The Aixod Platform is our R&D-driven engine for automating secure-by-default cloud foundations, network baselines, and AI workload optimization — so startups can launch faster without compromising on security or reliability.
Most AI startups don’t have an in-house cloud security architect. They still have to make production-grade decisions on networking, IAM, data protection, and cost — often while rapidly iterating on their models. Aixod’s platform turns this expertise into repeatable automation.
Every new project starts from scratch: VPC design, IAM roles, firewall rules, logging, keys, policies. It works for a small proof-of-concept but breaks quickly under real scale or regulatory requirements.
Aixod captures proven architecture patterns — network segmentation, IAM boundaries, logging pipelines, encryption controls — and expresses them as configurable blueprints that can be applied consistently.
With foundations handled, teams can put their energy into training, evaluation, and product features. Cloud setup becomes a solved problem — not a constant distraction.
Our R&D is structured into four pillars that together form a complete “cloud foundations engine” for AI workloads.
Opinionated templates for VPCs, subnets, routing, firewalls, encryption, and logging. Inputs come from a short questionnaire about workloads, data, and user flows.
Workload-aware recommendations that choose the right combination of instances, accelerators, load balancers and scaling policies for training and inference.
A lightweight rules engine that continuously checks IAM, network exposure, logging coverage and data-at-rest / in-transit protections against a set of reference architectures.
A rules-based and ML-assisted engine that takes structured inputs from teams and maps them to architecture blueprints, manifests, and diagrams.
We’re actively exploring several technically challenging areas where there is no obvious off-the-shelf solution. These form the backbone of the platform roadmap.
Generating micro-segmentation and east-west controls from high-level service descriptions, rather than writing firewall rules manually.
Understanding IAM sprawl and configuration drift across rapidly evolving teams.
Models that recommend sensible defaults for training and inference infrastructure without requiring deep infra expertise.
Scenario-based simulation of infrastructure decisions before committing to them, focused on AI-heavy environments.
Automatically checking generated architectures against a library of internal best practices and external expectations.
Making the platform usable from where engineers already work: CLIs, pipelines, and chat interfaces.
Founders shouldn’t have to choose between moving fast and doing cloud properly. The platform exists to give them both: velocity and safety.
We’re working with a small number of AI-focused teams to shape the first version of the Aixod Platform. In practice, this means pairing our automation with hands-on architecture sessions, then baking the patterns back into the product.