An Indian startup named Rocket is positioning itself to bring artificial intelligence into the consulting space, aiming to deliver strategic insights at a fraction of the cost traditionally associated with advisory firms.
The company is building an AI-driven platform designed to replicate aspects of consulting workflows, including data analysis, market research, and strategic recommendations. By automating these processes, Rocket aims to make consulting services more accessible to a broader set of businesses, particularly startups and mid-sized enterprises that may not typically engage large consulting firms.
The development reflects a wider trend in enterprise AI adoption, where companies are exploring ways to automate knowledge-based work. Consulting, which has historically relied on human expertise, structured frameworks, and manual research, is increasingly being examined as a candidate for AI-led transformation.
Rocket’s platform is designed to ingest large volumes of structured and unstructured data, analyse it, and generate insights that can inform business decisions. This includes identifying market trends, evaluating competitive landscapes, and suggesting strategic directions. The company is positioning the product as a tool that can augment decision-making rather than replace human consultants entirely.
According to the startup, the goal is not to replicate consulting firms in full but to streamline the more repetitive and data-intensive aspects of their work. This approach allows businesses to access insights faster and at a lower cost, while still retaining the option to layer human expertise where required.
The cost factor is a central part of Rocket’s positioning. Traditional consulting engagements can be expensive and time-consuming, often requiring weeks or months of analysis. By contrast, AI-driven systems can process information and generate outputs in significantly shorter timeframes. This efficiency is expected to appeal to companies operating in fast-moving sectors where speed of decision-making is critical.
Industry observers note that the rise of such platforms is closely tied to advances in generative AI and large language models. These technologies have improved the ability of machines to interpret complex data, generate structured reports, and provide context-aware recommendations. As a result, tasks that were once considered highly specialised are becoming increasingly automated.
At the same time, questions remain around accuracy, reliability, and accountability in AI-generated insights. Consulting firms are often valued not just for their analysis but also for their judgement and ability to contextualise recommendations. AI systems, while efficient, may require careful oversight to ensure outputs align with real-world business complexities.
Rocket’s approach appears to acknowledge these limitations by positioning its platform as a support system rather than a standalone solution. Businesses are expected to use the insights generated by the platform as a starting point, refining them with internal expertise or external guidance where necessary.
The company is entering a competitive and evolving landscape, where both startups and established technology providers are exploring similar opportunities. Large enterprise software companies are also embedding AI capabilities into their platforms, enabling organisations to perform advanced analysis without relying entirely on external consultants.
For the consulting industry, the emergence of AI-driven platforms presents both a challenge and an opportunity. While automation may reduce demand for certain types of services, it could also shift the focus of consulting firms towards higher-value, more strategic engagements that require human judgement and domain expertise.
As AI continues to mature, its role in knowledge-intensive sectors is expected to expand. Rocket’s entry into the consulting space highlights how startups are leveraging these technologies to rethink traditional business models and create new forms of service delivery.
The coming years are likely to see further experimentation in this area, as companies test how AI can be integrated into decision-making processes while maintaining reliability and trust.