Your team knows the process. NeoBram turns that knowledge into private, production-ready AI systems and helps you deliver AI projects to your own customers.
NeoBram is an AI engineering partner for industrial SMEs and firms serving manufacturing, pharma, oil and gas, and EPC across India, the Middle East, Europe and the United States.
Bring one workflow, bottleneck or customer opportunity to a working conversation with an AI practitioner.
Offline, on-premises, private cloud or edge selected for the real operating environment.
01 / Partnership paths
Your experts remain the domain authority. NeoBram owns the AI engineering work agreed for the project.
For industrial SMEs
Choose a measurable first use case, validate the data, build a private pilot and enable your own team to operate it.
For firms serving industry
Add NeoBram's AI engineering to your automation, equipment, EPC, engineering or software delivery while you keep the customer relationship.
02 / Start with the work
Each pattern begins with a user, a decision, available evidence and a failure condition.
Knowledge
Capture hard-won expertise from SOPs, manuals, drawings, maintenance history and experienced people before retirement or turnover puts it at risk.
02Vision
Assist inspection teams with defects, assembly checks, PPE and hazard detection at the edge or inside the plant network.
03Reliability
Combine sensor, historian and maintenance evidence to prioritize attention before a failure or process drift becomes costly.
04Automation
Reduce repetitive document and approval work while keeping people responsible for exceptions and high-impact decisions.
05Sustainability
Find abnormal consumption and compare operating modes without presenting a calculator estimate as a measured saving.
06Co-delivery
Add private AI, RAG, computer vision or governed agents to the industrial products and projects you already deliver.
03 / Industry 5.0 lens
We use the European Commission's human-centric, sustainable and resilient pillars as design questions not as a certification claim.
Capture expertise, reduce repetitive work and keep responsible people in the decision loop.
Measure energy, scrap, rework and compute cost alongside financial value.
Design for privacy, fallback, monitoring, maintenance and skills transfer.
04 / Private deployment
Deployment design depends on sensitivity, connectivity, latency, hardware, support access and licence rights.
Local models and data stores for restricted or air-gapped environments when licences and hardware permit.
AI services inside the client data centre or plant network with client identity and monitoring controls.
Deployment inside the client's cloud tenancy, region, networks, keys and storage boundary.
Local inference near cameras or equipment with governed synchronization when connectivity is allowed.
Contract-defined transfer package
Third-party models, libraries, data and connectors remain under their own licences.
Integration contexts
Product names describe possible environments, not partnerships, certifications or guaranteed connectors.
05 / Learn, calculate, decide
No login is required. Calculator outputs are illustrative estimates built from your inputs and visible assumptions.
Review human-centricity, sustainability, resilience and AI delivery foundations.
02Estimate downtime exposure and the value of avoided events from your inputs.
03Model inspection time, defect escape and rework assumptions.
04Estimate utility savings using visible assumptions you can change.
05Estimate time recovered when people find approved knowledge faster.
06Model rejected-batch and investigation-cycle exposure without promising an outcome.
06 / Industry learning hubs
NeoBram supplies AI engineering. Client experts retain process, safety, quality and regulatory authority.
07 / Frequently asked
Every answer remains present in the page HTML even when the visual panel is closed.
Ask a specific questionNeoBram is an industrial AI engineering and enablement company based in Bengaluru, India. We help manufacturing, pharmaceutical, oil and gas, and EPC teams choose practical AI use cases, prepare data, build and integrate working systems, deploy them inside an agreed security boundary, evaluate performance with domain experts, and transfer the operating knowledge to the client team.
Our primary focus is asset-heavy and regulated work in manufacturing, pharmaceutical manufacturing, oil and gas, and EPC or construction delivery. We bring AI engineering patterns; the client supplies the process, quality, safety and regulatory authority. A project proceeds only when named client experts can define the domain rules and accept the result.
NeoBram's engineering base is in Bengaluru, Karnataka, India. We work with teams in India, the Middle East, Europe and the United States through remote collaboration and scoped on-site work. Delivery location, working hours, data residency and travel requirements are agreed for each engagement rather than assumed from a sales region.
A planning sequence is usually 1-2 weeks for qualification, 2-4 weeks for readiness work, 4-6 weeks for a technical proof of value, and 8-12 weeks for a focused production pilot. Multi-site rollout often needs 3-6 months or more. These are planning ranges, not guarantees; timing starts after scope, access, data, owners and acceptance criteria are available.
Yes, when the use case, hardware and model licences support it. We compare fully offline or air-gapped, on-premises, private-cloud, edge and hybrid designs. The decision includes identity, logging, patching, backup, monitoring and support access not only where inference runs. We document which data can cross each boundary before implementation begins.
The contract should state this precisely. NeoBram can transfer client-specific source code, configuration, prompt and retrieval assets, evaluation cases, deployment files and documentation. Fine-tuned weights or adapters can be transferred when the selected base-model licence permits it. Third-party models, libraries, data and connectors remain subject to their own licences and are not automatically owned by the client.
We design integrations around the interfaces the client can lawfully and safely expose, including APIs, files, databases, event streams, OPC UA and MQTT. Common environments include ERP and maintenance systems, MES, SCADA and historians, LIMS, QMS and document systems. A named product on this site is an integration context, not a claim of vendor partnership or a pre-certified connector.
NeoBram supports risk assessment, requirements, traceability, testing evidence, audit logging, access control, change control and human approval. The pharmaceutical company's qualified quality and validation professionals remain responsible for the intended use, predicate-rule interpretation and release decision. We describe solutions as GxP-aligned only when the workflow is designed for that environment; we do not claim that an AI product is automatically compliant.
We do not publish a universal pilot price because cost depends on data condition, interfaces, hardware, validation, security, travel and support. The first step is a bounded scope with assumptions and exclusions. Our public calculators are illustrative business-case tools; they do not quote NeoBram fees, predict a guaranteed return or replace a feasibility assessment.
The pilot ends with an evidence review, not an automatic rollout. Client owners compare the agreed baseline, acceptance tests, failure cases, operating cost, security findings and user adoption. The decision may be to stop, revise, scale one workflow, or prepare a wider rollout. Handover should include code and configuration covered by contract, evaluation assets, documentation, training and an operating plan.
NeoBram does not replace the client's domain experts. Operators, engineers, quality teams, safety leaders and customer-facing specialists own the process truth and acceptance decisions. We bring AI architecture, data and model engineering, application delivery, evaluation, deployment and enablement. The partnership works when those responsibilities are explicit from discovery through production support.
Bring one real workflow
We will discuss the user, baseline, data, business value, failure conditions, deployment boundary and smallest responsible pilot.
