AI Development Services

It depends on your AI readiness. Approximately 85% of AI and ML models fail due to a lack of technical expertise, the absence of the right tech team, ill-defined user personas, a mismatch between vision and product, misguided expectations, and many other reasons. You can avoid all these issues by starting your implementation plans with an AI implementation checklist. Drawing from our experience of building over 40 production-ready AI models, we have created a comprehensive checklist for you. Download it to review and enhance your plans accordingly.

We have deployed more than 40 AI models across industries. The list includes 

• Image processing models for a healthcare startup
• Video generation models for marketing platforms
• Chatbots (RAG-enabled, assistants, and others) for industries like recruitment, IT, and security companies
• Predictive analytics models for IT, healthcare, RealTech, and FinTech companies
• Generative AI models for RealTech, FinTech, IT, marketing, and other industries
• Automated workflow management for a marketing company

Multiple factors can impact the cost of AI development services. The project’s complexity is a big challenge as the complication of the algorithms can drive the costs high by demanding more resources and time. Sophisticated models also require more support in terms of GPUs and cloud services. 

A lack of data can also impact, as building the right data pipeline is a costly affair. Deciding between LLMs and classical models is another. LLMs are cheaper, but they can trigger privacy issues. In addition, LLM cost is incremental, and it increases with the number of users. Integration with the existing system, deployment, and maintenance of the model can also influence the overall cost.

The timeline for AI development projects varies based on type and complexity. Building a helpdesk or assistant and RAG pipeline can take at least 2 months while automating workflows can take at least a year.

The AI development team structure varies according to the product stage and project complexity. At a bare minimum, a Data Scientist can manage the Proof of Concept (POC) stage. For MVP development, a team comprising a Data Scientist and a Backend Engineer is necessary. For full product development, the team should include a Data Scientist, a Backend Engineer, a DevOps Engineer, and a CloudOps Engineer. Additionally, there may be a need for UX/UI Designers and QA Experts to ensure a seamless user experience and robust product quality.

Our approach is mostly requirement-driven. However, some questions fit most GenAI development processes and help decide the approach. Here they are-

• How crucial is data privacy? 
• What is the breakeven point for Open AI services and open-source models?
• If OpenAI is the platform, then at what rate requests come?
• What is the cloud environment we are using?
• Are we okay with not having real-time responses?
• Can we have open-source models with their own GPUs?
• Do we have to generate pure images?
• Do we have to use Llama models or Anthropic?

For effective generative AI implementation, always onboard product engineers with experience in Large Language Models (LLM), Prompt engineering, Agents, and Data Science.

We have deployed more than 15 AI models across industries. The list includes 

• Image processing models for a marketing platform
• Audio generation models for entertainment and animation companies
• Video generation models for a marketing platform
• Chatbots (RAG-enabled, assistants, and others) for industries like recruitment, IT, and security companies
• Info extraction models for analytics, retail, and e-commerce companies
• Automated workflow management for a marketing company

Generative AI has proven its capabilities in terms of improving productivity, managing workflow, and optimizing resource utilization. However, its proper impact depends on four major factors.

1. ROI—GenAI pilots should establish clear success criteria before launch, focusing on measurable outcomes in two key areas: enhancing customer experience and reducing unit costs. This will help close the gap between their promise and reality.
2. Data privacy—Security is still a big concern for many companies, particularly tech giants, as they want to prevent data breaches at all costs.
3. Performance quality and response time- Sometimes, these two factors can adversely affect each other. For instance, while GPT-4o delivers results faster than GPT-4, the quality may be inferior. Prioritizing requirements based on the use case is absolutely necessary.
4. Human supervision is required to ensure accuracy, ethical compliance, and quality control.

The ideal team composition for a generative AI project includes 

• Project Manager to oversee timelines and coordinate efforts 
• Data Scientists to manage data acquisition and preprocessing. 
• Machine Learning Engineers implement and optimize the models
• DevOps Engineers handle deployment and maintenance
• UX/UI Designers focus on user-friendly interfaces
• QA Engineers validate the software’s performance and reliability
• Ethics and Compliance Officers ensure adherence to ethical standards, 

This comprehensive team structure can ensure the successful development, deployment, and maintenance of generative AI projects.

Implementing machine learning can help your business by improving decision-making, increasing efficiency, automating processes, and uncovering valuable insights from data. We have identified a faster method by initially leveraging LLMs to understand their scope and potential solutions. Subsequently, if there is a need, we transition to pure machine learning, which, although more time and resource-intensive, offers robust and precise results.

We have deployed around 30 models across industries. Some of these projects are- 

• Reinforcement learning model for a mobile advertising company
• Predictive analytics model for real estate, wireless network, TV advertising and email marketing platforms
• Deep learning model to build a domain-specific Q&A system for a channel marketing company
• Image processing technology for a biotech startup
• Assistants for a recruitment platform

Developers with a good knowledge of either statistics or mathematics and Machine Learning Algorithms can build successful models. Expertise in Data Science is always a plus.

There are four main steps: business understanding, data acquisition, model development, and deployment. However, using LLMs can reduce cycle time by speeding up business understanding and data acquisition.

The required timeline in a machine learning development project is problem-specific and depends heavily on complexity. A simple project can be completed in 2 to 4 months, whereas a pure ML project with a high level of complexity may take 8 to 18 months to complete.