Accelerators

Generative AI
Proof of Concept

Our Clients

Bringing Endless Opportunities with Generative AI

Generative AI apps like ChatGPT, GitHub Copilot, and Stable Diffusion are transforming how we interact with technology, unlocking vast possibilities for innovation.

Our Generative AI Proof of Concept program rapidly prototypes use cases to assess viability, ROI, and technical feasibility, demonstrating the value and potential of Generative AI for your business.

Our Approach

1 Week

Readiness

Define PoC requirements, KPIs, and success criteria.

Identify and validate business use cases.

Determine the target audience.

Validate data availability and quality, and readiness.

Finalize the scope for proof-of-concept

Develop requirements document

7 Week

Proof-of-concept Excecution

Design and build PoC infrastructure

Data Ingestion, transformation and validation

Design and build data annotation and curation infrastructure

LLM Model Identification and selection

Final technical architecture

Fine-tune pre-trained models with customer data

Develop and optimize prompts for the use case

Deploy and training model

Deploy the model and expose model API

Integrate the API with application per use-case Perform LLM testing

Benefits

Engagement Details

Engaging Model

Define Requirements: Collaborate to set KPIs and success criteria.

Validate Use Case: Confirm Generative AI use case and business needs.

Data and Infrastructure Analysis: Check data availability, quality, sources, and readiness for model training.

Set Up PoC Infrastructure: Establish an environment for model training and deployment.

Select Foundation Models: Choose models like GPT 3.5/4.0, Llama 2, based on use cases and data.

Optional: Vector Database: Load specific data for in-context prompting.

Model Fine-Tuning: Optimize model and prompts for the use case.

Deliverables

Define Requirements: Collaborate to set KPIs and success criteria.

Validate Use Case: Confirm Generative AI use case and business needs.

Data and Infrastructure Analysis: Check data availability, quality, sources, and readiness for model training.

Set Up PoC Infrastructure: Establish an environment for model training and deployment.

Select Foundation Models: Choose models like GPT 3.5/4.0, Llama 2, based on use cases and data.

Optional: Vector Database: Load specific data for in-context prompting.

Model Fine-Tuning: Optimize model and prompts for the use case.

Highlights

Provides quick, accurate, and personalized responses, improving overall satisfaction.

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Client Stories

Success Stories

Interactive, personalized, and automated learning, teaching, and assessment processes were developed and enabled for an online learning platform supporting K12 education using Generative AI. The solution leveraged and connected with the client’s data sources to generate the responses.

A generative AI powdered audience persona builder and recommendation engine was developed for one of the world's top media and advertising companies. It helps redefine campaign creation, audience segmentation and selection, and operations as tasks of minutes rather than days.

USA-based retail and commerce company leveraged Generative AI solutions to automate marketing content creation and its SEO optimization that replicates the brand’s unique writing style and tone. It enhances content production and publishing efficiency, ensuring consistent quality while scaling up content generation to meet timely marketing objectives.

Industries We Serve

Serving Industries

From healthcare to finance, we’re helping industries grow with tailored AI solutions.

Healthcare

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