Generative AI – Aganyta

Gen AI - Game Changer

Innovation Catalyst

Custom Language Models

Fine tune for Business Data

Semantic Relationships

Foundation of Language Models

AI Augmented Development

Code Generation

Jevons Paradox

AI, Efficiency, More Jobs

Generative AI Services

Advisory

AI Strategy
AI Business Alignment

AI Opportunity Analysis
Data and Human Oversight
Regulatory Guidance
Responsible AI
Risk Governance

Models

Technology Selection
AI Infrastructure Evaluation
Language Model Selection
Business Feature-Model Mapping
Language Model Cost Benefit
Fine Tuning Strategy
Gen AI Tool Selection

Applications

Gen AI Architecture
Multi-Modal Language Models
Gen AI Data Pipelines
Gen AI Agent Design
Fine Tune using Business Data
Model Inference and Integration
Knowledge Graphs

Data Strategy

Data Platform Selection
Data Vectorization
Data Governance
Prompt Engineering
Fine Tuning Data
Data Lineage
Synthetic Data Generation

Operations

Model Life Cycle Management
Model Quantization
Model Optimization
Model Cost Monitoring
Model Performance Metrics
Model Response Caching
Bias and HAP Detection

SDLC

AI Assisted Coding
Test Code Generation
Test Case Writing
Product Documentation
User Story Generation
Data Model Authoring
Performance Monitoring

Adapt Gen AI to Business?

Pre-trained Large Language Models (LLMs) offer falter with targeted customer use cases, leading to inaccurate responses, known as “Hallucination.”

Solution: Fine-tune LLMs

Enterprises can fine-tune LLMs with smaller, domain-specific datasets to reduce inaccuracies and to accurately capture domain intricacies.

Aganyta Expertise – Adapt LLMs

Adaptation strategies like prompts, contextual learning, text retrieval augmentation, fine-tuning, reinforcement learning, and vector embeddings.

Challenges with adapting LLMs

Overfitting, catastrophic forgetting, and the high cost associated with adapting LLMs.

Optimize Gen AI Investment?

Aganyta helps businesses adapt Large Language Models (LLMs), but we go further to de-risk projects and enhance ROI of AI investments.

Small Language Models (SLMs)

SLMs offer flexibility and control to fine-tune to specific tasks or domains. They are cost-effective to train, deploy, and operate compared to LLMs.

Domain Specific Small Language Models

These models are tailored to particular domains or industries, allowing for more precise and effective performance within specific contexts.

Multiple Language Models

Organizations can orchestrate multiple Language Models, each tailored to specific tasks.

AI Agents – Autonomous Reasoning