We don’t just advise
on AI. We build it —
and run the compute.
Production-grade AI adoption for compute- and data-heavy organizations. The market is drowning in slideware. We are the partner that ships to production and runs the infrastructure underneath it — on-prem, in your cloud, or on ours.
Cost governance is a first-class citizen — not an afterthought
Almost everyone is piloting AI. Almost no one is shipping it.
The failure isn’t the models — it’s the gap between a demo and a governed, running system. Teams that stall are the ones that built alone or hired an advisory that stopped at the slide deck. The teams that win partnered with people who could take it all the way to production, and keep the compute bill from exploding once it got there.
That last part is where most AI shops go quiet. It’s where we start.
of enterprise GenAI pilots show no measurable P&L return.
MIT NANDA · State of AI in Business 2025
buyers who partner succeed about twice as often as teams building alone.
MIT NANDA · GenAI Divide
monthly AI-compute bills once inference hits production — the cost enterprises fear most.
Deloitte · Tech & AI Outlook 2026
Most AI advisories hand you a strategy deck and a bill.
We hand you a running system with an eval gate — and we keep the compute humming underneath it.
Six things a generic AI consultant simply doesn’t have.
Not a longer feature list — a different category. Everything below comes from being an HPC company first, and an AI partner second.
We own the compute layer.
Most consultants stop at Bedrock and SageMaker app-glue. We go all the way down — AWS ParallelCluster, Slurm, GPU scheduling, fabric and storage — and, critically, to AI-compute cost, the line item enterprises fear most. When your inference bill is the thing that decides whether AI survives contact with finance, you want the partner who tunes the cluster, not just the prompt.
We ship it — and we run it. A closed loop.
Assess → build → run, on our own live platform. Not a deck; a running system behind an eval gate, with rollback if a check fails. We already do this in anger: we took a research group from a bare HPC cluster to a scientific simulation pipeline in production — the run exited clean, EXIT 0, on hardware we manage.
| METRIC | VALUE | THRESHOLD | GATE |
|---|---|---|---|
| task accuracy | 0.91 | ≥ 0.85 | pass |
| hallucination | 2.1% | ≤ 3% | pass |
| p95 latency | 840ms | ≤ 1.2s | pass |
| cost / 1k req | $0.94 | ≤ $1.20 | pass |
Hybrid on-prem + cloud, with data sovereignty by default.
We run managed on-prem HPC and cloud (PeakOps Eleven) — so your data can stay in your own data center or your own cloud account, never ours. Air-gapped where it must be. KVKK and EU AI Act awareness is built into how we scope, not bolted on at the end.
AWS Partner Network — credibility where it counts.
We’re an AWS Partner Network member — Registered Tier, and advancing — Technical and Sales Accredited, and an AWS Marketplace Seller. That means real co-sell reach and procurement paths that shorten the road from pilot to signed contract. AWS-native where AWS is the right answer, but never locked to the app layer, because we own the compute beneath it too.
Cost governance as a first-class capability.
Budgets, alerts and auto-pause — the same guardrails that run inside PeakOps Eleven today. We make AI spend predictable and defensible: a hard cap, an alert before you hit it, and an automatic stop so a runaway agent can’t quietly burn a quarter’s budget overnight. This is our signature.
A real scientific & engineering compute pedigree.
We come from HPC — molecular simulation, CFD, genomics, materials screening — not from building another chatbot. That means we’re fluent in the workloads where AI actually moves the needle for compute-heavy organizations, and we know what “correct” looks like when a wrong answer is expensive.
A ladder, not a leap of faith.
Start small and fixed-fee. Climb only when the evidence says so. Most engagements begin with a two-week Readiness Sprint — a low-risk way to find out whether, and where, AI is worth it for you.
The front door. A focused audit that tells you exactly where AI pays off — and what the compute will actually cost.
- Compute + data audit
- Ranked use-case shortlist
- Reference architecture (ParallelCluster / Slurm / GPU)
- AI-compute cost model
- KVKK / EU AI Act risk flags
- 90-day roadmap
One use case, built for real and measured against a gate — so the go/no-go is evidence, not a hunch.
- A working, evaluated pilot
- Eval harness + thresholds
- Cost + latency benchmarks
- Go / no-go memo
The winning pilot, hardened, integrated and deployed — then operated on PeakOps so it keeps working.
- Deployed, integrated system
- Run on PeakOps (on-prem / cloud)
- Monitoring + eval in production
- Cost governance: budgets, alerts, auto-pause
Senior ownership on tap: someone who holds the roadmap, the governance and the spend so you don’t have to hire for it full-time.
- Roadmap ownership
- Cost + risk governance
- Model & vendor strategy
- Named engineer on an SLA
Pricing is scoped per engagement — the Readiness Sprint is a fixed fee, the pilot is milestone-priced, and production + retainer are quoted to your workload. Research & academic pricing available. Talk to us and we’ll scope it honestly.
Assess. Prototype. Build. Run.
A closed loop with a gate in the middle — the discipline that separates the 5% who ship from the 95% who pilot forever.
We map compute, data and use cases.
Two weeks to understand your workloads, data estate and constraints — and to rank where AI actually earns its keep. You leave with a reference architecture, a cost model and a 90-day roadmap.
We build one thing and gate it.
A working pilot on real data, measured against explicit thresholds — accuracy, latency, hallucination, cost per request. The gate decides whether it graduates. No gate, no promotion.
We harden it for production.
Integration, security review, KVKK / EU AI Act alignment, and the eval harness wired into CI. Deployed on-prem, in your cloud account, or on PeakOps — your data stays where you want it.
We operate and govern it.
Live monitoring, in-production evals to catch drift, and cost governance with budgets, alerts and auto-pause. Named engineers on an SLA — the same way we run HPC clusters today.
AI is only as good as the compute under it.
When a training run stalls or an inference fleet saturates, the answer isn’t a better prompt — it’s the scheduler, the fabric and the GPU topology. This is home turf for us. The same console our engineers use to run HPC clusters is where your AI workloads live too.
| NODE | STATE | LOAD |
|---|---|---|
| gpu-a100-01 | alloc | 94% |
| gpu-a100-02 | alloc | 88% |
| cpu-hi-07 | idle | 6% |
| cpu-hi-08 | mix | 51% |
| cpu-hi-09 | drain | 0% |
Built for organizations where compute and data are the hard part.
Research universities
Labs and national research groups that want AI on top of real compute — LLM-assisted analysis, surrogate models, agentic pipelines — without hiring an ML platform team per department.
Pharma & materials
Discovery teams pairing simulation with ML — property prediction, screening, generative design. We speak both the science and the scheduler.
Engineering & CAE
CFD, FEA and design teams using AI surrogates and copilots to cut solver time and turn simulation output into decisions faster.
GPU-heavy startups
Teams whose product IS the model, watching their GPU bill outrun their runway. We make training and inference cost predictable and the infra someone else’s problem.
Regulated enterprises
Finance, public sector and IP-sensitive R&D that can’t send data to a third party. On-prem or your own cloud, air-gapped where required, KVKK and EU AI Act aware by design.
PeakOps vs a generic AI consultant.
The difference isn’t the pitch. It’s what still exists — and still runs — six months after the engagement ends.
AI adoption sits on top of the same HPC we already install, manage and support — on-prem and in the cloud.
The questions a serious buyer asks.
Straight answers on compute, cost, sovereignty and proof — no hand-waving, no hype.
App-layer GenAI shops are excellent at wiring Bedrock, agents and RAG together — and we do that too. The difference is the floor beneath it. We own the compute: ParallelCluster, Slurm, GPU scheduling and, above all, AI-compute cost. When your inference bill is what determines whether AI survives the next budget review, the partner who can tune the cluster and cap the spend is worth more than the one who can only tune the prompt.
Yes — that’s a first-class mode, not an exception. We run managed on-prem HPC and can build and operate AI entirely inside your data center or your own cloud account, air-gapped where required. Your data never touches our infrastructure. We scope every engagement with KVKK and EU AI Act obligations in mind from day one.
An explicit set of thresholds a pilot must clear before it’s allowed into production — task accuracy, hallucination rate, p95 latency, and cost per request, measured on your data. If the numbers don’t clear the bar, it doesn’t ship; you get an honest go/no-go memo instead of a system that quietly underperforms. The same evals then run in production to catch drift.
The same way PeakOps Eleven does today: hard budgets, alerts before you hit them, and automatic pause when a cap is reached — so a runaway agent or a forgotten batch job can’t burn a quarter’s budget overnight. We model the cost before we build, then govern it in production. Predictable spend is treated as a feature, not a report you read after the fact.
No. The front door is a fixed-fee, two-week AI/HPC Readiness Sprint. You get a ranked use-case shortlist, a reference architecture, a cost model and a 90-day roadmap — enough to decide whether to go further, with no obligation to. Most clients start there precisely because it de-risks everything after it.
We run production HPC today, and we’ve taken a research group from a bare cluster to a scientific simulation pipeline in production — provisioned, tuned, and verified end-to-end with a clean EXIT 0 on hardware we manage. AI adoption is that same discipline applied one layer up: build the thing, gate it, run it, and own the outcome.
Let’s talk about
your cluster.
Standing up on-prem HPC, wrangling Slurm, or bursting to AWS? Tell us what you’re running. A PeakOps engineer will get back to you — not a bot.
hello@peakops.co