Relanto and the art of getting time back

Forty-eight hours is an awkward amount of time for a business to lose. Long enough for a sales team to need an answer, revise its question, and need another answer. At one high-tech company described in Relanto’s case studies, business planning brought precisely that interruption. The systems held the information. Getting it through the machinery was the problem.

Forty-two hours, returned

The culprit, in Relanto’s account, was a combination of tightly linked processes, rigid schedules and the consumption of full datasets from upstream systems. Salesforce dependencies slowed critical twice-yearly operations. The remedy involved examining those dependencies, reducing reliance on Salesforce, batching records and separating processes so they caused fewer interruptions.

Relanto says the resulting downtime was six hours. That is an 87.5 percent reduction, and a useful introduction to a company whose website speaks fluent AI. Here, the explanation is satisfyingly mechanical. Change how records move. Change which operation waits for another. Give the people downstream their information sooner.

The case matters because it gives a concrete meaning to enterprise transformation. A business can own capable software and still spend two days waiting for its own data. Relanto occupies that uncomfortable space between a technology purchase and a working operation.

The question behind the query

Consider another published engagement, this time with a global technology provider. Business users needed information but lacked SQL skills. Technical teams became intermediaries. Manually written queries introduced errors, and growing data volumes made the arrangement harder to sustain. Every question came with an organizational detour.

Relanto built a natural-language-to-SQL layer, a contextual assistant and a query reasoning engine that mapped intent to the customer’s data schema. It connected the system to Amazon Redshift and PGVector and presented results with filtering and sorting. The appealing part is asking a question in everyday language. The demanding part is making that question mean the right thing inside a particular business.

A separate finance engagement connected Salesforce, BigQuery and financial systems through APIs, with a conversational interface for budget and return-on-investment questions. These projects describe a consistent specialty: joining information that already exists, then making it accessible to the person trying to decide something.

The same problem appears in Relanto’s partner-performance case. Regional teams calculated metrics differently, while dashboards showed changes without explaining them. Relanto describes adding conversational analytics and retrieval-augmented generation, which draws on relevant information to ground responses. The telling feature is the ability to ask why a score dropped. A number can travel across a company much faster than agreement about what it means.

A consultancy with a parts drawer

Founded in 2021, Relanto combines advisory work with implementation and engineering. Its publicly identified co-founders include CEO Rajan Gaur, president Shankar Chowhan and COO Yeshwant Nayak. Its service menu covers enterprise business planning, data engineering and analytics, digital transformation, revenue and sales, and an AI Center of Excellence.

The customers are enterprises with systems to reconcile: financial institutions, technology providers, retailers, healthcare businesses and manufacturers. Anaplan, Salesforce, Google, Microsoft, AWS and Snowflake appear in its partner ecosystem. For a buyer, the proposition is an implementation team that can work across planning, customer records, cloud infrastructure and the data connecting them.

That places Relanto alongside systems integrators and specialist platform consultancies. Its chosen distinction is the combination of connected planning, applied AI and reusable accelerators. The commercial model is service-led, with proprietary frameworks supporting delivery. A buyer is engaging people to adapt technology to an operating business.

The parts drawer has names. R-Optimizer monitors Salesforce health, including API usage, code coverage, permissions and platform limits. R-SmarTest supports test automation. R-SmartAssist connects enterprise information and workflows to contextual assistance. The AI-First Lab, launched in Bengaluru in February 2024, provides a setting for experimentation involving employees, customers and academic collaborators.

There is a community ambition alongside the commercial one. At the 2024 launch, Relanto pledged $1 million to its Relanto Cares initiative. Announced plans included a computer lab and classrooms at Neraluru Government School. The careers page, meanwhile, emphasizes collaboration and professional growth. These are the company’s stated commitments; the pledge should be understood as a commitment rather than money already spent.

Someone has to watch the meter

Once an organization has working AI, it acquires another management problem: consumption. Relanto’s R-TokenomIQ is designed to track spending, assign it to teams and projects, enforce budgets before requests reach providers, and route workloads among approved models. Its logic is practical. A small classification task and a complex reasoning task need not consume the same resources.

R-LiveMeasure addresses the neighboring problem of agent oversight. Its advertised capabilities include tracing interactions, evaluating behavior against business context, involving human reviewers and connecting performance to business measures. The lab page invites early-access walkthroughs for both offerings. These are capabilities to assess during procurement, rather than guaranteed savings to enter into a spreadsheet.

“The issue isn’t the promise of AI - it’s the foundation beneath it.”Rajan Gaur · on enterprise AI investments

On September 21, 2026, Relanto announced OpenAI Select Partner status, linking the milestone to intelligent applications, governed workflows and enterprise integration. The announcement also highlighted its agent and token governance tools. Together, they show where its offering is expanding: controlling the behavior and economics of AI after deployment.

Start with the waiting

What can another company borrow? Start by finding a delay with a name and an owner. Measure it. Map the systems and people involved. Decide whether the obstacle is missing information, inconsistent definitions, manual entry or an unnecessary dependency. Relanto’s data-capture case, which reports 30 percent less manual effort, combined OCR and conversational input with validation and anomaly detection.

The lesson has conditions. A conversational answer needs agreed business definitions and permitted access to current data. Automated actions need owners and review rules. A smaller business with one straightforward system may have little use for this much integration. For an enterprise, the meaningful test is whether a workflow finishes sooner, accurately, at an acceptable total cost. Forty-eight hours becoming six is an intelligible place to begin.