Fractional CTO

Most startups and agencies don't need a full-time CTO. They need one for the decisions that compound: stack selection, vendor vetting, CI/CD pipeline architecture, and knowing what to say when a client's IT team challenges your security posture.

We bring enterprise architecture and DevSecOps experience — zero-trust pipeline design, security-reviewed application development, SOW review, hiring support, and infrastructure thinking that typically lives inside large engineering organizations.

For non-technical founders, that also means:

• Reviewing SOWs before you sign and sanity-checking estimates where contractors may be padding hours
• Running technical interviews so you're not hiring blind
• Increasing technical velocity by mentoring developers who haven't had the benefit of a structured engineering culture — instilling the code review discipline, testing standards, and delivery rigor that separates a dev shop from an engineering organization

Reviewing a SOW or hiring for a technical role? Send us the details.

We are the agency's agency.

Most agencies have developers. Very few have a Technical Leader or Advisor. When a client's IT team starts asking about security posture, API gateways, or service boundaries — that's where engagements fall apart.

We step in as that technical authority: reviewing vendor proposals, setting architecture direction, and translating between your creative team and enterprise IT. We've been that person before. We know what the other side of the table is asking.

Example: digital workflow tooling with high-resolution file support.

Modern Cloud

Deploying to AWS isn't the same as architecting for scale. Multi-region failover, containerized workloads, CI/CD pipelines, service mesh, zero-downtime deployments — these decisions compound. A wrong call at the architecture layer costs months later. We've operated at the level of enterprise systems engineering and bring that experience to every infrastructure engagement, including for teams who've never needed it before but are about to.

We've orchestrated compute-intensive batch workloads in production — GPU render fleets provisioned on demand against queue depth, scaled to peak throughput, and deprovisioned after job completion. Not just apps deployed to the cloud. Workloads engineered for it.

AWS accounts accumulate quietly. A company that's been running on AWS for a few years typically has ghost instances from cancelled projects, detached EBS volumes that were supposed to be temporary, snapshots from three years ago, and security group rules pointing at IPs that haven't been valid in months. We run a structured audit: full inventory catalog, orphaned resource identification, and a pass over every inbound security policy for stale or overly permissive rules. The output is a prioritized list of what to cut and what to lock down — alongside what the monthly bill should actually look like.

AI/ML

Most AI work stalls between prototype and operations. We help close that gap: architecture, deployment paths, model-provider routing, security guard rails, admin controls, logging, and human review workflows that let teams ship AI features without losing control of the system around them.

Recent work spans vision analysis, retrieval-backed search, and AI-assisted media workflows. In haus-video, that has meant ideation and storyboard generation, review-at-stage controls, guided scene mapping, cast consistency, voiceover, captions, and final render inside the same production environment rather than across disconnected tools.

We also built a video archive search pipeline for a podcast library with 200 episodes: ingest from S3, transcribe with Whisper, generate .srt files, index the transcripts in a vector store, and return answers with source citations including video title, publish date, and exact timestamp. The value is not just the model output. It is the production path around it.