Generative AI Services & Solutions

Reimagine your enterprise with Generative AI – unlock innovation, automate intelligence, and transform the future of work.

Generative AI Services and solutions

Industries We Serve Gen AI Services

BFSI (Banking, Financial Services, and Insurance)

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Capital Markets

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TMT (Telecom, Media & Entertainment, & Technology)

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Other Industries

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Generative AI Solutions

Large language models (LLMs) and, more broadly, generative AI (Gen AI) have taken the world by storm, revolutionizing the way businesses operate. They enable enterprises to automate complex processes, enhance creativity, and drive intelligent decision-making.

By leveraging advanced machine learning (ML) models, Generative AI can generate human-like text, code, images, and even strategic insights, reducing operational bottlenecks and accelerating innovation. From personalized customer experiences to automated content generation and predictive analytics, Generative AI empowers organizations to scale efficiently while maintaining a competitive edge. As enterprises reimagine their workflows, Gen AI serves as the catalyst for a smarter, faster, and more adaptive future.

How Gen AI Works:

A Step-By-Step Guide

Gen AI uses deep learning techniques to create human-like content, automate tasks, and generate insights. It relies on neural networks trained on vast datasets to understand patterns and produce coherent responses. Here’s how it works:

1. Data Ingestion & Preprocessing
2. Training Deep Learning Models
3. Generating Content With Transformer Architectures
4. Fine-Tuning & Adaptation
5. Contextual Understanding & Retrieval-Augmented Generation
6. Continuous Learning & Adaptation
step 1

Data Ingestion & Preprocessing

  • Gen AI models learn from massive datasets, including text, images, code, and structured data.
  • Data is cleaned, structured, and tokenized to remove inconsistencies and optimize learning.

Issues

Generic models are trained on broad, publicly available data, which may not fully align with specific enterprise requirements.

step 2

Training Deep Learning Models

  • Large-scale models (such as GPT, DALL·E, and Stable Diffusion) use neural networks to identify patterns and relationships within data.
  • These models are trained using billions of parameters to improve accuracy and coherence.

Issues

Pretrained models may lack industry-specific knowledge and could generate inaccurate or irrelevant content without further fine-tuning.

step 3

Generating Content With Transformer Architectures

  • Most modern Gen AI models use transformer architectures (e.g., GPT, BERT, T5) that understand context and generate text, images, or code dynamically.
  • The model predicts the next word, pixel, or data point based on previous inputs, creating contextually relevant outputs.

Issues

Models may hallucinate (generate false or misleading content) due to training limitations and a lack of real-world validation.

step 4

Fine-Tuning & Adaptation

  • Enterprises can fine-tune AI models using proprietary datasets to align outputs with specific industry needs.
  • Reinforcement learning (e.g., reinforcement learning from human feedback [RLHF]) improves model behavior and accuracy.

Issues

Without fine-tuning, models may produce biased, outdated, or incorrect information.

step 5

Contextual Understanding & Retrieval-Augmented Generation

  • AI models can integrate retrieval-augmented generation (RAG) to fetch real-time, relevant data from external sources.
  • This approach enhances accuracy by combining pre-trained knowledge with up-to-date enterprise data.

Issues

Standard AI models operate in a closed loop, leading to outdated responses and a lack of real-world awareness.

step 6

Continuous Learning & Adaptation

  • Gen AI models can be retrained or fine-tuned over time to improve accuracy and relevance.
  • Feedback loops help AI systems evolve based on user interactions and changing business needs.

Issues

Most generic AI models do not self-improve unless explicitly retrained, leading to stagnation in quality over time.

Despite significant advancements in state-of-the-art LLMs and Gen AI tools, off-the-shelf models often fail to meet enterprise needs due to limitations in customization, real-time adaptability, and domain-specific expertise. To fully harness the power of Gen AI, businesses require tailored solutions, fine-tuning, and continuous optimization. SG Analytics (SGA) partners with clients across industries to bridge these gaps, delivering generative AI solutions that are precise, scalable, and effective.

Generative AI Services and Solutions

At SGA, we have executed multiple Gen AI projects, ranging from data extraction to research agents and co-pilots that assist with various tasks within organizations. For our enterprise clients, we provide:

AI Strategy & Consulting

  • AI roadmap development.
  • Use case identification & feasibility analysis.
  • AI ethics & governance frameworks.

Custom AI Model Development

  • Fine-tuned LLMs for domain-specific applications.
  • Custom chatbot & virtual assistant development.
  • AI-driven content and image generation.

Enterprise AI Integration

  • Embedding AI into existing business workflows.
  • API integration with enterprise systems (CRM, ERP, etc.)
  • Intelligent automation & RPA augmentation.

AI-Powered Analytics & Insights

  • Predictive analytics & forecasting.
  • AI-driven data visualization & reporting.
  • Automated knowledge management.

AI-Augmented Creativity & Content Generation

  • AI-powered marketing & ad content creation.
  • Automated report & document generation.
  • AI-enhanced design & media production.

Responsible AI & Compliance

  • AI fairness, bias mitigation, and transparency.
  • Compliance with industry regulations (GDPR, HIPAA).
  • AI security & risk management.

AI Testing & LLM Ops

  • Model evaluation for accuracy, bias, and robustness.
  • AI performance monitoring & drift detection.
  • Scalable LLM deployment & lifecycle management.
  • Automated testing for AI-driven applications.

AI Training & Enablement

  • AI literacy programs for enterprises.
  • Custom training datasets & AI model refinement.
  • AI adoption workshops for leadership & employees.

Lifecycle of Gen AI Projects

Choice of Use Case
  • Automated process discovery/ mining
  • Design thinking for new/digital process design
  • Cost-benefit analysis
Choice of LLM
  • Chat GPT vs. LLAMA or other open-source models
  • Cost-benefit analysis
  • Gen AI architecture design
Evaluation metrics
  • Output accuracy and relevance
  • Cost-performance efficiency
  • Task success rate (e.g., completions, satisfaction)
Combining ML + GEN-AI

Probabilistic, forecasting, personalization, valuation, BRE based models in conjunction with LLMs

Contextualizing LLMs

RAG based approaches for rapidly changing data

Fine Tuning LLMs

Parameter tuning for directed outcomes ( brand guidelines, tone, nuances etc.) Smaller models

Guardrails
  • Hallucination prevention
  • Data security & privacy
  • Compliance
  • Human in the loop (RHLF)
Deployment
  • AI testing/Prompt testing
  • Data Ops
  • ML Ops
  • Infrastructure and cost management
Choice of Use Case
  • Automated process discovery/ mining
  • Design thinking for new/digital process design
  • Cost-benefit analysis
Choice of LLM
  • Chat GPT vs. LLAMA or other open-source models
  • Cost-benefit analysis
  • Gen AI architecture design
Evaluation metrics
  • Output accuracy and relevance
  • Cost-performance efficiency
  • Task success rate (e.g., completions, satisfaction)
Combining ML + GEN-AI

Probabilistic, forecasting, personalization, valuation, BRE based models in conjunction with LLMs

Contextualizing LLMs

RAG based approaches for rapidly changing data

Fine Tuning LLMs

Parameter tuning for directed outcomes ( brand guidelines, tone, nuances etc.) Smaller models

Guardrails
  • Hallucination prevention
  • Data security & privacy
  • Compliance
  • Human in the loop (RHLF)
Deployment
  • AI testing/Prompt testing
  • Data Ops
  • ML Ops
  • Infrastructure and cost management

Why Choose SGA for Gen AI Services & Solutions?

Proven Technical Expertise
  • Deep expertise in advanced LLMs, AI model fine-tuning, and custom AI development.
  • Experience with leading AI frameworks and architectures, including GPT, BERT, and RAG.
Industry-Specific Experience
  • Successful AI implementations across multiple industries, including BFSI and Media clients.
  • Domain-focused generative AI solutions tailored to specific business challenges.
Real-World AI Implementation
  • We don’t just build AI for clients – we use it internally to optimize our own operations.
  • Proven success in automating workflows, enhancing analytics, and driving efficiencies within SGA.
Customized, Scalable Solutions
  • Our solutions are fine-tuned for accuracy, adaptability, and real-time relevance.
  • Scalable AI models designed to evolve with business needs.
End-To-End AI Enablement
  • From strategy to deployment, we guide enterprises through the entire AI journey.
  • Ongoing monitoring, optimization, and governance to ensure long-term AI success.

Gen AI Ins(AI)ghts

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How Enterprises Are Using Generative AI

The upcoming phase of enterprise generative AI will be less about the sheer number of applications and more about how cleverly companies integrate them. The AI adoption curve in enterprises is also far different than any other technology transition trend in recent memory. Unlike past waves of automation or analytics tools that took years to diffuse, generative AI is being absorbed at a breakneck pace worldwide.

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Generative AI is Increasing Employee Productivity and Expanding Capabilities

The power of generative AI is as strong as people’s willingness to integrate it into their operations. Oftentimes, organizations are focused on how generative AI can help simplify and automate everyday processes. While such measures can increase productivity, executives are overlooking the rare opportunities to revolutionize the potential of their employees and pioneer a new future for their organization.

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