Innovative minds are stepping up to “change the game” in the fast-paced world of legal and compliance work, where mountains of documents and client interactions can bog down even the most efficient teams. One such professional within this discipline is Tejas Sarvankar, a Senior Product Manager with over six years of experience in leading product development from scratch. Sarvankar holds a Master’s in Human-Computer Interaction from Carnegie Mellon University, along with certifications in design innovation, artificial intelligence, and machine learning. He has made a mark for himself by integrating user-centered design with advanced AI.
The legal profession has forever wrestled with zapping inefficiencies that impact both time and resources. It can take days to undertake a document review for compliance. Checks are done manually to find out whether the documentation complies with the rules, whether the brand is consistent, and whether it has risks. This saves time within banks, financial institutions, and law firms. Added to this is that operations incur added costs and a raised percentage of errors.
Another sore pain area is case intake: qualifying clients and requesting initial information either by a call or through filling out forms is cumbersome. They yield frustrated waits or, worst, loss of opportunity. The problems grew during the COVID-19 period because they highlighted the fragility of such systems, the usual realities of work-at-home and global distribution demands. Life sciences training, for example, became a bottleneck in those events, with medical representatives unable to track logistical capabilities relating to vaccines and equipment amidst increasing inflow demands.
There are existing solutions processes that try to fill these gaps, but by and large, they have failed. Most are static automation tools like rule-based checklists or basic NLP for classification and summarization. Normal tasks are handled with these systems, like flagging errors or redlining documents; unfortunately, that is all they can do. They don’t adapt to nuanced workflows. Some platforms do offer conversational interfaces, but they are typically very rigid, relying on structured forms that do not account for context. For instance, a tool might find violations in compliance reviews, but it is unable to adopt the internal style guidelines or emulate expert judgment. Voice systems exist for case intake but are usually unrelated to the follow-up conducted automatically or document automation. What this boils down to is teams still spending their hours on manual interventions, 20 to 30 percent gains in efficiency at best, and these are nowhere near transformative.
Enter agentic AI-a paradigm shift toward dynamic, intelligent systems that act like virtual teammates. Not merely chatbots, they are conversational agents capable of learning, reasoning, and performing tasks in real time based on human-like knowledge. In legal tech, these systems will replace context-free document review and client-interaction processes. Sarvankar is at the forefront of developing such systems, with the entire focus on user-centered design that ensures they are trustworthy and accurate. The human-machine interaction education will allow him to translate technical AI demands into readily digestible legal ones and build tools that help eliminate bottlenecks and enhance strategic decision making.
Key projects highlight this approach. One was a recent initiative for launching an AI engine for document reviews that ensured regulatory compliance while eliminating legal obstacles for doing so. It creates an agentic architecture that not only tags problems, but also can learn internally specific nuances like tone and style of branding. Another developed a voice AI intake assistant that was integrated with Twilio and ElevenLabs and will automate client qualification and case details. It engages in conversations dynamically, extracting missing context, and qualifying leads faster. Moreover, an AI on marketing compliance was built to reduce review times at large institutions, converting days of reviews into minutes.
Exemplary benefits were derived out of these endeavors, proving their industrial worth. Legal intake call times were cut down significantly-advised by the voice AI assistant-by as much as 70 percent, with law firms, therefore, able to convert their leads faster and save money. The compliance review agent was able to deliver a speedier turnaround of documents by as much as 80 percent, reducing time to market for regulated content. With one deployment portion, the outcome also presented clients with a 30 percent reduction in overall time for compliance review, resulting in $1 million recycling revenue in a year. Beyond the noticeable modular impact, these include frameworks, adaptable in medical, customer service, and other workflows, with innovations scaling beyond initial applications.
Sarvankar reflects on this evolution: “Legal tech often gets stuck in checklists and forms, but context-aware conversations change everything-it’s about bringing AI truly on board in the process, not as just a tool.” His work creates a new paradigm by moving from static rules to adaptive agents that mimic experts, providing personalization and scalability.
Besides core projects, other internal contributions include the creation of foundational system prompts for AI agents, technical guidance, and the shaping of a future strategy. He took on additional responsibilities like initiating partnerships with legal tech platforms, defining product culture, and designing project workflows from the very first block with tools like Linear. Mentorship also did play a significant role; he guided junior engineers and designers in AI-first design and large language model usage, creating that type of collaborative environment where iteration could be accelerated.
The external efforts enlarge his networks. This time, having founded a community-run podcast on design stories, he garnered an audience of over 5,000 unique listeners, sharing insights across social channels to amplify real-world applications. The next innovation-a system prompt framework for high-quality research papers via multi-modal ingestion and Socratic prompting-is consequently broader in its applicability, although the metrics in this case relate more to conceptual advancement than to quantifiable measures.
Such strides have by now been followed by some recognition. He, therefore, received the Academic Excellence Award in 2020 and then won the Product Development Program at Aalto University competing with participants from 16 other countries. His work has attracted commendations from advisors, investors, and design partners, who remarked that the agent-first positioning served as a valuable strategic approach. He has demoed for top law firms, garnering significant approval from industry experts for the transition toward intelligent process ownership.
The effects then ripple outward. By modularizing offerings for different legal sectors, these inventions allow industries to view compliance not as a burden but as a strategic asset. Fast-tracked client onboarding-multiple firms signing on within a fortnight of demos was well received by firms since it allowed greater alignment with market needs. Effectively, agentic AI is just helping with efficiency, cost-cutting, and accessibility to equalize access to legal services.
As Sarvankar puts it: “We are not just automating tasks; we are building systems that learn and evolve with the experts they support, turning compliance into a strategic advantage.” Given this attitude, this is why the progression is relevant; they target existing problems and facilitate the creation of an agile legal terrain.
