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AI models you can deploy, govern and defend.
Model development, computer vision and generative AI, engineered by people who also ship firmware, MLOps and edge systems. We build models that run where your data lives, including offline and air-gapped, with governance to ISO/IEC 42001 and evidence a regulator will accept.
A model is only as good as the way it is governed.
Anyone can call an API. The hard part is a model that is accurate, reproducible, deployable at the edge and accountable to an auditor. That is the work we do.
ISO/IEC 42001*
the AI management system standard we build model governance to.
Zero egress*
on-device and air-gapped inference, so your data never leaves the perimeter.
Offline*
models that keep serving when the network and the cloud are gone.
Reflecting how we approach AI, computer-vision and MLOps work as a certified AI-native firm.
What we engineer for AI modelling
From data and training to on-device inference, with the governance and MLOps to keep it accountable in production.
Model development and fine-tuning
Task-specific models, fine-tuned language and vision models, and evaluation harnesses that prove accuracy against your data, not a generic benchmark.
Computer vision and point-cloud AI
Detection, segmentation and 3D point-cloud pipelines, with the annotation tooling to build and curate the datasets that make them work.
Generative AI and RAG assistants
On-device LLMs with retrieval-augmented generation and voice agents, grounded in your knowledge base with citations and guardrails.
Edge and offline deployment
Quantized, optimized models that run on-device, offline and air-gapped, with zero data egress, using Ollama, Qdrant and custom runtimes.
MLOps and model lifecycle
Reproducible training, versioned data and models, automated evaluation, monitoring for drift, and clean paths to retrain and redeploy.
Model governance and risk
An AI management system to ISO/IEC 42001, model cards, bias and safety testing, and audit-ready documentation that stands up to scrutiny.
How we work
A governed path from a business problem to a model in production that you can measure, defend and improve.
- 1
Frame and evaluate
Define the task, the acceptance metrics and the risk profile first, then baseline against your own data so success is measurable, not a feeling.
- 2
Build and prove
Develop, fine-tune and test the model with versioned data and reproducible pipelines, and capture the evidence as we go.
- 3
Deploy where it matters
Ship to the cloud, the edge or fully offline, optimized for the hardware and the latency the use case actually demands.
- 4
Govern and improve
Monitor for drift and misuse, keep model cards and controls current to ISO/IEC 42001, and retrain on a documented, auditable baseline.
- 1
Frame and evaluate
Define the task, the acceptance metrics and the risk profile first, then baseline against your own data so success is measurable, not a feeling.
- 2
Build and prove
Develop, fine-tune and test the model with versioned data and reproducible pipelines, and capture the evidence as we go.
- 3
Deploy where it matters
Ship to the cloud, the edge or fully offline, optimized for the hardware and the latency the use case actually demands.
- 4
Govern and improve
Monitor for drift and misuse, keep model cards and controls current to ISO/IEC 42001, and retrain on a documented, auditable baseline.
Selected work
AI, vision and MLOps systems we have shipped into demanding, regulated and disconnected environments. Client names withheld under NDA.
Edge AIRead the case study AI & MLOpsRead the case study
An Offline AI Decision Register for the Operating Theatre, Zero Data Egress
An on-device assistant that transcribes operating-theatre video feeds and generates auditable decision summaries entirely offline, using an on-box LLM and retrieval, so no clinical data ever leaves the room.
0 bytes
Clinical data leaving the room
100%
On-device, offline inference
Adding 3D and Point-Cloud Annotation to a Widely Used CV Tool
Integrating 3D rendering and point-cloud annotation into an established 2D annotation tool, so users move seamlessly between 2D and 3D for autonomous-systems and robotics datasets.
Read the case study AI & MLOpsOne Dashboard Across 8+ DevOps Tools
A single platform that unifies insight from eight or more DevOps tools, with row-level permissions, a configurable dashboard, incremental updates and embedding.
8+
DevOps tools unified in one view
Standards and experience
Built to the standard responsible AI expects.
Governance is not a slide at the end. We design models to recognized AI and security standards from the first line of data, and we back it with real model, vision and MLOps delivery.
Our experience
Edge LLMs, computer-vision tooling and generative agents shipped into regulated, disconnected and mission-critical environments, as a certified AI-native firm.
designed to
Standards we design to
ISO/IEC 42001
AI management system
The management system standard for responsible development and governance of AI, which we are certified to.
Standards we design to
ISO/IEC 27001
Information security
Information security management for the data and models we train, store and serve, which we are certified to.
Standards we design to
MLOps
Model lifecycle governance
Reproducible training, versioning, evaluation and drift monitoring across the full model lifecycle.
Standards we design to
EU AI Act
Regulatory readiness
Risk classification, transparency and documentation practices that prepare high-risk AI systems for the EU AI Act.
The result is models that are accurate, reproducible and deployable at the edge, and governance that holds up when a regulator or an auditor arrives.
Capabilities
Product Engineering
AI, Data & Intelligent Systems
UX & Product Design
Experience Engineering
Cybersecurity
Compliance Engineering
US · Muscat · Bangalore · Puttur, ISO 13485:2016 · ISO 27001:2022 · ISO 9001:2015
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