Ombrulla builds custom AI solutions tailored to your requirements, your data, and the tools your teams already use. GenAI and LLM apps, computer vision, predictive analytics, and document automation, delivered in a way you can measure.We run discovery and pilots with distributed teams, with delivery planned around your time zone
Most “AI products” fail because they do not match how work actually gets done. Our work starts with your workflow and the systems behind it, then we build what fits.
If your engineers keep digging through SOPs and old tickets, we can make answers searchable and permission-safe. If quality varies by shift, we can make checks consistent. If planning is always late, we can build forecasting that is usable by the team that owns the decision. The point is simple: less manual effort, fewer avoidable errors, and results you can track in the numbers you already use.
We build different things for different teams and give solutions to different problems, but the pattern is consistent: take a real process, connect the right data, and ship something people can use inside their existing tools.
That might be a GenAI assistant for internal knowledge, vision inspection on a line, forecasting for demand or failures, or document automation for reports and approvals. What you get is not a generic tool.
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.
ROI usually comes from boring wins: time saved on repeat work, fewer mistakes, fewer delays, and fewer “we will get back to you” loops between teams.
If a process is manual today, AI can remove steps. If decisions are slow because data is scattered, AI can pull the right info into one place. If downtime and defects show up as surprises, AI can flag early signals so teams act sooner.
We work with teams across regions and time zones, delivering custom AI solutions globally with clear security and delivery plans.
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.
We treat AI like production software, because that is what it becomes the minute people depend on it. So we version the model and prompts, test changes before they go live, and roll updates out in small steps instead of flipping a switch.
Once it is running, we watch for the stuff that usually breaks trust: answers drifting, costs creeping up, or latency getting worse. If something starts slipping, we catch it early and roll back or adjust before it turns into a fire drill.
Teams usually choose Ombrulla when they need AI that fits their workflow and can run inside real enterprise systems like ERP, MES, CRM, and internal tools. We focus on reliability, access control, and measurable ROI, not just getting a model to work once. We also start with a pilot in the real workflow, so you can see value early and decide whether scaling is worth it.
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.
These are the core capabilities we use to deliver custom AI solutions that fit your requirements, run safely in your environment, and keep working as usage grows.
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.
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.
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.
Ombrulla's custom AI solution delivers measurable ROI, operational resilience, enterprise-grade governance, and a scalable architecture that integrates seamlessly across your stack.
Oil and gas work gets messy when production data, maintenance logs, and safety checks live in different tools and people rely on memory. The focus is usually simple: fewer surprise shutdowns, fewer repeat issues, and faster decisions in the field.
Oil and gas work gets messy when production data, maintenance logs, and safety checks live in different tools and people rely on memory. The focus is usually simple: fewer surprise shutdowns, fewer repeat issues, and faster decisions in the field.
Most automobile teams feel pressure when defects are found late, the line stops unexpectedly, or warranty cases drag. The goal is not fancy analytics. It has fewer repeats and faster closure across plant and service.
Construction delays usually come from coordination, not effort. RFIs, BIM, drawings, safety checks, and schedule updates are everywhere, and one missed detail becomes a week of rework.
Manufacturing problems often start as small drift and end as scrap, rework, or downtime. The value is in catching issues earlier and keeping output consistent across shifts.
SLA misses usually happen because exceptions are spotted too late. Late scans, wrong ETAs, and inventory mismatches cause teams to chase problems after customers are already impacted.