About NeoBram
AI engineers working beside the people who know the operation.
NeoBram helps industrial operators and engineering firms turn useful AI ideas into working systems. We work with the people who know the process, and with technology and automation partners delivering AI-enabled solutions to their customers.
Leadership and engineering base
Karthick Raju, Chief of AI and Co-Founder, leads NeoBram’s AI practice from Bangalore. NeoBram works alongside industrial operators, engineering firms and delivery partners, combining their process expertise with AI engineering.
Our engagement briefs describe company-reported work and its measurement context. Our validation approach explains how the evidence for a new project is assessed.
One useful problem before a wider programme
We begin with a task or decision that matters to the business: a slow quality review, recurring equipment investigation, tender workload or operational loss. We check the available information, define a useful result and test the approach against the way the work happens today.
The next step may be a focused implementation, better data preparation or a decision to pause. A working prototype is useful only when the team can evaluate it and understand what production use would require.
Engineering across the AI your work needs
Our capabilities cover machine learning and prediction, computer vision, knowledge assistants and chatbots, domain-specific small language models, AI agents and manual-process automation. We also assess simulation and reinforcement learning for suitable decision problems with an appropriate test environment.
Applications can support oil and gas operations, food and beverage production, pharmaceutical quality work, EPC delivery, water treatment and manufacturing. The scope depends on available domain expertise, usable information and the operating constraints. A proposed application is assessed before delivery is agreed.
Based in Bangalore, working across markets
Our engineering base is in Bangalore, India. We scope projects for teams in India, the Middle East, the United States, the European Union and the United Kingdom, with collaboration hours and any site activity agreed for each engagement.
Local, offline, private-cloud and connected deployments are assessed against the customer's information boundary and infrastructure.
See what working together should produce
The first discussion connects a business problem to a piece of work someone can inspect. For an illustrative engineering-document project, that could be a source-linked requirements register. For an operating-data project, it might be a forecast beside the current planning method. The output and its intended user make the scope concrete before technology choices take over.
- Define: the recurring task, current effort, cost of errors and owner of the result.
- Prepare: representative records and examples of accepted work, with permission to use them.
- Build and review: an agreed workflow that exposes its evidence, uncertainty and exceptions.
- Decide: acceptance findings, remaining gaps and the cost of operating or extending the system.
Judge the delivery by useful work
Ask what your team will receive, how they will check it and who owns it after handover. The proposal should state the engineering deliverables, customer contributions, acceptance approach and continuing responsibilities. Customer-specific work and third-party licences need clear treatment.
We assess total workflow effort, including the review and correction that remains. A demonstration is a starting point for evaluation. Expansion should follow evidence of useful work and a supportable operating process, with quality requirements kept in view.
Explore products for your workflow
Explore three starting points for industrial vision, asset and component lifecycle intelligence, and company-specific knowledge. Each product's fit, configuration and deployment requirements are assessed against your workflow and acceptance criteria.
More clarity
Questions and answers
Do you replace our domain experts?
No. Their process knowledge and acceptance decisions are central to the work. We provide the AI engineering around that expertise.
Can you work alongside our existing IT or automation team?
Yes. We agree the division of work with your internal team or delivery partner, including data access, integration, testing and operating responsibilities.
Do we need to know which AI technology to choose?
No. Start with the business problem. We help assess the technical options against the information available and the result you need.
What should we bring to an initial discussion?
Describe one recurring task, who does it, the information they use and where time, money or quality is being lost. A representative output or workflow description can be enough to begin; confidential records can follow through an agreed process.
How long does an industrial AI project take?
There is no standard timeline for every project. Data readiness, integrations, hardware, evaluation and deployment requirements shape the plan. Scope and acceptance checkpoints are agreed before delivery. Reusing a product or component does not remove the need to test it against your workflow.
Start with one business problem