Turinton's Insights AI platform tackles enterprise AI challenges by cutting implementation timelines from eight to 12 months to just eight to 12 weeks.
The enterprise AI landscape has a dirty secret: most projects never make it to production. Despite the billions invested and countless pilot programs launched, 90% of enterprise AI initiatives fail to reach actual business users. They get stuck in data preparation phases, derailed by integration challenges or abandoned when user adoption falls flat.
Vikrant Labde, co-founder and CTO at Turinton, has witnessed this cycle countless times. "Most platforms focus solely on model or agent building, but organisations need governance, cost control, explainability and seamless user distribution. Without solving this 'last mile' problem, AI remains an expensive science experiment rather than a business transformation tool," he said.
The gap between AI potential and AI reality has become a significant challenge that's costing organisations both time and money, while competitors who get it right are pulling ahead.
Turinton, a next-generation AI enablement firm, aims to bridge this gap. The company accelerates enterprise AI transformation, founded by executives who've already built and scaled successful technology businesses.
Co-founders Labde and Nikhil Ambekar previously built Cuelogic, a digital product engineering firm that was successfully acquired by LTIMindtree. Their experience gave them front-row seats to watch enterprises struggle with the same AI deployment challenges repeatedly.
"We saw companies investing heavily in AI but struggling to productise it. The experience at Cuelogic revealed a crucial challenge that companies experimented with AI but struggled to productise it," Ambekar explained.
The founders recognised that moving beyond model development was necessary for secure, governable AI to be delivered to business users. They realised that custom consulting did not scale and that the industry required a platform to provide consistent, repeatable results.
What was built hereon was an AI business infrastructure, rather than just another AI platform. This insight shaped Turinton's focus on building AI business infrastructure, not just AI models, a fundamental shift that's proving to make all the difference.
Enterprise AI adoption faces three persistent bottlenecks, according to Turinton. First, enterprise data lives in silos across ERP, CRM and operational systems and typically requires extensive extract, transform, load (ETL) work to make it AI-ready. That is a time-consuming process and creates security risks.
Second, developing and deploying AI tools quickly enough is a challenge. By the time solutions are production-ready, business requirements have often changed, making the original solution less relevant.
Third, access to every stakeholder remains limited. Often, AI remains accessible only to technical teams, leaving business users unable to benefit from the investment.
These roadblocks lead to costly, isolated and delayed AI initiatives. Vikas Singh, the company's chief growth officer, noted that "data accessibility" is the number one complaint they hear.
CTOs report valuable data locked in various systems, such as ERP and CRM, which makes creating a unified view time-consuming and risky due to the extensive ETL work required.
Moreover, speed presents a critical challenge. By the time an AI solution is ready, business requirements often change. The most urgent pain point is user adoption. Businesses frequently express frustration that they can only utilise the advanced AI systems available to data scientists.
"That's why we built Turinton around zero-ETL architecture, rapid deployment and built-in user distribution. By having this approach, we're solving technical challenges as well as business problems," Singh expressed.
Turinton's Insights AI platform tackles enterprise AI challenges by cutting implementation timelines from eight to 12 months to just eight to 12 weeks, while maintaining high accuracy and scalability. Its zero-movement architecture connects structured, unstructured and operational data in real time without relocating sensitive information, reducing security and compliance risks.
Powered by knowledge graphs and ontology-based intelligence, the platform creates a dynamic, unified knowledge graph. The Discover AI agent scans data, uncovers silos, and enriches metadata, while the Correlate agent maps relationships across systems to build enterprise-specific ontologies.
"Traditional ETL takes months just to prepare data; we eliminate that entirely," Labde said. "Our approach turns months of engineering into minutes of configuration while keeping data under your control. No quality issues, no privacy concerns, no synchronisation problems." He added that this not only reduces risk but also significantly lowers costs, since ETL is both slow and expensive.
Traditional AI projects 1follow a predictable, lengthy timeline and are costly, ranging from $2 million to $5 million. Turinton Insights AI compresses this entire cycle to weeks, cutting traditional timelines by 80%.
Ambekar recalled a heavy-industry client monitoring Overall Equipment Effectiveness (OEE). Within two weeks, Turinton's AI flagged hidden downtime patterns in machine logs and operator notes, insights their ERP had missed for years. "We moved beyond dashboards, we gave frontline managers a 24/7 digital co-pilot," he said.
Another manufacturing client implemented predictive maintenance across production lines in three weeks. "A traditional approach would have required eight to 10 months just for ETL pipelines. We eliminate that entirely," Labde noted.
These implementations moved beyond pilot programs to deliver immediate, measurable business value.
Turinton built their platform with enterprise requirements as core features, instead of add-ons.
The platform is built on an open-source and API-first foundation, and is ISO 27001 and SOC 2 ready, ensuring robust security and compliance. This open-source and API-first architecture prevents vendor lock-in.
It also offers multi-cloud and on-premise flexibility, adapting to diverse IT environments across AWS, Azure and GCP. Built and certified by Red Hat, the platform runs securely on OpenShift and RHEL AI.
Its advanced capabilities include multi-agentic orchestration for complex workflows, GraphRAG and AutoML for intelligent automation, and integrated FinOps for cost management and ROI tracking. It also has full support for LLMs, SLMs, RAG and GraphRAG.
Labde explained why making AI "operational" and not just "accessible" represents the end of fundamental transformation. He elaborated that building AI that works in a lab is hard, but building one that works in production is exponentially harder.
The difference between 'accessible' and 'operational' is everything that makes AI 'enterprise-ready', he asserted. Key operational requirements include real-time cost and ROI tracking for financial teams, complete audit trails for compliance teams, explainable recommendations for business users and administrative controls for managing AI processes.
"Most platforms treat governance, cost control and explainability as afterthoughts. We built them into the foundation. That is the difference between a demo and a business transformation," Labde said.
Ambekar predicts significant changes in the coming two to three years that will determine which companies become AI leaders versus AI followers. "Success won't go to organisations with the most advanced models or biggest AI budgets, it will go to those that can operationalise AI at scale and put AI capabilities directly into decision-makers' hands," he said.
The companies winning with AI aren't just implementing the technology; they're implementing it right the first time, with platforms that deliver consistent results and measurable business value.
With AI adoption reaching a tipping point, Turinton represents this shift from experimental AI to operational AI, providing enterprises with a proven path from concept to production that works.
