For several years, Altimetrik has observed a recurring pattern in moving AI projects from the proof-of-concept (POC) stage to production. Ambitious AI pilots, while performing well in controlled environments with manually stitched data and domain-specific workarounds, consistently struggled to scale once they encountered the realities of production.
This experience has exposed a critical truth: sustainable AI scaling requires a robust, automated data pipeline that minimises human intervention. This insight first took shape nearly seven years ago during a project for a UK-based fintech unicorn.
At the time, well before AI became a buzzword, the industry norm was to spend one to three years consolidating enterprise data into a monolithic data lake before even initiating AI initiatives.
Jayaprakash Nair, head of AI and analytics at Altimetrik, explained in an email interaction with AIM that this is where the company chose to take a different approach.
Instead, the pure-play AI, data and digital engineering company identified customer analytics as the business area with the highest AI potential. They started with marketing analytics, aggregating only relevant data into a data lake on AWS and solving the top two business problems using ML and AI.
These solutions, Nair claimed, were deployed to production within three months, delivering tangible business value. With the core data foundation in place, the team addressed subsequent marketing analytics problems more efficiently. The effort expanded horizontally—first into other domains within customer analytics and later to risk analytics.
According to him, this horizontal, end-to-end slicing of a larger problem allowed the business to realise incremental value early, instead of waiting years for a fully consolidated data lake.
Altimetrik’s key takeaway was clear: success at scale demands a production-first approach, Nair further explained. Rather than narrowly focusing on POCs, the company began working backwards from production realities.
This mindset became the foundation for ALTI Lab, which Altimetrik recently launched in Bengaluru. Nair said the lab is already driving increased demand from both new and existing clients across geographies and is central to Altimetrik’s strategy for reaching $1 billion in revenue within the next three years.
From Ambition to Production
ALTI Lab is Altimetrik’s new applied innovation and prototyping centre based in Bengaluru. Envisioned as a lab-to-factory model, the facility aims to help clients move from AI ambition to scalable, production-grade execution, while also transforming the company’s internal business operations with an AI-first mindset, the company stated.
Described by the company as the “epicentre” of its AI-first transformation, ALTI Lab offers production-grade AI accelerators like the LLM Benchmarking Accelerator, KIONE, and DomainForge.ai. These tools, supported by Altimetrik’s Agentic Development and Assessment (ADA) framework, help businesses achieve scalable AI solutions.
According to Altimetrik, the lab boasts cloud-native flexibility with a model-agnostic design, supporting major platforms such as OpenAI, Gemini, Llama, GCP, AWS, Azure and Snowflake for secure and future-ready development.
This approach also accelerates time-to-value, cutting it by over 30% through rapid prototyping for faster experimentation and enterprise deployment, the company further claimed.
ALTI Lab develops domain-specific LLMs that combine contextual accuracy with robust governance, enabling secure and high-impact AI use cases across various industries.
During the launch, Altimetrik CEO Raj Sundaresan introduced the lab as a breakthrough moment in the company’s journey.
“Enterprises need production-grade AI that delivers measurable results. That’s precisely what ALTI Lab is designed to achieve. What sets this apart is our ability to combine deep engineering rigour with practitioner-led domain expertise. Through ALTI Lab, we are enabling our clients to adopt AI in ways that are smarter in design, bolder in ambition, and faster in execution,” Sundaresan said in a statement.
ALTI Lab prioritises AI safety as a business imperative, embedding governance frameworks for business confidential information (BCI) governance, prompt injection resistance and regulatory readiness.
The lab also enforces explainability across all projects, building user-facing rationale interfaces and internal dashboards that make model decisions understandable, and therefore, trustworthy.
Organic Growth, Strategic Partnerships
Altimetrik’s journey toward ALTI Lab evolved not from a single event or acquisition, but from years of iterative work solving real-world AI problems, Nair revealed. Strategic partnerships, like those with OpenAI, helped lay the groundwork. However, the lab’s structure is largely built on organic growth and in-house expertise.
According to Nair, ALTI Lab has implemented AI solutions across diverse sectors by focusing on long-term scalability over quick fixes.
In agriculture, the team improved data quality by setting up upstream controls, enhancing pipeline predictability. In financial services, a successful initial deployment led to an enterprise-wide AI roadmap driven by practitioner-led integration.
For a premium chocolate maker, Altimetrik prioritised fixing master data issues before advancing analytics, ensuring sustainable impact and ROI, Nair substantiated.
Enterprise-Ready, Talent-First
ALTI Lab also builds a native AI-first workforce. The company has invested in structured in-house ecosystems, offering continuous learning, certification programs and real-world projects for employees to gain hands-on experience in building, piloting and scaling AI solutions.
This internal transformation is tightly linked to the lab’s overall financial contribution, which is measured through KPIs such as new client acquisitions, growth in existing accounts and workforce upskilling across four internal cohorts.
However, keeping up with the ever-evolving AI landscape remains a challenge. Rather than chasing every new trend, Nair pointed out that ALTI Lab sticks to a fundamentals-first philosophy, anchored in enterprise architecture principles. This ensures client roadmaps are sustainable, integrated with legacy systems and aligned with long-term business value.
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