Ombrulla designs and builds custom AI solutions — GenAI and LLM applications, computer vision systems, predictive analytics, RAG knowledge assistants, document automation, and edge AI, tailored to your processes, your data, and the enterprise systems your teams already use.
A custom AI solution is built specifically for an organisation's processes, data, and technology stack — not adapted from generic software. It's the right choice when standard tools are too broad, insufficiently accurate, or can't integrate with existing operational systems. , Custom AI spans four capability areas: generative AI and LLMs for content, summarisation, and knowledge retrieval; computer vision for inspection and defect detection; predictive analytics for forecasting, failure prediction, and risk scoring; and document AI for processing PDFs, forms, and unstructured data. Ombrulla builds custom AI for industrial and enterprise operations — starting with a discovery sprint to define the use case and success metrics, validating through a real-workflow pilot, then integrating into ERP, MES, CRM, CMMS, and operational systems for enterprise-wide deployment.
AI apps that generate, summarize, classify, or draft content using your business context and rules.
A Q and A assistant that pulls answers from your internal documents, with access control applied.
Camera based AI that detects defects, verifies steps, reads labels, or flags safety risks in real conditions.
In Predictive Analytics models that predict failures, demand, delays, or risk so teams can plan earlier and reduce surprises.
Automation that reads PDFs and forms, extracts key fields, and routes them into your process.
AI that runs near sensors and devices when you need fast response or limited connectivity.
The setup that keeps models and prompts stable in production, with monitoring, safe updates, and rollback.
When base models are not accurate enough, we adapt them to your domain and automate consistent outputs.
In a 6–8 week pilot the model flagged early failure patterns from vibration and run-hour trends, helping teams move from emergency fixes to planned maintenance, cutting unplanned stops and overtime, with alerts tuned to avoid alarms early.
Computer vision ran line side checks to catch recurring surface & assembly issues earlier, so defects were fixed before moving downstream. Typical impact included lower rework, faster root cause identification, improved Quality Control.
A permission controlled assistant answered repeat questions from SOPs, manuals, and past tickets inside the tools teams already use. Typical impact is less time spent searching, faster ticket handling, and fewer repeated escalations.
AI only works if the data is reliable. We build pipelines that pull data from your systems (batch or real time) and keep schemas clean so your custom AI solutions run on trusted, up to date data.
RAG infrastructure connects LLMs to your internal documents so answers are grounded, not guessed. We use embeddings and a vector database with access control so results are accurate and permission-aware.
We engineer LLM apps that can follow instructions, use tools, and return consistent outputs. That includes prompt design, function calling, agent workflows, and classical ML where it fits, plus fine-tuning (LoRA) only when it clearly improves accuracy and ROI.
We test AI systems the same way we test software, so results stay consistent as things change. That means using a gold dataset, clear scoring rules, unit tests for prompts and tools, and A/B tests with regression checks to catch breaks before users do.
We design the AI application and connect it to your enterprise systems so it works inside the workflow, not as a separate tool. That includes event-driven services, secure connectors, and user screens that show sources, allow undo, and collect feedback.
MLOps and LLMOps keep AI stable after launch by versioning models, prompts, and code, then rolling changes out safely with testing and quick rollback. We use staged releases, serving controls, and caching so performance and cost stay predictable as usage grows.
We control access, keep audit logs, and follow your data residency and retention rules. We also block prompt injection and prevent PII leaks.
We track quality, speed, and cost end to end so you can keep SLAs and ROI under control. That includes latency and spend dashboards, drift monitoring, token budgets, and routing rules to avoid slow or expensive model calls when they are not needed.
Access is controlled, actions are logged, and data handling follows your rules, so teams can use the system without worrying about leaks or policy violations.
The AI connects to tools like ERP, MES, CRM, ticketing, and shared drives, so the output shows up where people already work.
We deploy in cloud, hybrid, or on-prem setups depending on your security, latency, and data residency needs.
The solution is built in parts, so you can add features, swap components, or scale specific services without rebuilding everything.
Models, prompts, and code are versioned and monitored, so updates are controlled and quality does not drift quietly over time.
We turn your data into early signals and clear recommendations, so teams can act before issues become downtime, delays, or rework.
Vision systems handle real conditions like lighting changes, camera variation, and edge cases, so inspection and detection stay consistent.
When decisions need to happen on site, we run AI close to sensors and devices, so you get fast responses even with limited connectivity.
Define goals, agree on what success looks like, choose metrics, and map the workflow.
Validate data sources, access, quality, and gaps before development starts.
Run a pilot in the real workflow, measure impact, then integrate and roll out to users.
Monitor performance, quality, and cost, and continuously improve as needs evolve.
We start small, prove value in the real workflow, then scale only when the numbers make sense. NDA is fine. Onsite or remote delivery. Cloud, hybrid, or on-prem supported. Work planned around your time zone.
Primary Use Cases: Pipeline, tank & rotating equipment inspection. Key AI Detections: Corrosion, coating failure, gauge anomalies, leak indicators.
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Primary Use Cases: Progress verification, quality assurance, HSE audits. Key AI Detections: Cracks, spalling, rebar exposure, milestone deviation.
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Primary Use Cases: Hull, engine room & safety apparatus inspection. Key AI Detections: Fouling, coating degradation, dial anomalies, lashing faults.
Primary Use Cases: Grid asset, substation & meter inspection. Key AI Detections: Insulator cracks, tamper cues, vegetation encroachment.