AI consulting that takes you from pilot to production
Most AI initiatives don't fail on model quality — they stall on infrastructure, operations, and governance. Our US-based platform engineers build AI systems that run in production, on infrastructure you control, with your data inside your boundary.
Most AI consulting isn't what it says it is
Before you evaluate us or anyone else, know what you're actually buying. The market splits into three familiar shapes — and none of them ends with AI running in your production environment.
What you're often sold
Strategy decks with no platform delivery
A roadmap presentation, a maturity matrix, and an invoice. Six months later nothing runs in production because nobody on the engagement could build the platform the roadmap assumed.
What to demand instead
Platform-first delivery
AI runs somewhere, and that somewhere is Kubernetes. We design the platform and then build it — GPU scheduling, model serving, GitOps — with the same engineers who wrote the architecture.
What you're often sold
Staffing shops renting bodies
Resumes with 'GenAI' added last quarter, billed hourly, learning on your dime. When they roll off, the knowledge leaves with them.
What to demand instead
Platform engineers who ship
Platform engineers who have run production Kubernetes for a decade and treat models as workloads with SLOs — then transfer ownership to your team with runbooks, not dependency.
What you're often sold
Model-vendor partners steering you to their stack
Advice that always concludes with the sponsor's API, your data in their cloud, and per-token pricing that scales against you.
What to demand instead
Vendor-neutral, sovereign by design
Open-weight and frontier models both have a place. The THNKBIG AI Ontology — a governance boundary — decides what crosses your perimeter, so you get frontier capability without surrendering your data.
What our AI consulting services cover
Every service below is delivered by platform engineers who build and operate the systems — not analysts describing them.
AI Readiness Assessment
Two weeks against your infrastructure, data posture, and use cases. You get a prioritized architecture and a build plan with real numbers — not a maturity matrix.
Sovereign AI Platform Build
Metal to model: bare metal or cloud GPUs, Kubernetes, GPU scheduling, inference serving, and the governance boundary — inside your perimeter.
GPU Infrastructure
On-prem clusters (Dell, Supermicro, Cisco validated designs) and cloud GPU capacity — sized, scheduled, and utilized instead of idle.
Self-Hosted LLM Deployment
Open-weight models — Qwen, Kimi, MiniMax, Gemma — served production-grade on your hardware, with no per-token fees on steady inference volume.
LLM Evaluation & Governance
Evaluation harnesses and the AI Ontology boundary: models reason over typed schemas, never raw records; every call audited; guarantees public, mechanics private.
MLOps & GitOps Operationalization
The 66%-run-it / 7%-ship-daily gap is operational. Argo CD-driven delivery, observability, and rollback discipline for models like any other workload.
Team Enablement
Your engineers run the platform when we leave. Runbooks, pairing, and knowledge transfer are deliverables, not afterthoughts.
Assessment to production, in increments that ship
No 18-month transformations. Each step ends with something you own — an architecture, a running platform, a team that operates it.
Assess
Infrastructure, data boundaries, use-case triage, and a build-vs-API economics read. Ends with an architecture you could take to any vendor — including not us.
Architect
The reference design made concrete: hardware and cooling reality, cluster topology, model selection, the governance boundary, and what crosses it.
Build
Focused builds ship in a six-week rapid strike — our record engagement took a client from idle GPUs to production inference in exactly that. Larger platforms phase in behind working increments.
Operate & Enable
Bounded handover by default — your team owns it. Continued coverage under defined SLAs if you want it. Either way, we make ourselves optional.
AI platforms that made it to production
The CNCF's numbers say it plainly: 66% of organizations run AI workloads on Kubernetes, but only 7% deploy models daily. The gap is operational — and closing it is exactly what a platform-first firm does. Two of the engagements below are named clients, published with their approval.
Enterprise AI Rapid Strike
Idle GPUs to production AI in six weeks
GPU cluster that sat dark for months serving production inference in six weeks — platform, serving, and governance included.
Sovereign Infrastructure
On-prem AI cluster buildout
Bare-metal GPU platform designed, racked, and operationalized on Kubernetes — the full metal-to-model path.
Delta Data (named client)
Delta Data: platform assessment on AKS
Platform assessment and Kafka-on-Kubernetes delivery for a financial-services software firm — published with their approval.
Mimrr (named client)
Mimrr: startup platform build
Production platform for an AI-powered developer-tools startup — built to scale from day one.
Frequently asked questions
What does an AI consulting company actually do?
The useful ones close the distance between a model that works in a demo and a system that works in production: infrastructure (GPUs, Kubernetes, networking), delivery (GitOps, observability, rollback), and governance (what data models can see, what leaves your boundary). The less useful ones produce strategy documents and leave the hard part — running AI as a production workload — to you. Ask any firm you evaluate to show you something they operate today.
How should we choose between AI consulting firms?
Three filters eliminate most of the field: Can they build and operate the platform, or only advise? Are they financially neutral on your model and cloud choices? Can they show named clients with concrete outcomes? Then ask who actually shows up — the partner who sold the engagement or the engineers who deliver it. At THNKBIG the people who design the architecture are the people who build it.
Do we need to buy our own GPUs?
Not necessarily. Steady, predictable inference volume usually justifies owned hardware within a year; spiky or exploratory workloads are often better on cloud GPUs or hosted APIs. The assessment includes this economics read against your actual usage — and if the honest answer is 'stay on APIs for now,' that's the recommendation you'll get.
Can we use Claude or OpenAI and still be sovereign?
Yes — sovereignty is about controlling what crosses your boundary, not refusing to use frontier models. The AI Ontology governance layer lets hybrid work safely: open-weight models handle workloads that must stay inside; frontier APIs handle what's allowed out, seeing typed schemas rather than raw records, with every call audited and redacted.
How long until we're in production?
For a focused use case with hardware available: about six weeks from kickoff to production inference — we've done it. A full platform (multi-team, governance, self-service) typically lands in phased increments over one to two quarters. If a firm quotes you an 18-month transformation before anything ships, keep looking.
Do you work with regulated industries?
Yes — healthcare, financial services, defense, and government are where sovereign AI matters most. We've delivered HIPAA, SOC 2, FedRAMP, and IL-5 environments on Kubernetes, and the same discipline carries into AI platforms: policy-as-code, audit logging, and data boundaries designed for your auditors, not around them.
Technology Partners
Related Solutions & Guides
AI Infrastructure
GPU clusters, model serving, and AI-ready Kubernetes platforms at scale.
Self-Hosted LLM
Run open models on your own GPUs — private, air-gapped, no per-token lock-in.
GPU Kubernetes & Inference
Model serving, GPU scheduling, and inference optimization on Kubernetes.
Kubernetes Orchestration Guide
What orchestration actually does — and how to run it well in production.
An AI consulting company built on platform engineering
THNKBIG is an AI consulting company with an unusual origin: we came to AI from a decade of production Kubernetes, not from a strategy practice that added "AI" to its letterhead. That origin shapes everything about how our AI consulting services work. We treat models as workloads — with scheduling, observability, rollback, and cost discipline — because that is what separates the 66% of organizations running AI on Kubernetes from the 7% who actually ship models daily.
Our AI consulting engagements center on sovereignty: your data, your GPUs, your models, your boundary. For regulated industries — healthcare, financial services, defense — that isn't a preference, it's a constraint. We design AI platforms where open-weight models like Qwen, Kimi, MiniMax, and Gemma run entirely inside your perimeter, while the THNKBIG AI Ontology governs precisely what any external frontier model can see. The guarantees are public; the mechanics stay private. That is what enterprise AI consulting should mean: frontier capability without surrendered control.
If you're evaluating AI consulting firms, our advice is the same whether or not you hire us: demand platform delivery, demand vendor neutrality, and demand named proof. We publish ours — Delta Data and Mimrr by name, a six-week idle-GPUs-to-production engagement with the timeline attached — because an AI consultant who can't show you production systems is selling you a document. Book a discovery call and bring your hardest question; you'll be talking to the engineers who would deliver the work.
Backed by senior, US-based platform engineers — no offshoring, no junior bench. Leadership stays constant from assessment through delivery, and every engagement transfers ownership to your team. Meet the team.
Ready to make AI operational?
Whether you're planning GPU infrastructure, stabilizing Kubernetes, or moving AI workloads into production — we'll assess where you are and what it takes to get there.
US-based team · All US citizens · Continental United States only