Manara
A serverless backend for AI: build and run AI features without managing the infrastructure underneath.
AI-Native Engineering
AI systems that hold up in production.
Most AI demos well, then comes apart under real work. We build AI into the systems an enterprise can't afford to have fail: engineered to keep working, and defensible when an auditor, a regulator, or a board asks.
Drawing the line · Building the guardrails · Watching it after launch
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The problem we are built for
A team builds AI. It demos well, gets approved, meets real use, and returns a wrong answer, or quietly drifts. Now there's a risk no one can size, an audit question no one can answer, and a system no one trusts.
This isn't an AI problem. It's an engineering problem. No one drew the line between the parts that must be exact and the parts that use judgment, and no one built the guardrails to contain the judgment parts. That's the work we do.
01 / Design
Drawing the line
We decide with you what must stay exact (billing, validation, access rules, anything an auditor will ask about) and what should use judgment. Most failures begin here, with the line drawn carelessly. We can defend every call.
02 / Engineer
Building the guardrails
Limits on what the AI can touch, grounding in your real data, defined behaviour under uncertainty, and tests that check whether answers are good. The engineering most vendors skip, and the part we're built around.
03 / Operate
Watching it after launch
Monitoring for whether the system is still right, not only still running. Models drift and data changes; correctness has to be watched the whole time the system is live, not confirmed once.
Trusted in production
Jazz
NEOM
Motorola
MTN
Telenor
Three practices, the domains where our method has depth.
Not a long list: depth is what tells a buyer we understand their kind of problem.
Agentic & Hybrid Systems
New systems that are part written and part grown, built so the AI can't break the whole.
Net-new
Guardrails
Legacy Modernisation
Bringing AI and modern discipline into enterprise systems that already run, without breaking them.
Modernise
OSS/BSS
Infrastructure Operations & Assurance
AI-native systems that run, monitor, secure, and assure large operational infrastructure.
Assurance
Security
Three ways to work with us, in the order an engagement runs.
Three engagement modes, in the order an engagement usually runs. A client can enter at any point.
01
Architecture ReviewStart here
A short, paid diagnostic. We assess an existing or planned AI system, draw the line, and tell you where it will fail. Delivered as a clear written assessment.
Start a review02
Build
We build the system: architecture, guardrail engineering, production hardening, and delivery. A modernisation engagement is a Build with a different starting point.
Start a Build03
Managed Services
We operate and assure the system in production: correctness monitoring, drift detection, and the work that keeps it dependable as models and data change.
Hand it overFive platforms. Each one evidence the method works.
We don't only advise. We build and incubate our own products, each one evidence the method works in production. Each runs as its own company, on its own site.
Backend
Performio
A vendor-agnostic network performance platform that gives regulators and operators an independent, real-time view of mobile network quality.
LYRS
An AI-native ecommerce platform that runs B2C, B2B, and marketplace commerce from one foundation.
Bayan & AUNO
An agentic AI platform for contracts and document management, and an observability and drive-testing platform for network operators.
Field notes from operating AI systems, not announcements.
What we've learned drawing the line, building the guardrails, and watching systems after launch.
Why AI systems fail in month three
The gap between a system that demos well and one that holds up, and the specific reason it opens.
Drawing the line
Deciding what must be exact and what should use judgment is the most consequential call in an AI build.
Monitoring has to watch a new thing
Old monitoring asks whether a system ran. An AI system also has to be watched for whether it is still right.
Start with the question, not the build.
An AI system built with full process control can still get things wrong after launch. A clean audit trail records the failure; the guardrails and the correctness monitoring are what prevent it. If you have a system that worries you, or one you are about to build, an Architecture Review tells you where that risk sits: short, paid, and specific, before any larger commitment.