Eucloid makes the connections count

Consider the peculiar burden of having 2,500 models. Each answers a question. Each also needs a home, computing power and someone willing to investigate when the answer looks wrong. In one Eucloid Data Solutions engagement, that collection had become expensive and laborious to manage on Google Cloud. The client wanted better performance and lower costs. Getting there involved a deceptively ordinary task: checking that the new system still told the truth.

The team converted the models’ queries for Databricks, compared outputs for accuracy and tuned performance. Eucloid reports a 20% performance improvement and roughly 30% cost savings. Those are the company’s figures for an unnamed client. The revealing detail is the comparison of outputs. A cheaper answer only helps if it remains the right answer. Eucloid’s migration account puts that checking inside the job.

A marketing question becomes an engineering job

Eucloid began in 2020 with Raghvendra Kushwah and Anuj Gupta, two IIT Delhi batchmates. Its early territory was digital commerce: acquiring customers, improving conversion and keeping buyers coming back. These sound like marketing assignments. Follow them far enough and they become questions about databases, identity and measurement.

A customer can browse anonymously, click an advertisement and buy through another channel. The business then has several records of what was, to the customer, one experience. Spend can be counted. Revenue can be counted. Connecting the two takes more work. Eucloid’s early offerings included multi-touch attribution and marketing-mix models, both intended to help clients decide where advertising money should go.

The company’s present structure makes that journey visible. Martech covers customer intelligence and engagement. Data covers platforms, pipelines, migration and governance. AI covers models and the workflows that use them. The sequence is useful: first understand the decision, then arrange the information, then make the result usable.

The customer has already crossed the aisle

One particularly concrete expression is its customer data platform work. A CDP brings information about the same customer together so teams can build audiences, measure behavior and coordinate communication. Eucloid implements packaged platforms and composable versions, where components are assembled around the enterprise’s existing data infrastructure.

Its Databricks-based composable accelerator is aimed at large enterprises and data-mature teams. The advertised time to value is four to eight weeks. Keeping customer data in the company’s warehouse gives the enterprise more control over identity rules, models and governance. It also gives somebody the responsibility for maintaining those choices.

That is the tradeoff hiding inside the word “composable.” A business with engineers, clear ownership and complex requirements can use the flexibility. A small team seeking a ready-made campaign tool may find the extra integration work difficult to justify. The sensible starting question is which customer decision needs improving, and whether the current setup prevents it.

Permission to do something useful

There is another obstacle after data has been collected: access. Eucloid’s Unity Catalog Command Center addresses the everyday administration around Databricks governance. Its advertised functions include bulk permissions, role management, onboarding templates, activity visibility and audit snapshots. The intended users extend beyond the engineers who built the platform.

This reveals something about the firm’s market position. A platform vendor supplies the underlying machinery. A consultancy can make that machinery fit an organization’s permissions, processes and business vocabulary. Eucloid occupies that implementation layer, with reusable software alongside its services. The relevant alternatives include an internal engineering team, a large systems integrator or a specialist analytics consultancy.

Its Databricks partnership was publicly announced in January 2023, together with an in-house training academy. Its Adobe practice covers analytics, experimentation, campaigns and content; its AWS work covers data lakes, pipelines and event-driven systems. Those relationships widen the available tools. The actual choice still belongs inside the client’s workload and operating requirements.

The model has to leave the notebook

Eucloid’s AI services continue the same argument. Developing a model is one stage; deploying, governing and monitoring it are continuing jobs. Its MLOps offering includes experiment tracking, controlled promotion into production, drift monitoring and retraining triggers. Useful intelligence needs an operating routine.

In one published MLOps example, a financial institution had manual deployment processes, fragmented experiment management and limited model traceability. Eucloid describes moving the lifecycle to Azure Databricks, using MLflow for tracking and version management and introducing controlled staging-to-production workflows. The problem started with the machinery around the model. The intervention made its history and release process more visible.

The dedicated Lumen AI product site applies automation to documents: classification, extraction, completeness checks, business-rule validation and summaries. Insurance claims, referral forms and credit memos appear among its use cases. The proposition is understandable without an AI vocabulary lesson: information trapped in paperwork should reach the system where somebody can use it.

“Explainability is now the agenda.”

Raghvendra Kushwah, in commentary reproduced by Eucloid’s newsroom, June 2026

Start with the answer you need

Eucloid sells scoped consulting and implementation, with managed services after deployment. Customers described in its public materials include retailers, financial institutions, healthcare organizations and technology businesses. In a 2022 founder interview, the company described positive cash flow and no immediate need to raise money. That historical position helps explain the emphasis on client work.

The careers page offers another clue to how that work is organized: a budget for learning and certifications, hybrid working and opportunities to work directly with clients and leadership. Consulting depends on people who can translate between business requirements and technical systems. Training and client contact are practical investments in that translation, especially when a project crosses marketing, engineering and compliance teams.

The lesson a reader can borrow is specific. Before moving a platform, inventory the models. Before celebrating faster queries, compare outputs. Before automating a campaign, establish identity and consent. Before declaring an AI project finished, decide who monitors it. Eucloid’s interesting territory lies in those handoffs. The click is easy to see. Making everything after it agree is the work.