Ombrulla designs and delivers custom AI solutions that fit how your business actually operates - built around your data, your tools, and the systems your teams already trust. We engineer GenAI and LLM applications, computer vision systems, predictive analytics, agentic AI, and document automation across regulated and operationally complex environments.
Enterprise-grade applications built on foundation models that generate, summarise, classify, and draft content using your specific business context and rules. Includes prompt engineering, output validation, retrieval grounding, and guardrails to ensure responses meet your accuracy, tone, and compliance requirements at scale.
Retrieval-Augmented Generation assistants that answer questions from your internal documents - SOPs, manuals, contracts, tickets, knowledge bases - with full access control and source citations. Answers are grounded in your evidence, not guessed by the model, and respect every existing permission boundary in your enterprise.
Camera-based AI that detects defects, verifies process steps, reads labels and gauges, and flags safety risks in real industrial conditions. Engineered for variable lighting, non-standard angles, occlusion, and weathering so detection accuracy holds up across sites, shifts, and seasons - not just in laboratory benchmarks.
Machine learning models that predict equipment failures, customer demand, project delays, and operational risk - giving teams the lead time to plan, allocate resources, and avoid the surprises that drive emergency cost. Every forecast is delivered with confidence intervals so decisions are taken with full awareness of uncertainty.
Intelligent document processing that reads PDFs, scanned forms, invoices, and unstructured documents - extracting key fields, classifying content, and routing each item into the right downstream workflow. Combines OCR, natural language processing, and structured data validation to eliminate the manual transcription that slows enterprise back-office operations.
AI inference deployed close to sensors, cameras, and operational equipment - delivering low-latency response and continuous operation in environments where cloud connectivity is intermittent or unavailable. Ideal for industrial floors, remote sites, vehicles, and any workflow where milliseconds and resilience matter more than centralised compute.
The production engineering layer that keeps models, prompts, and agents stable once they are live: versioning, automated testing, staged rollout, drift monitoring, safe rollback, and cost governance. Without this layer, AI systems silently degrade after launch; with it, they get more accurate, faster, and cheaper over time.
When foundation models are not accurate enough for a specialised domain, we adapt them using LoRA, instruction tuning, and preference optimisation - delivering domain-specific accuracy without the cost and risk of full pre-training. Combined with content automation pipelines, this produces consistent, on-brand outputs at enterprise volume.
We treat AI as production software - because that is exactly what it becomes the moment people depend on it. Models, prompts, agents, and integration code are versioned, tested in staging, and rolled out in controlled stages rather than flipped on in production. Once a system is live, we monitor for the issues that erode trust: accuracy drift, cost creep, latency degradation, and unexpected output variance. If a metric begins to slip, we identify the cause, roll back where needed, and adjust before it becomes an operational incident. This is the engineering discipline that turns AI from a pilot into a dependable enterprise capability.
AI only works when the data beneath it is reliable. We build pipelines that pull data from your operational systems in batch or real time, normalise schemas, validate quality, and maintain lineage - so every custom AI solution runs on trusted, up-to-date data rather than fragile snapshots.
RAG infrastructure connects large language models to your internal documents so answers are grounded in evidence, not guessed by the model. We deploy embeddings, vector databases, and retrieval pipelines with access control baked in - so every answer is accurate, source-cited, and permission-aware.
We engineer LLM applications that follow instructions reliably, use tools and functions safely, and return consistent, structured outputs. The discipline covers prompt design, function calling, agent workflows, classical ML where it fits the problem, and LoRA fine-tuning where it clearly improves accuracy and ROI rather than for its own sake.
We test AI systems the same way mature engineering teams test software - so accuracy stays consistent as the system evolves. That means a curated gold dataset, clear scoring rubrics, unit tests for prompts and tools, regression checks, and A/B evaluation against live traffic to catch quality drops before users do.
We design the application layer and integrate it into your enterprise environment so the AI shows up inside the workflow rather than as a separate tool. The architecture uses event-driven services, secure connectors, and user interfaces that show sources, allow undo, and capture structured feedback for continuous improvement.
MLOps and LLMOps keep AI stable after launch by versioning models, prompts, and code; rolling changes out through staged releases with automated testing; and providing rapid rollback when needed. Serving controls, caching, and routing rules keep performance and cost predictable as usage grows from pilot to enterprise scale.
We control access via role-based permissions and SSO, maintain complete audit logs, and enforce your data residency and retention policies. Engineering defences against prompt injection, jailbreaks, and PII leakage are built into every deployment - aligned with SOC 2, ISO 27001, GDPR, and sector-specific compliance frameworks.
We track quality, speed, and cost end to end so SLAs and ROI stay under control. Dashboards cover latency and spend, drift monitoring, token budgets per use case, and intelligent routing rules that avoid slow or expensive model calls when a cheaper alternative will deliver the same outcome.
Access is controlled at every layer, every action is logged, and data handling follows your governance rules - so teams can use the system at scale without exposure to data leaks, policy violations, or audit gaps.
The AI integrates with the tools your teams already work in - ERP, MES, CRM, ticketing systems, collaboration platforms, and shared drives - so outputs appear directly in the workflow rather than in a separate dashboard that nobody opens.
We deploy across public cloud, private cloud, hybrid, and on-premises environments depending on your security posture, latency requirements, data residency rules, and regulatory obligations - without compromising functionality across deployment modes.
Solutions are built in composable services, so you can add capability, swap components, or scale specific services independently without re-engineering the full stack - protecting your investment as requirements evolve.
Models, prompts, and code are versioned, monitored, and tested through CI/CD pipelines - so updates are controlled, accuracy does not drift silently, and the system improves measurably with every release cycle.
We convert your operational data into early signals and clear, ranked recommendations - so teams can act before issues escalate into downtime, delays, rework, or customer escalations.
Vision systems are engineered to handle real-world conditions: variable lighting, camera variation, partial occlusion, weathering, and edge cases that typically break laboratory-trained models. The result is consistent inspection and detection performance across every site and shift.
When decisions must happen on site, we run AI close to the sensors and devices generating the data - delivering low-latency response, local data privacy, and continuous operation even with limited or intermittent connectivity.
| Off-the-shelf AI tool limitation | How custom AI solves it |
|---|---|
| Trained on generic public data; inaccurate on your domain | Trained or fine-tuned on your operational data, maintenance records, engineering documents, and domain-specific terminology |
| Cannot access your internal systems or proprietary data | RAG infrastructure connects the AI to your ERP, CMMS, document repositories, and data lakes with access control applied per user role |
| Outputs go to a separate dashboard or chat interface | Integrated into the workflow: work orders in CMMS, alerts in SCADA, records in MES - output lands where the work gets done |
| One-size governance; cannot enforce your data residency or access rules | Built-in RBAC, data residency compliance (UK / EU / US / India), audit trails, and your specific retention and classification rules |
| Fixed capability; cannot adapt to your process edge cases | Designed around your process including edge cases identified during the pilot - not discovered in production by your users |
| Vendor controls the model; you cannot retrain on your outcomes | You own the data and models; technician feedback, inspection results, and production outcomes continuously improve model accuracy |
| Pricing scales with seats or usage; unpredictable at enterprise scale | Structured for enterprise deployment with predictable total cost of ownership; no per-seat AI fees on top of your existing system licenses |