term vision is the key ingredient required for defining an AI driven growth strategy.
The Product First Strategy
Our AI consultants will look at your business from the proverbial 30,000 feet, do a competitor analysis, look at your long-term objectives, and define a 5-year and 10-year road map. Our approach to AI consultation is to provide agonostic, unbiased and authentic advisory services rooted in a Product First strategy. This is vastly different from creating complex architectures, overloading you with jargon, talking about AI tools and regurgitating stuff repeated by AI vendors. The truth is, an 'AI First,' 'Data First,' or 'Technology First' strategy will not drive success. Your guiding principle should always be a Product First approach.
Key Questions We Explore:
- What does the business truly need?
- What opportunities do business leaders see today?
- Where does AI and Technology fit into these opportunities?
- Will AI add real value to customers—or will it just be a gimmick?
- Are there measureable returns?
How We Can Support You:
✔️ Define a Product-First approach to AI adoption
✔️ Evaluate the long-term capabilities for sustainability
✔️ Monetize AI through scalable strategies
✔️ Implement Robotic Process Automation (RPA) for efficiency gains
✔️ Conduct Techninal Impact Assessment before AI adoption
✔️ Establish AI Governance and LLMOps best practices
✔️ Guide you on Model Selection for optimal outcome
Success comes from putting the product at the center; AI should serve your product, not the other way around.
Ready to rethink AI with a Product-First mindset?
Data Engineering
Data and AI are two sides of the same coin. Using AI when it comes to productivity boosting tools like content creation and basic chat bots is relatively straightforward. However, it requires careful planning, strategizing and above all, good data with context to realize the true transformative power of AI.
Data Preparation for AI
A common misconception is that more data yields better results when using AI/ML algorithms. In reality, what truly matters is the quality of cleansed data for creating training sets. The other common misconception is that data preparation is a one-time task. As your Machine Learning models evolve, or newer data becomes available, you will often need to refine your data cleansing steps.
Mantrax can help you with your data preparation process in the following ways:
- Defining your data ingestion process and framework
- Development of Rule-Based Systems to cleanse your data
- Using Foundation Models (like LLMs) to cleanse your data
- Data Virtualization (we are particularly fan of Lakehouse Federation)
Design. Deploy. Sustain.
Data ingestion and cleansing, MLOps, etc requires data pipelines and other supporting infrastructure. Whether you choose to build your data pipelines in cloud or on-premise, we have a long history of infrastructure creation and deployment. When it comes to infrastructure, we put a lot of thought into disaster recovery, log monitoring, notifications and operating cost. And yes, infrastructure creation is always done idempotently through scripts (not manually).
LLM Services
Model Selection
With new Large Language Models (LLMs) coming out every now and then, you might be wondering which LLM is the most suitable for your use case. There are various factors to consider when choosing an LLM.
- Model Size and Performance
- Domain-Specific Training
- Bias (Political, Cultural etc.)
- Support and Update Frequency
- Privacy (for API based LLMs)
- Cost
LLM Customization
Once you have selected a Large Language Model (LLM) for your use case, you will need to customize it for it to give you unhalucinated, useful information. The customization will depend on your specific use case and industry. Here are the steps typically taken:
Define Objectives & Use Cases
Customer Support, Content Generation, Code Generation, Coding Assistance, Domain Specific Research
Select the Right Base Model
Oper Source vs. Proprietary Models, Cost, Latency & Performance
Retrieval Augmented Generation (RAG) and Embeddings
Use private knowledge to improve LLM's response. Use Vector databases to save business specific knowledge.
Data Collection and Data Wrangling
Domain specific data, Pipelines, Data Cleansing, Normalize to remove any biases
LLMOps and AI Governance
Logging and Monitoring, Prevent biases & hallucinations, Ensure Compliance, Updates and Retraining
Fine-tuning (when necessary)
Supervised Fine-Tuning, Reinforcement Learning, Low Rank Adaptation
Prompt Engineering
Provide Context, Code Sample, Writing Styles, Company Culture etc. to improve LLM output.
Feedback and Refinement
Obtain feedback from users and retrain the model as rquired.
True power of AI can only be harnessed iteratively, not surgically.
Agentic AI
The rise of Agentic AI has ushered in a new era of productivity gains when it comes to software development and other process automation. Large Language Models (LLMs) are designed to read natural language prompts, analyze them using the given context, and provide some output using its training data. The training data is not real-time and does not have rule-based training. Therefore, it cannot be reliably used for traditional programming which is highly structured, deterministic and verifiable. Sure you can generate some code samples using LLMs, but the accuracy cannot be assessed without human intervention.
Enter Agentic AI.
Agentic AI is a hybrid system which uses LLMs for code generation, but also uses rule-based AI training to deliver more reliable and verifiable instructions to perform tasks autonomously, often eliminating the need for human intervention.
Some Business Use Cases for Agentic AI
✔️ Real-time log monitoring
IoT sensor data, website logs etc.
✔️ Supply Chain Order Management
Check inventory levels and place orders with suppliers based on predefined rules.
✔️ Online Fraud Prevention
Monitor refund transactions on your payment gateway to detect anomalies.
✔️ Unit Testing Software
Once the code has been written, use Agentic AI to automate Unit Testing
✔️ Data Ingestion
Validate data for batch or stream processing
For organization performing lots of manual repititive tasks, performance gains using Agentic AI is real and worth exploring.
