Walk the floor of a working factory, then a wellhead, a cleanroom, and a construction site. Every machine, sensor, and worker on this page maps to a production AI solution NeoBram can deploy in 90 days.
Scroll to explore each industry
From discrete parts to continuous lines, AI on the shop floor turns sensor noise into action. Predict failures, catch defects in real time, lift OEE, and give operators a copilot that actually knows the machine.
For most plants, a single hour of unplanned stoppage erases a full shift of margin. Vibration, thermal, current, and acoustic signals already exist on the floor - they just sit in PLC registers no one reads. AI changes the economics by turning that exhaust data into 30-60 day failure warnings.
Production VPs we work with stop measuring success in MTBF averages and start measuring it in scheduled-vs-unscheduled maintenance ratio. The goal is calm: no fire drills, no expedited freight, no overtime call-outs.
We deploy sensor-agnostic models that learn each asset's healthy signature, then flag deviation weeks in advance. Works with existing PLC/SCADA, runs at the edge, and never sends raw data to the cloud unless you want it to.
Pilots ship in 8 weeks. We start with a single critical asset class - usually motors, gearboxes, or pumps - prove the savings, then scale across the plant network.
High-speed cameras paired with custom-trained vision models inspect every part as it moves down the line. Surface defects, dimensional drift, missing components, and assembly errors are caught at the source, not at final QC.
Sub-100ms inference runs on factory-grade industrial PCs. The model is yours, the labeled data is yours, and we co-train your team to maintain and expand it.
Live OEE telemetry from every machine feeds AI that pinpoints exactly where availability, performance, or quality losses originate. Energy models layer on top and recommend setpoint changes that cut kWh per unit without slowing the line.
We deliver plant-wide dashboards and shift-level shop-floor displays, so operators and managers see the same numbers and act on them in real time.
Build a physics-grounded digital twin of your line, then layer a factory-floor copilot that answers operator questions in plain language - drawing on SOPs, maintenance history, and live telemetry.
Operators stop hunting through binders. Engineers stop fielding the same Tier-1 questions. New hires get up to speed in weeks, not quarters.
Existing CCTV streams are repurposed by AI to monitor PPE compliance, hazardous-zone intrusions, and unsafe behaviors. Alerts go to supervisor mobile apps in under 2 seconds, with audit-grade event logs for EHS.
Privacy-by-design: faces are never stored, video stays on-prem, and event metadata is the only thing that leaves the camera.
From wellhead to refinery, AI cuts the cost of reactive maintenance, spots leaks before regulators do, and turns turnaround planning from spreadsheet hell into a guided optimization problem.
Upstream and midstream operators face an average of 27 days of unplanned downtime per asset per year. Calendar-based maintenance misses progressive failures; manual inspections in hazardous zones are inconsistent and slow.
AI shifts the model from reactive and preventative to truly predictive - with failure warnings 60 to 120 days in advance and condition-based intervention that extends asset life 30-45%.
Compressors, pumps, top drives, and gas-lift systems stream vibration, temperature, pressure, and acoustic signals to AI models trained on years of failure data. We achieve 90%+ prediction accuracy on the failure modes that matter.
Edge deployment means data stays on the platform or in the gathering station. Inference runs without a wide-area link.
Pressure, flow, and acoustic signals are fused with satellite and drone-based methane plume detection. Operators get a single, prioritized leak feed with geolocation, severity score, and recommended response.
Cuts both environmental risk and regulatory exposure. Designed to integrate with existing SCADA and emergency response workflows.
Turnarounds are the single largest planned spend in most refineries. AI optimizes work-pack sequencing, contractor scheduling, scaffold and crane logistics, and risk-based prioritization of inspection scope.
We integrate with SAP PM, Primavera, and your existing CMMS. The output is a turnaround that finishes on time, on budget, and with fewer surprise findings.
Continuous emissions monitoring fuses sensor data with AI inference to deliver scope-1 and scope-2 reporting that regulators, investors, and ESG auditors accept. No more quarterly spreadsheet scrambles.
We help operators meet methane intensity targets without losing production - identifying the lowest-cost abatement opportunities first.
Wearable sensors and AI-powered CCTV monitor field crews for gas exposure, slips and falls, and unauthorized entry to live energy zones. Permits-to-work are validated in real time against actual location and equipment state.
Site supervisors get a single safety pane of glass; incidents are routed automatically, with full audit trails for HSE investigations.
GxP-grade AI for pharmaceutical manufacturing. Cut deviations and CAPA cycle time, inspect every vial and blister, give SOP-aware copilots to operators, and de-risk cold-chain and pharmacovigilance workflows.
A single significant deviation can take 60-90 days of quality team effort, delay product release, and risk regulatory action. The root cause is almost always knowable - the data exists - but it's trapped across MES, LIMS, eBR, and paper logs.
AI unifies that data into a single deviation narrative, with traceable root cause and recommended CAPA. Quality investigators move from data hunting to true investigation.
Our deviation copilot reads MES events, eBR entries, lab results, and prior CAPAs to draft a complete deviation report with root cause hypothesis and recommended actions. Quality teams review and approve, not rewrite from scratch.
21 CFR Part 11 compliant. Full audit trail, electronic signatures, and explainable rationale for every AI-generated recommendation.
Vision models trained on your defect catalog inspect every unit - cracks, particulates, fill-level deviation, missing or broken tablets, blister seal integrity. Designed to integrate with existing line equipment.
Defect images and labels become a self-improving dataset; the model gets better with every batch, and the IP stays yours.
A copilot grounded only in your validated SOPs, work instructions, and batch records answers operator questions in plain language - with citation back to the exact controlled document and version.
No hallucination, no off-policy answers, and every interaction logged for quality review.
AI fuses shipping telemetry, weather, customs delays, and stability data to predict cold-chain excursion risk before products leave the dock. Shelf-life and remaining stability are recomputed in real time.
QA gets early warning on at-risk shipments; commercial gets a defensible release decision; patients get product that actually works.
Adverse event case intake from email, web forms, calls, and literature is parsed by AI, deduplicated, and pre-coded with MedDRA terms. Safety physicians focus on the cases that need human judgment.
We help PV organizations absorb growing case volumes without growing headcount - and without missing reporting deadlines.
From bid to handover, AI compresses the work that decides whether a large project finishes on schedule. Faster tendering, sharper change-order discipline, automated clash detection, and live project-risk forecasting.
Across global EPC, the McKinsey benchmark is brutal: most large projects deliver late and over budget. The root causes - estimation gaps, change-order leakage, design clashes, and field productivity - are all addressable with AI when applied early enough.
Project directors want fewer surprises. We give them a forecasting layer that flags emerging risk weeks before the schedule slips visibly.
The biggest controllable cost in commercial teams is the time spent on tenders that don't win. AI reads RFP documents, extracts requirements, drafts initial scope and pricing tables, and flags clauses that need legal review.
Estimators move from line-item entry to strategic positioning. Win rates rise because every bid is finally reviewed in detail.
Change orders are where margin disappears. AI cross-references RFIs, daily site reports, and contract clauses to flag commercially significant changes the day they happen - not at month-end reconciliation.
Contract managers get a single live ledger of entitlement, exposure, and outstanding claims, fully traceable back to source documents.
AI extends BIM clash detection beyond geometry into constructability, sequencing, and code compliance. Engineering teams can also ask plain-language questions across drawings, specs, and standards - cited to the exact document.
Less rework in the field. Faster RFI close-out. New engineers find institutional knowledge in minutes.
Existing site cameras are repurposed for AI-based PPE compliance, fall hazard detection, work-at-height monitoring, and restricted-zone intrusion. Designed for active construction environments, not lab demos.
Site managers and HSE leads get mobile alerts in under 2 seconds. Incident rates drop; near-miss visibility goes up.
A project-level digital twin fuses schedule, cost, RFI, change-order, and field-progress data into a single forward-looking forecast. Project controls teams stop chasing yesterday's data and start managing forward.
Owners and JV partners see the same numbers as the project director. Disputes shrink because the data layer is shared.
We assess, prototype, and deploy production AI inside your plant, platform, cleanroom, or job site - with full data sovereignty and 100% client ownership of every model we train.
Our standard engagement model targets a working production pilot in 8-12 weeks, with measurable ROI inside the first 6 months. Multi-asset scale-out typically follows in 90-day waves.
By default, nowhere. We deploy on-prem or at the edge inside your network. Models train on your data, and you retain 100% ownership of every model we build. Cloud is an option, never a default.
No. Every solution above is designed to integrate with your existing PLCs, SCADA, MES, ERP, BIM, CMMS, or LIMS. We add an AI layer; we don't replace your system of record.
Most industrial data is messy. We start with a 2-week data and use-case assessment that surfaces exactly which gaps matter for the first model. We then build the data pipeline as part of the pilot.
Our pharma solutions are 21 CFR Part 11 ready. Manufacturing solutions follow ISA-95 conventions. Oil & gas safety solutions are designed against IEC 61508/61511. We co-author validation packs with your QA team.
We co-train your team during the pilot so internal engineers can extend and retrain models. We also offer managed support if you'd prefer we run them - the choice is yours, not ours.
Fixed-fee for assessment and pilot, then value-based or platform fee for scale-out. We're explicit about ROI assumptions and target payback in 6-12 months.
Yes. Our deployment patterns are designed for multi-site rollout, with central governance, local edge inference, and federated learning where appropriate.