soft guideINFRASTRUCTURE & ADVISORY

Platform / Infrastructure as a product

Your product,
on a platform that looks after itself.

A production-grade, secure AWS and Kubernetes platform built from a tested baseline, delivered into your accounts as your own code, with optional AI agents that watch it and propose the fixes.

01

Launch

You have a product. You need somewhere production-grade to run it.

  • AWS organization and landing zone
  • A cell: VPC, EKS on Graviton, Cilium, Karpenter
  • GitOps with Flux; Terraform and Terramate for AWS
  • Atlantis: plans on pull requests, policy checks, approved applies, drift detection
  • Prometheus, Grafana, Loki and Tempo with alerts as code
  • Architecture guide, runbooks and a handover week
Discuss Launch ↗
02

Secure

Everything in Launch, plus the controls auditors and attackers both test first.

  • Organization guardrails: SCPs and RCPs with offline tests
  • Admission policy, tested both ways, enforced in production
  • Default-deny network policy with declared capabilities
  • One controlled path to the internet
  • Secrets with rotation; backups with tested restores
  • Evidence pack mapped to CIS and AWS Foundational Security Best Practices
Discuss Secure ↗
03

Autonomous

Everything in Secure, plus agents that watch the platform and propose the fixes.

  • SRE agent: investigates every alert, reports cause and evidence
  • DevSecOps agent: turns findings into patch pull requests
  • Ask the platform from Slack; approve actions with a button
  • Own identities, least privilege, approval by blast radius
  • Spend limits, redaction and a full audit trail
  • You keep the agents and their skills as your own code
Discuss Autonomous ↗

Fixed-scope packages. Each one includes the one before it. Send a few lines about your product and you get a scoped proposal within two working days.

Just one piece

Start with
what hurts.

Every package is made of modules that also stand on their own.

CI/CD foundation
Atlantis with policy checks, reusable pipelines, OIDC to AWS, signed images, drift detection.
AWS guardrails
SCPs and RCPs, permission boundaries, a dry run against your CloudTrail history.
Kubernetes hardening
Admission policy, default-deny network policy, workload identity.
Controlled egress
One allowlisted path out for clusters and CI runners.
Observability
Metrics, logs, traces, SLO burn-rate alerts, dashboards as code.
SRE agent
For a cluster you already run, with read-only access to start.

How it works

From an idea
to a platform you own.

01

Discover

One week. Your product, constraints and compliance needs, written down as decisions you can review.

02

Build

Three to six weeks. The baseline, adapted to you, in your GitHub organization and your AWS accounts from the first day.

03

Hand over

Runbooks, decision records and a pairing week. Your team can operate and extend everything.

04

Operate, optionally

Upgrades, reviews and on-call for the platform, or agents that do the first hour of every incident.

AI-native, not AI-unsupervised

Agents do the legwork.
People decide.

The SRE agent investigates every alert with read-only tools and reports the cause with the evidence behind it. The DevSecOps agent turns new vulnerability findings into patch pull requests. Neither changes anything on its own: a runbook action needs an approval in chat, checked in code against who approved; anything bigger is a pull request your team reviews.

Each agent has its own identity and the least privilege its task needs, a spend limit, secret redaction, and an audit trail of every tool call. They run in your cluster, and their skills are your code.

How the SRE agent works ↗

Proof

Built before it was sold.

Start a conversation

Bring your infrastructure
into focus.

Tell me what you are building, operating, or trying to improve.

Let’s talk