Available for Q4 engagements
HubbardAI designs, builds, and operates AI and automation systems for teams who need results in production — not another pilot that stalls at the demo. We own the whole stack, from the model down to the storage layer.
The gap
The demo works. Then it meets real data, real load, real compliance, and a storage layer nobody owns. Suddenly the question isn't "can the model do this" — it's who runs it, what it costs, how you know it's still correct, and what happens at 3am.
We work the other direction: start from the operational reality, then add intelligence where it actually pays for itself.
Services
Engage one practice or the whole chain. Most clients start with an assessment and grow into a build-and-operate relationship.
Where AI earns its keep in your business — and where it doesn't. Readiness assessments, opportunity mapping scored on value and effort, build-vs-buy calls, vendor and model selection, and executive and team enablement.
Bespoke LLM systems built to your domain: retrieval over your own corpus, tool-using agents wired into real APIs, copilots for internal teams, and document understanding that survives messy inputs.
The unglamorous work that returns the most: intake, triage, reconciliation, reporting, and the copy-paste between systems nobody owns. Integrations and pipelines that run without a human babysitting them.
Shipping is the start. We run what we build: quality and drift monitoring, eval regression suites in CI, cost and latency budgets, incident response, and a steady cadence of measured improvement.
Clusters that are boring on purpose. Design, build, and hardening for on-prem, cloud, and hybrid — plus the GPU scheduling, autoscaling, and GitOps discipline that AI workloads demand.
Deep, specialist help with Ceph on Kubernetes — the layer most teams inherit and few genuinely understand. Greenfield design, performance tuning, painful upgrades, and recovery when a cluster is already unhealthy.
Approach
No six-month discovery phase. We aim to have something real in front of your users inside a month, then harden it.
One focused week. We map the workflow, the data, and the systems it touches, then score opportunities by value against effort. You get a written roadmap and a straight answer on what's worth building — including "nothing, yet."
Deliverable Opportunity map, architecture sketch, cost model
Two to four weeks against your real data, not a sanitized sample. We build the thinnest thing that proves the value, wrapped in an eval suite so "is it good enough?" becomes a number instead of an opinion.
Deliverable Working prototype, eval harness, baseline metrics
The prototype becomes a system: hardened infrastructure, CI with eval gates, observability, access control, cost ceilings, and a rollback path. Deployed to your cloud or your cluster — never locked to ours.
Deliverable Deployed system, IaC, runbooks, dashboards
We run it while your team learns it, or hand it over clean. Either way: monitored quality, tracked spend, scheduled model upgrades, and a monthly review of what to fix, cut, or expand next.
Deliverable SLOs, on-call, monthly improvement cycle
Platform
We pick tools that your team can still operate after we're gone. That usually means open standards, infrastructure as code, and an exit path from every managed service we recommend.
Your models stay swappable, your data stays yours, and everything we build ships with the documentation to run it without us.
Engagements
Fixed scope where it makes sense, retained capacity where it doesn't. Every engagement starts with the same free 30-minute call.
Fixed fee · 1–2 weeks
A decision-grade answer on what to build, what it costs, and whether it's worth it.
Project or monthly · 1–4 months
From prototype to a production system your team owns, with the platform underneath it done properly.
Monthly · ongoing
We keep it healthy, current, and cheap while your roadmap keeps moving.
About
HubbardAI is a small, senior consultancy. The people you meet on the first call are the people who write the code, design the cluster, and take the page when something breaks. There's no bench to staff and no junior team to hand off to.
That combination is deliberate: applied AI teams rarely have deep infrastructure expertise, and infrastructure teams rarely have applied AI expertise. Systems break in the seam between them. We work that seam.
Including "don't build this" and "you don't need us for that."
Code, infrastructure, and documentation in your repositories from week one.
If we can't define what success looks like numerically, we don't start.
Success is your team running it confidently without us.
FAQ
It's the right starting point. A good share of the automation work we do involves no model at all — it's integration, data plumbing, and removing steps. The assessment is built to tell you honestly which parts of a workflow deserve a model and which just need better engineering.
Yes. We deploy into your cloud account or your own hardware, and we regularly build fully self-hosted systems on Kubernetes with open-weight models where regulation, residency, or policy requires it. Rook-Ceph is often the storage layer underneath those.
Because that's where AI systems actually fail. GPU scheduling, storage throughput, and cluster reliability decide whether a model ever serves real traffic. We took on the infrastructure practice because handing those problems to someone else meant we couldn't be accountable for the outcome. Both practices also stand alone — plenty of clients hire us purely for platform work.
Yes, and it's a common first engagement. We start with a health and capacity triage — placement groups, CRUSH topology, OSD behaviour, recovery throttling — stabilize the cluster, then fix the design decisions that caused the problem rather than just clearing the warning.
Assessments are a fixed fee. Builds are fixed-scope when the scope is genuinely knowable and monthly when it isn't — we'll tell you which one your project is. Retainers are a flat monthly rate by coverage level. No per-seat pricing, and no margin on infrastructure you buy directly.
You keep everything: source, infrastructure as code, evals, dashboards, and runbooks, all in your own repositories from the first commit. We run a handover with your engineers and stay available for questions. If you'd rather we keep operating it, that's the retainer — but it's a choice, not a dependency.
Assessments usually begin within one to two weeks. Build engagements depend on current capacity — we deliberately run a small number of projects at once so that senior attention is real rather than nominal. Ask on the call and you'll get a specific date.
Contact
A 30-minute call, no pitch deck. Describe the workflow, the system, or the cluster that's causing pain and we'll tell you how we'd approach it — whether or not that involves hiring us.