Hibiscus ConsultingWhat a fractional AI CTO engagement covers
Most AI leadership on offer stops at strategy: a deck, a roadmap, and a set of recommendations someone else has to build. The point of this engagement is the opposite. It is senior technical leadership that owns the architecture and stays close enough to the code to ship it, so the decisions and the implementation don't drift apart. That combination is what turns an AI initiative from a demo into something in production.
Sets the architecture
Model strategy, build-vs-buy, data flow, and the seams that let you swap providers later. The calls a product depends on, made by someone who has made them in production before.
Stays close to the code
Not a strategist who hands off. The same person writes and reviews the code that ships, so the architecture on the whiteboard is the architecture in the repo.
Makes AI outputs provable
Evaluation harnesses and signed audit trails so you can show a customer, a regulator, or your own board why the model did what it did, not just that it did it.
Grows with the need
Start fractional. If it makes sense for both sides, the same operator can step into a founding-engineer, senior/staff/director, or contract role as the work steadies.
The production stack this sets up
These are the four pieces most AI initiatives are missing, and the four this engagement stands up for you. Each one is built, not theorized, and each is drawn from production systems already running:
- Model gateways. One seam that routes requests to models and lets tool-using agents plug in, so you can swap providers and control cost and latency without a rewrite. It is where routing, fallbacks, and per-tenant policy live.
- RAG that stays grounded. Retrieval, chunking, embeddings, and grounding designed so the model answers from your data instead of hallucinating, and built to be measured rather than just demoed.
- Evaluation harnesses. Backtests output against historical ground truth, so you know when a prompt, model, or retrieval change actually improved things or quietly broke them. This is the difference between shipping on evidence and shipping on vibes.
- Signed audit trails. A tamper-evident log where every AI decision is signed, anchored, and independently verifiable, using post-quantum cryptography (ML-DSA; provisional patent filed). "Trust us" becomes "here is the proof."
The through-line: production AI whose every decision is signed, anchored, and independently auditable. There is also a live real-time voice agent you can actually talk to, so the claim that this ships end to end isn't a promise, it's a thing you can call.
Leadership at scale, still writing the code
The reason a fractional engagement works here is that the same person carries both the leadership and the hands. For about three years as Director of Engineering at a neobank, the org ran $720M in payments at 100% uptime across Stripe Connect multi-tenant, Plaid ACH, Apple and Google Pay, fraud rules, and KYC/KYB, with 17 engineers across 5 products. That is not a management-only story: I owned all commits on the Snap! Spend customer-facing React app and wrote core payments code across the platform.
The range backs it up. I built a WebRTC platform for the Emmys in React in 5 weeks, and a React dashboard on a Rails EHR (Medaxion), so healthcare experience and roughly a decade of React and TypeScript are in the toolkit. And the multi-tenant payments and field-ops platform behind Hibiscus (Stripe, Temporal, Postgres, Rust and Node on GCP) is the same production discipline applied to AI. That is the judgment a fractional engagement puts on your team from the first session.
A fit for regulated and federally-adjacent buyers
Hibiscus Consulting LLC is an SBIR-eligible small business, which matters if you are federally-adjacent or working toward government-adjacent contracts. More to the point, the whole stack is built around the thing regulated buyers actually need: not a model that is merely accurate, but one whose decisions are provable after the fact. The signed, anchored, independently-auditable audit trail exists precisely because "the AI decided" is not an answer that survives an audit, a dispute, or a compliance review.
If you operate in payments, healthcare-adjacent, or any domain where an AI decision has to be defensible later, the evaluation harnesses and post-quantum-signed audit trails are the parts of the engagement that de-risk you the most. They are what let you put an AI system in front of a regulator or a large customer without holding your breath.
When a fractional AI CTO is the right call
Good fit
- You need senior AI direction now, before you can even scope a full-time role
- You want the architecture set and the first production version shipped, by the same person
- Your AI outputs have to be trustworthy: measured, grounded, and auditable, not just demo-ready
- You are in a regulated or federally-adjacent domain where decisions must be provable
- You would rather rent director-level depth part-time than fund a full-time senior salary before it is warranted
Probably not yet
- You have a proven roadmap and steady, full-time AI work that needs an owner in the building every day
- You need a large team executing in parallel rather than senior direction plus a strong first build
- You want a pure advisor with no hands on the code, that is a different, cheaper engagement
- Your need is a one-off script, not architecture and a production stack
Not sure which of these you are? That is the conversation. These go deeper on what the role does day to day, how it compares to a full-time hire, and the specific stack it builds:
Frequently asked questions
What do fractional AI CTO services actually include?+
Part-time senior AI technical leadership that both sets the architecture and stays close enough to ship the code. In practice that means deciding the model strategy, standing up the production stack (model gateways that route tool-using agents, RAG that stays grounded, evaluation harnesses that backtest against historical ground truth, and signed, auditable trails), and writing and reviewing the code that ships. It is the same operator across strategy and delivery, so the decisions and the implementation never drift apart.
Does a fractional AI CTO write code, or just advise?+
Both, and that is the point. This is not a strategist who hands off a deck. The same person who sets the architecture stays close enough to write and review the production code, so the plan and the repo stay in sync. That comes from a hands-on track record: about three years as Director of Engineering at a neobank running $720M in payments at 100% uptime, owning all commits on the Snap! Spend customer-facing React app and writing core payments code across the platform, while leading 17 engineers across 5 products.
What senior depth am I getting, specifically?+
Production AI end to end from an engineer who has shipped it: model gateways routing tool-using agents, RAG built to stay grounded and be measured, evaluation harnesses that backtest output against historical ground truth, and tamper-evident audit trails signed with post-quantum cryptography (ML-DSA, provisional patent filed). Beyond AI, the toolkit includes Nectar, a Rust programming language that compiles to WebAssembly with a public compiler and 2,500+ tests, a multi-tenant payments and field-ops platform (Stripe, Temporal, Postgres, Rust and Node on GCP), a WebRTC platform built for the Emmys in React in 5 weeks, and roughly a decade of React and TypeScript.
Is this a fit for regulated or federally-adjacent buyers?+
Yes. Hibiscus Consulting LLC is an SBIR-eligible small business, and the stack is built around what regulated buyers actually need: production AI whose every decision is signed, anchored, and independently auditable. The evaluation harnesses and post-quantum-signed audit trails exist so an AI decision can be defended after the fact, in front of an auditor, a regulator, or a large customer. That makes it a fit for payments, healthcare-adjacent, and government-adjacent work where "the AI decided" is not an acceptable answer.
Can a fractional engagement grow into a full-time role?+
Yes. The engagement is deliberately flexible: start fractional to set and de-risk the AI roadmap, and if it makes sense for both sides, the same operator can step into a founding-engineer, senior, staff, director, or contract role as the need grows. Engagements are remote across the US or Triangle-local in Raleigh and Cary, North Carolina. To start the conversation, email Blake at blake@hibiscus.buzz.