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The platform layer that gets AI through a security review: access control, PII redaction, prompt versioning and full tracing around every model, deployed in the region you choose.
The layer that makes AI passable at a security review: tenant isolation, PII redaction, prompt versioning, full trace logging and rollback on any deployed change.
Single sign-on through your identity provider, role-based access to models and data, and strict isolation between teams, business units or customers.
Names, ID numbers, account details and health data are detected and masked before a prompt leaves your network, then restored in the response where permitted.
Prompts, model choices and settings are versioned like code, reviewed before release and rolled back in one step if quality or cost moves the wrong way.
Every request is traced end to end with user, version, cost and latency, and scheduled evals flag drift before your users notice a change.
Policies block prompt injection, off-topic use and disallowed content on the way in, and check outputs for leaked data or unsupported claims on the way out.
Everything can run in your VPC or on-premises, with model traffic kept on private endpoints, the same multi-region approach behind our Regionix product.
Instead of each team wiring up models separately, every AI request passes through one controlled path that security can inspect and approve once.
We list your AI use cases, data classes and regulators, then agree with your security team which risks the platform must control and how they will be tested.
How secure ai platform shows up across India, the Gulf and the US. Pick one to see what changes in the process.
Teams use public chat tools informally, with no record of what customer data is being pasted into them.
Staff use approved models through a gateway that masks account data, logs each request and keeps traffic in an Indian region.
Code, documentation and the tests that prove it — yours outright — and the limits it runs inside, agreed before anything goes live.
Each stage ends in something you can hold — a document, a demo, a passing eval, a dashboard. Nothing carries over on trust alone.
A paid two-week audit of your processes, data and systems. We come back with a ranked list of what AI should touch — and what it should not.
Audit report and ranked backlog
Model selection, retrieval design, guardrails and the integration surface. You get a written architecture with a cost model attached to it.
Architecture doc with cost model
Two-week sprints to implement agents, integrate with your systems and run internal evals. You see working software early and often.
A working demo in your environment
We run your real use cases, measure accuracy, latency and cost, and pressure-test edge cases with your team before go-live.
Evaluation report with KPIs
We help you launch, monitor and continuously improve. You get playbooks, dashboards and regular reviews to scale safely.
Live dashboards and runbooks
Still deciding?
Thirty minutes with an engineer who builds secure ai platform. No deck, no discovery form.
Talk to an engineerWe do not claim certifications we do not hold. Our engineering and access practices are modelled on SOC 2 controls, and the platform we build is designed to support your own audits: access logs, change history, data retention settings and documentation your security team can review line by line.
Most engagements combine two or three of these. Discovery is where we tell you which.
Agents that take a task, use your tools and finish it.
ExploreAnswers from your own documents, with the source cited.
ExploreModels tuned and tested on your task, not a benchmark.
ExploreProcesses that run end to end, people only on exceptions.
ExploreA costed plan of what to build, buy or leave alone.
ExploreBring one process you think an agent could run.
We'll tell you straight whether it's worth building — and what it would cost if it is.