Generative AI Consulting


Accelerate your digital transformation and increase productivity with domain-specific Generative AI solutions.


Business benefits

What you can get with Generative AI solutions


Gen AI in Business
Domain-Specific Gen AI
Enhanced Safety
Generative AI Strategy
Gen AI Proof of Concept (PoC)

Safety and Effectiveness


Generative AI speeds up the creation of new products, improves customer interaction with a company, and helps workers do their jobs better. However, it’s important to know its limits and use it safely.

Thanks to our in-depth experience in delivering tailor-made AI projects, we can help you get the most out of Gen AI while limiting its risks.

  • Automating and accelerating processes with industry-specific Gen AI agents
  • Improving products with fine-tuned LLMs align with specific business logic
  • Empowering customer satisfaction with scalable AI-driven personalization
  • Reducing costs by equipping your employees with user-friendly and reliable AI-powered co-pilots

Precision Customization for Industry-Specific Usage


Domain-specific models are trained on data specific to a particular industry or domain, allowing them to better understand the context, terminology, and nuances of that domain. This results in more accurate and relevant outputs tailored to the specific needs of the business.

For example, a domain-specific model for the insurance industry can comprehend insurance jargon and process claims more accurately by analyzing policy details, damage assessments, and relevant regulations


Improved Data Privacy and Reduced Hallucitantions


Our Gen AI consulting service prioritizes the integrity of your proprietary data by training AI models on your unique datasets, safeguarding against privacy breaches. With our domain-specific approach, we address biases and fabrications, ensuring reliable and accurate outputs essential for trustworthy AI applications.

This approach effectively addresses and mitigates prevalent concerns around the potential exposure of sensitive, confidential, or proprietary information to models that are shared with the public.

Moreover, models focused on a specific area effectively reduce the risk of biases and hallucinations, making them a safer and more reliable choice, especially for tasks where accuracy and truthfulness are paramount.


Why Generative AI is crucial?


Developing a comprehensive generative AI strategy is essential for businesses aiming to unlock the full potential of this revolutionary technology and stay ahead in today’s competitive landscape.

It ensures that generative AI initiatives are directly aligned with the organization’s overarching goals and operational priorities.

It provides the framework for establishing robust data governance frameworks, ethical guidelines, and risk management protocols. This proactive approach helps mitigate potential legal, reputational, and societal risks associated with AI implementation, fostering responsible and sustainable AI usage.

It guides the development of essential technical infrastructure, data management capabilities, and talent acquisition or upskilling initiatives.


Generative AI Proof of Concept (PoC): Benefits


  • Validates Technical Feasibility

A generative AI PoC allows organizations to verify if the proposed AI model or solution can function as intended in a real-world environment.

  • Secures Stakeholder Buy-in and Funding

By demonstrating a working model, a successful PoC can help secure buy-in from key stakeholders and decision-makers.

  • Mitigates Risks

A PoC allows organizations to identify and mitigate potential risks associated with generative AI implementation.

  • Evaluate Performance and ROI

By testing the generative AI solution in a controlled setting, a PoC helps organizations evaluate the model’s performance, efficiency, and potential ROI.

  • Accelerates Time-to-Market

Conducting a PoC can significantly reduce the time and effort required for full-scale development and deployment.



Checklist for
Successful Gen AI Adoption








Setting Gen AI Goals


  • Clearly define the specific generative tasks and capabilities you aim to achieve (e.g. text generation, image creation, code synthesis).
  • Ensure goals align with your district’s overall mission, vision, values, and strategic priorities.
  • Prioritize goals based on potential impact, benefits, risks, costs, and implementation timelines.

Aligning with Organizational Goals


  • Conduct a comprehensive assessment to ensure Gen AI goals align with broader organizational objectives, processes, and long-term roadmap.
  • Gain buy-in and support from key stakeholders like district leaders, educators, parents, and community members.
  • Establish clear policies and guidelines for responsible and ethical use of Gen AI aligned with district values.

Develop a Roadmap


  • Create a detailed, phased roadmap outlining milestones, timelines, and steps for planning, deploying, integrating, and continuously monitoring Gen AI.
  • Define roles, responsibilities, and accountability for each roadmap stage across relevant teams/departments.
  • Build in mechanisms for continuous improvement, iteration, and adaptation based on results and feedback.

Assess Readiness


  • Perform a thorough readiness evaluation across critical dimensions like data quality, technical infrastructure, workforce skills, and existing policies.
  • Identify gaps, risks, and areas requiring additional investment or preparation for successful Gen AI adoption.
  • Develop mitigation strategies and contingency plans to address potential roadblocks or challenges.

Identify Use Cases


  • Discover high-value, transformative use cases where Gen AI can deliver maximum benefits and ROI.
  • Prioritize use cases based on strategic impact, feasibility, costs, risks, and alignment with goals.
  • Engage end-users and subject matter experts to validate and refine identified use cases.

Generative AI in Various Industries: Use Cases



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Gen AI in Manufacturing


Generative AI enables industries like manufacturing, automotive, aerospace and defense to design parts that are optimized to meet specific goals and constraints. It is also being used to compose entirely new materials targeting specific physical properties, reducing product-development life cycle time from weeks with human experts to hours.


Gen AI in Finance


Generative AI is enabling AI-assisted financial advisors and credit risk assessments. It has the potential to personalize customer experiences, optimize inventory management, and improve fraud detection in retail



Manufacturing
Finance & Insurance

Gen AI Technologies



Open Source LLM

Commercial LLM


LLAMA


LLAMA – A family of large language models released by Meta AI, ranging from 7B to 65B parameters, aimed at advancing research in areas like instruction following and multi-task learning.

BLOOM (Hugging Face/BigScience)


BLOOM (Hugging Face/BigScience) – Model trained on a large multilingual dataset, developed by the BigScience workshop and Hugging Face.

Falcon (Anthropic)


Falcon (Anthropic) – Large Language Model released by Anthropic, focused on being safe and truthful.

Stable Diffusion


Stable Diffusion – An open-source text-to-image generative AI model capable of creating highly detailed images from text prompts, developed by Stability AI
Mistral


Mistral – A large open-source language model trained by LAION, comparable in size to GPT-3 but with a focus on safety and truthfulness.

Claude (Anthropic)


Claude (Anthropic) – A constitutional AI assistant from Anthropic that aims to be honest, harmless, and have stable long-term preferences aligned with human values.

Gemini (Google)


Gemini (Google) – Another large language model from Google, focused on open-ended conversation and question-answering

GPT-3.5 (OpenAI)


GPT-3.5 (OpenAI) – The predecessor to GPT-4, known for its strong language generation abilities but with some limitations in areas like math and commonsense reasoning

GPT-4 (OpenAI)


GPT-4 (OpenAI) – The latest and most advanced language model from OpenAI, succeeding GPT-3.5. It has improved capabilities across various tasks like question-answering, writing, and coding.

Key benefits

Gen AI Benefits



Accelerated Gen AI Adoption and Implementation


Gen AI consultants can guide businesses through the process of responsibly adopting and implementing Gen AI technologies. They provide expertise in identifying high-impact use cases, conducting proof-of-concepts, and integrating Gen AI into existing workflows and systems.


Strategic Guidance and Roadmap Gen AI Development


Consultants help organizations develop a strategic roadmap for leveraging Gen AI, aligning it with business objectives, and ensuring it drives innovation and competitive advantage. They assess readiness, prioritize opportunities, and plan for future capabilities.


Customized Solutions and Recommendations


Gen AI consultants analyze a company’s specific needs, challenges, and data to provide tailored recommendations and solutions. This includes customizing pre-trained models, developing prompts, and fine-tuning Gen AI for industry-specific or proprietary applications.


Expertise in Ethical and Responsible AI


Reputable consultants ensure Gen AI deployments adhere to ethical principles, mitigate risks like bias and hallucinations, and maintain data privacy and security. They implement governance frameworks and monitor for responsible use.


Generative AI Consulting - FAQ


What are the most important limitations and drawbacks of using Generative AI in business?
How consulting Gen AI companies can overcome the Gen AI limitations?
What are some best practices for managing the risks of Generative AI in business?


What are the most important limitations and drawbacks of using Generative AI in business?


Key Limitations of Generative AI in Business:

  • Lack of True Creativity – Generative AI remixes existing data rather than creating genuinely novel ideas.
  • Limited Contextual Understanding – Struggles with nuanced contexts in complex business situations, risking oversimplified or tone-deaf outputs.
  • Data Privacy and Security Risks – Heavy reliance on data poses risks of data breaches or mishandling, which can undermine client trust.
  • Algorithmic Biases – Can perpetuate societal biases from training data, leading to unfair or skewed recommendations.
  • Black Box Nature- Opaque decision-making processes make it hard to explain outputs, potentially breeding mistrust.
  • Vulnerability to Manipulation – Susceptible to adversarial attacks, raising concerns about the reliability of AI-driven outputs.

How consulting Gen AI companies can overcome the Gen AI limitations?


Strategies for Consulting Firms to Overcome Generative AI Limitations:

  • Ensuring Data Quality and Governance
  • Maintaining Human Oversight and Validation
  • Enhancing Transparency and Explainability
  • Implementing Robust Testing and Monitoring
  • Adopting Ethical AI Principles and Governance
  • Fostering Responsible Innovation and Upskilling

What are some best practices for managing the risks of Generative AI in business?


  • Implement robust data governance and privacy measures.
  • Utilize high-quality, curated, and well-labeled data for training generative AI models, reducing biases and inaccuracies.
  • Prioritize the use of first-party or zero-party data over third-party sources.
  • Keep training data fresh and up-to-date to maintain model accuracy over time.
  • Ensure there is a “human-in-the-loop” to review and validate the outputs of Generative AI.
  • Establish processes for external verification, fact-checking, and quality assurance, especially for critical recommendations or decisions.
  • Provide training for users to help them understand the strengths, limitations, and appropriate use cases of generative AI.
  • Prioritize transparency by making the decision-making processes and underlying data sources of generative AI models interpretable and explainable.
  • Implement techniques such as model documentation, output attribution, and confidence scoring to build trust and accountability.
  • Be transparent about the usage of generative AI and its limitations with clients.
  • Implement robust testing, monitoring, and feedback loops.

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