Inference Soft offers a comprehensive suite of AI and technology services, including: Generative AI Solutions, Large Language Model (LLM) Development & Fine-Tuning, Retrieval-Augmented Generation (RAG) Systems, Agentic AI & Autonomous Agent Development, Custom Machine Learning (ML) Model Development, MLOps & DevOps Implementation and Consulting Our goal is to provide end-to-end solutions, from strategy and development to deployment and ongoing management.

Our main operations and expert team are based in Ghaziabad, Uttar Pradesh, India. We are proud to serve clients both locally in India and internationally.

We pride ourselves on a combination of deep technical expertise, a client-centric approach, and a commitment to delivering tangible business value.

Key differentiators include:

End-to-End Solutions: We cover the entire lifecycle from ideation to production.

Customization: We tailor solutions to your specific business needs rather than offering one-size-fits-all products.

Expert Team: Our team consists of experienced AI specialists, data scientists, and MLOps/DevOps engineers.

Focus on Responsible AI: We are committed to ethical development and deployment practices.

Local Presence, Global Standards: We offer the accessibility of a local partner with adherence to international best practices.

Our AI and technology solutions are adaptable across a wide range of industries, including but not limited to: Finance (FinTech), Healthcare, Retail & E-commerce, Manufacturing, Marketing, Software Development, and more. We focus on understanding your specific industry challenges to deliver relevant solutions.

The best way to start is by contacting us for an initial consultation. We'll discuss your business needs, potential use cases, and how our services can help you achieve your goals. You can reach us via the contact details on our website.

Data privacy and security are paramount in all our projects. We adhere to industry best practices and relevant data protection regulations. Our approach includes secure data handling protocols, access controls, and designing systems with privacy-preserving techniques where applicable. We are happy to discuss specific security measures related to your project.

Generative AI refers to artificial intelligence systems that can create new and original content, such as text, images, audio, code, and synthetic data. These models learn patterns from vast amounts of existing data and use that knowledge to generate novel outputs.

Generative AI can offer significant benefits, including:

Automating content creation (marketing copy, reports, drafts).

Enhancing creativity and idea generation.

Personalizing customer interactions at scale.

Improving productivity by assisting with tasks like coding or summarization.

Creating synthetic data for testing and training other AI models.

Yes, we can develop custom Generative AI solutions. This often involves fine-tuning existing state-of-the-art foundation models (like LLMs) with your specific data to create applications tailored to your unique requirements and domain.

: LLMs are a type of Generative AI specifically trained on massive amounts of text data. They excel at understanding, processing, and generating human-like language. They can perform tasks like translation, summarization, question answering, text generation, and more.

We offer comprehensive LLM services, including:

Strategic consulting to identify LLM use cases.

Selecting the right LLM (proprietary or open-source) for your needs.

Fine-tuning LLMs with your data for domain-specific accuracy and style.

Expert prompt engineering to optimize LLM outputs.

Integrating LLMs into your applications and workflows.

This is a critical consideration. We employ several strategies, including:

Retrieval-Augmented Generation (RAG): Grounding LLM responses with factual information from your verified knowledge bases (see RAG section below).

Careful Prompt Engineering: Designing prompts that guide the LLM towards accurate and relevant outputs.

Fine-tuning: Training the LLM on specific, high-quality data to improve its factual accuracy within a domain.

Fact-checking and Validation Layers: Implementing mechanisms to verify information where critical.

RAG is an AI architecture that enhances the capabilities of Large Language Models (LLMs) by connecting them to external knowledge bases. Before generating a response, a RAG system retrieves relevant, up-to-date information from these sources and provides it to the LLM as context.

RAG is crucial because it helps address key limitations of LLMs, such as:

Knowledge cut-offs (LLMs are only aware of data up to their last training date).

Tendency to "hallucinate" or generate plausible but incorrect information.

Lack of access to proprietary or domain-specific information. RAG makes LLM outputs more accurate, reliable, and current.

Our team can help you build RAG systems that leverage a wide variety of your company's structured and unstructured data, including internal documents (PDFs, Word docs), databases, website content, product manuals, FAQs, and more. The goal is to make your specific knowledge accessible to the LLM.

Agentic AI refers to AI systems (agents) that can autonomously perceive their environment, reason, make decisions, create plans, and take actions to achieve specific goals. While Generative AI focuses on creating content, Agentic AI focuses on doing things and executing tasks, often using tools (which could include Generative AI models).

AI agents can be designed for a wide range of tasks, such as:

Automating complex business processes.

Interacting with software applications or APIs on a user's behalf.

Managing customer inquiries and performing follow-up actions.

Proactively monitoring systems and responding to events.

Coordinating tasks within a multi-agent system.

Safety and reliability are paramount. We design AI agents with clear operational boundaries, robust error handling, monitoring systems, and human oversight mechanisms. Our development process includes rigorous testing and validation to ensure agents perform as expected and adhere to defined governance protocols.

Think of it as a team of smart computer programs (agents) working together to solve problems or achieve goals. Each agent can act on its own but also communicates and coordinates with others.

Machine Learning is a field of AI where systems learn from data to identify patterns, make predictions, or automate decisions without being explicitly programmed for each specific task. It encompasses various techniques like supervised learning (e.g., classification, regression), unsupervised learning (e.g., clustering), and reinforcement learning.

ML can address a vast array of business problems, including:

Predicting customer churn or sales trends (predictive analytics).

Identifying fraudulent transactions (anomaly detection).

Segmenting customers for targeted marketing (clustering).

Automating image recognition or document classification.

Optimizing supply chains or recommending products.

A typical ML project follows a structured lifecycle:

Business Understanding & Problem Definition

Data Acquisition & Exploration

Data Preparation & Feature Engineering

Model Selection, Training & Tuning

Model Evaluation & Validation

Deployment & Integration

Monitoring & Maintenance (often managed via MLOps practices)

DevOps is a set of practices that combines software development (Dev) and IT operations (Ops) to shorten the development lifecycle and provide continuous delivery with high software quality, emphasizing automation and collaboration.

MLOps (Machine Learning Operations) applies DevOps principles to the Machine Learning lifecycle. It addresses unique ML challenges like data/model versioning, continuous training (CT), model monitoring for performance drift, and ensuring reproducibility. Essentially, MLOps is DevOps tailored for the specific needs of AI/ML systems

In today's fast-paced environment, MLOps and DevOps are essential for:

Speed: Faster delivery of software updates and ML models.

Reliability: Ensuring stability and reducing errors in production.

Scalability: Building systems that can grow with demand.

Efficiency: Automating manual processes and optimizing resources.

Collaboration: Breaking down silos between development, operations, and data science teams.

Governance & Reproducibility: Particularly for MLOps, ensuring models are auditable and perform consistently.

Our experts can help you assess your current practices, design a tailored MLOps/DevOps strategy, select and implement the right tools and automation pipelines, and train your teams. We focus on creating efficient, scalable, and reliable operational workflows for both your software and AI initiatives.