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How we build AI
AI-native engineering you can put under audit.
We build AI the disciplined way: the requirement is the source of truth, guardrails run in CI, and nothing ships until it passes the spec. Governance is engineered in, not bolted on, so the whole build can be traced and inspected, from the first requirement to the model running on the edge.
The lifecycle
One unbroken line from a written requirement to a model running on the target. Follow a real one: an offline clinical assistant that has to run inside an operating theatre, with no connectivity and zero data egress.
Requirement gathering
Every build starts from a written spec: what it must do, where it must run, and what it must comply with. Intent is captured, versioned and traced, so there is a single source of truth to hold everything else to.
In practice
For an offline clinical assistant: transcribe and summarise inside an operating theatre, with no connectivity and zero data egress, under ISO 13485.
Stakeholder simulation
Before we build, we simulate the people and the real usage at scale, so the requirement is pressure-tested against how load, edge cases and human behaviour will actually hit it, not against an ideal demo.
In practice
We model surgeons, nurses and concurrent theatres to find where the assistant would be interrupted, misheard or overloaded.
Design & analysis
Architecture, model choice and a data strategy, all traced back to the spec. We decide what runs where, which model is small enough for the target, and what “good enough” has to mean before anything is trained.
In practice
An on-box LLM plus retrieval, sized to run on the theatre hardware rather than a datacentre GPU.
Data gathering & preparation
Often the dataset does not exist yet. Sourcing, collecting, cleaning, annotating and versioning it is part of the build, so the model is trained and evaluated on data that reflects the real target, not a public benchmark.
In practice
No off-the-shelf theatre corpus exists, so building and labelling a consented, de-identified one is part of the work.
Guardrails
The controls we add across the system: input and output guardrails, safety and domain policy, and the CI gates that block anything that fails them. Guardrails are engineered in, not bolted on afterwards.
In practice
Outputs that could leak PII or stray outside the clinical domain are blocked before they ever reach a screen.
Validation & UAT
A consistent regression suite re-runs on every model, prompt or data change, inside CI-gated MLOps, so nothing promotes until it passes. Where supervised learning is involved, evaluation is part of the pipeline, not a one-off.
In practice
Every model swap is re-scored on accuracy, latency and hallucination before it can ship to a theatre.
Runs on the target, at scale
Deployed to the actual target system, on the edge, offline, at scale, with zero data egress. Monitoring feeds drift, cost and quality back into the loop, so the model stays validated in production, not just at launch.
In practice
The assistant runs on the theatre box, offline, with drift and cost watched from day one and fed into the next release.
That is what “you can put it under audit” means: an unbroken line from the requirement, through the data and the guardrails, to the model running on the device.
What makes it different
Spec-driven development
The requirement is the source of truth. Intent is captured, versioned and traced from need to UAT, so the build never drifts from what was asked.
Governance, engineered in
An audited ISO 13485, 27001 and 9001 quality system, in from day one. The same QMS that certifies a patient-facing device governs your build.
Guardrails in CI
CI-gated MLOps. Nothing promotes until it passes its spec-derived checks, so quality is enforced by the pipeline, not by hope.
Peer-validated, reported
Structured peer review and reporting at every stage. Validated like a product, with a paper trail an auditor can follow.
How we work
A clear path from first call to handover, and your IP is yours at every step.
Discovery
We learn the problem, the constraints, and where it has to run.
NDA & consent
We sign your NDA on first contact; data consent and handling agreed up front.
Scoping
A costed plan with milestones, owners and a clear definition of done.
POC
A working proof against your real workflow, not a slide deck.
SOW & build
Production engineering under our ISO-aligned delivery process.
Delivery & handover
Source, runbooks and knowledge handed over, your team owns it.
Discovery
We learn the problem, the constraints, and where it has to run.
NDA & consent
We sign your NDA on first contact; data consent and handling agreed up front.
Scoping
A costed plan with milestones, owners and a clear definition of done.
POC
A working proof against your real workflow, not a slide deck.
SOW & build
Production engineering under our ISO-aligned delivery process.
Delivery & handover
Source, runbooks and knowledge handed over, your team owns it.
Your IP stays yours.
Work-for-hire by default. We sign your NDA on first contact, handle data consent and processing to ISO 27001, and hand over source on delivery, no lock-in to our tooling.
Certified & audited
Audited, not aspirational.
Medical devices
Information security
Quality management
That QMS isn't a certificate on a wall. It enforces a spec-driven lifecycle and CI-gated model validation on every build.
US · Muscat · Bangalore · Puttur, ISO 13485:2016 · ISO 27001:2013 · ISO 9001:2015
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