Turn a useful AI idea into a working part of your business. We help you choose the right use case, select or build the solution, and put the infrastructure and controls behind it — from a private AI server to a GPU cluster.
Already running AI or HPC and not getting the performance you expected? We investigate the fabric, storage and scheduler, with measurements and a clear next step.
Find a useful first application, compare buying with building, and understand the costs and data requirements before committing to a deployment.
Assessment $9,500 · a defined, standalone engagement Build infrastructurePrivate model deployments, GPU clusters, fabric, storage and schedulers. Specified, built, tested and handed over to the people who will operate them.
Single-system engineering $45,000 · hardware separate Improve performanceA focused investigation of an existing system, with a written report and recommended actions. Book after a brief qualification call.
Diagnostics from $7,000 · no paid assessment prerequisiteDocument intake, internal knowledge search, repetitive handoffs and specialist workflows are places to start. We assess the actual work, data and economics, then recommend a manageable first implementation.
Map the workflow, establish a baseline and compare the options. You get a written assessment, cost model and implementation sequence.
Select a platform or build a private deployment. Agree the integrations, human review steps and acceptance tests before implementation is quoted.
Document how to run it, who can access it and how to evaluate changes. Policy, governance and ongoing support are separately scoped services.
Architecture and readiness assessment — $9,500. Preparation, a full-day workshop and a written plan. A standalone engagement for new builds and complex adoption decisions. Bounded diagnostics can be commissioned directly.
Most firms selling AI advice will write you a policy and a roadmap and stop at the point where somebody has to make hardware work. We start there. One private inference box in your rack, or ten thousand GPUs with a fabric that has to actually carry the traffic. Same discipline either way. The interesting question at every size is which layer fails first.
One customer-owned server with model selection, model training or fine-tuning
using your data within an agreed scope. Choose a suitable Llama, Qwen or Mistral variant,
with a model runtime, access controls,
performance validation, monitoring, documentation and administrator handover.
$45,000 engineering fee Hardware, software licenses,
travel and ongoing support are separate. Hardware is bought in your name, without markup.
Scope and ordering →
From a few servers to large GPU installations. We design, build and tune the
computing, network, storage and software for your AI or high-performance computing workloads.
Quoted per build for your system and workloads
Explore cluster services →
Multi-rack, multi-tenant, with the operational discipline that keeps it running
once the launch is over: rolling maintenance with no filesystem interruption, health
checks that page before a user notices, and an image pipeline a reboot survives.
Quoted per build including the run-book and the
training to operate it without us
Start with your AI, simulation, research, or data workloads. Size CPU and GPU nodes, memory, storage, and network capacity. Plan topology, growth, rack space, power, and cooling, with hardware selection and procurement assistance.
Install and configure compute, GPU, login, and management nodes. Set up operating system images, provisioning, drivers, firmware, user access, and repeatable configuration. Integrate with your existing identity and network services.
Build and tune InfiniBand, RoCE, or Ethernet networks. Check cabling, link speeds, routing, congestion, and RDMA. Include NVIDIA UFM configuration and tuning where used, with settings matched to the hardware and workloads.
Set up parallel storage such as GPFS / IBM Storage Scale, Lustre, Weka, or VAST. Plan shared and scratch storage, client connections, permissions, data movement, and agreed backup or recovery requirements. Test throughput, latency, and metadata performance.
Configure Slurm, PBS Pro, or LSF for queues, priorities, fair share, GPU allocation, accounting, and license-aware scheduling. Set up containers or Kubernetes where the workload calls for them, and test how teams share the system.
Select models such as Llama, Qwen, Mistral or DeepSeek, with variants sized for your cluster. Set up agreed pretraining, continued pretraining, fine-tuning, distributed training and inference workflows. Include language, vision, embedding or multimodal models as needed. Define data preparation, licenses, integrations and model evaluation in the project scope.
Install agreed compilers, MPI libraries, math libraries, environment modules, and scientific or engineering applications. Help move workloads to the cluster and tune representative jobs for the available CPUs, GPUs, memory, and network.
Measure the complete system with agreed application and infrastructure tests, including representative AI model pretraining runs. Track tokens or samples per second, training step time, GPU memory, checkpoint performance and multi-node scaling. Use NCCL collectives, MPI/RDMA tests and IOR/mdtest where appropriate. Check GPUDirect RDMA, unintended TCP fallback and bottlenecks. Deliver reproducible results against agreed acceptance targets.
Set up health monitoring, dashboards, alerts, logs, and agreed security controls. Plan maintenance, upgrades, recovery, and high availability where required. Provide operating documentation, administrator training, and handover. Ongoing support can be included in a separate agreement.
Every build has a written scope covering system size, workloads, deliverables, licenses, hardware, testing, schedule, and acceptance criteria. The final design determines which services above apply. Hardware, software licenses, facilities work, travel, and ongoing support are itemized as applicable; they are not automatically included in an engineering quote.
Request a cluster quote →Build AI applications, connect your systems, and automate data, training, or scientific workflows. Includes agreed development, testing, deployment and handover. Custom quote.
Build custom AI applications, internal tools, APIs, and connections to your existing systems. Define users, workflows, access controls, and acceptance criteria before development starts.
Develop data ingestion, validation, transformation and retrieval workflows. Build agreed pretraining, fine-tuning, evaluation and inference pipelines for models such as Llama or Qwen, plus embedding and vision models where useful. Include dataset and model versioning, checkpoint handling and reproducible runs.
Automate simulation, analysis, batch processing, and multi-step research workflows. Integrate with cluster schedulers, containers, shared storage, and agreed CPU or GPU applications.
Include agreed automated tests, error handling, logging, monitoring, and deployment automation. Provide source code, documentation, and team handover for the custom work, with ownership and third-party license terms defined in the agreement.
Quoted by scope, integrations, data requirements, delivery milestones, and acceptance criteria. Software development is a separate service from infrastructure installation. Hosting, compute, third-party licenses, and ongoing maintenance are itemized separately where applicable.
Which layer fails first changes with scale, and that is the whole job. At a few nodes it is the scheduler. At a few hundred it is the fabric — routing, congestion, and whether RDMA is carrying the traffic or has silently fallen back to TCP. Past that it is storage bandwidth, then power and cooling. A vendor benchmark will not tell you which one you have, partly because the storage array's own write buffer can defeat the test you were told to run.
Fixed-price help to set up your scheduler or parallel filesystem, or find out why your infrastructure is slow. Each one includes measurements, written findings and a readout. No paid architecture assessment is required.
InfiniBand (QDR through XDR), RoCE v1/v2, and Ethernet TCP/IP.
We check links, routing, congestion and RDMA, plus NCCL tests for GPU-to-GPU
and multi-node performance. Includes NVIDIA UFM review and agreed tuning where used.
$9,000
Book the fabric diagnostic →
Setup and configuration of GPFS, Lustre, Weka or VAST, including server connections,
performance testing, agreed tuning and administrator handover.
$13,000
Book filesystem setup and review →
Test the system with real workloads, including AI model pretraining, training and
multi-node scaling. Get measured results against agreed targets.
$9,500
Book acceptance testing →
Benchmark pretraining from scratch or continued pretraining on agreed datasets, using selected Llama, Qwen, Mistral or DeepSeek model architectures. Add BERT-style language encoders, vision transformers or diffusion-model training when relevant. Model variants, sizes and dense or mixture-of-experts configurations are chosen to fit your hardware, licenses and test goals.
Measure training throughput in tokens or samples per second, step time, GPU utilization, memory use, and scaling across GPUs and nodes. Check data loading, checkpoint save and restore, communication overhead, and loss or agreed quality metrics. Fine-tuning and inference tests can be included for workloads that need them.
Agree on model configuration, dataset, precision, batch size, sequence length, run duration and acceptance targets before testing. Report the settings alongside results so comparisons can be reproduced. The fixed-price service covers the agreed test window; full model pretraining to completion or production model development is scoped separately.
Scheduler setup and workload review — $7,000. Setup and configuration of one Slurm, PBS Pro or LSF environment, including queues, resource sharing, GPU scheduling, workload testing and administrator handover.
A clear boundary before work starts. A brief qualification call confirms which diagnostic fits. Written acceptance identifies the system size, workloads, setup tasks where included, access, testing window and delivery date. Hardware, licenses, travel, additional systems, work outside the agreed scope and ongoing support are separate.
Two jobs sit either side of the build, priced and delivered separately. Putting it to work — which work in your operation is genuinely worth automating, judged against how the business runs rather than what is fashionable, and whether to buy, build or neither. The policy ($18,000) — an inventory of what is already in use and a policy written to permit rather than forbid, because a ban is ignored by the second week. The governance ($28,000) — the machinery that keeps that true once everyone is busy, mapped to the NIST AI Risk Management Framework and ISO/IEC 42001. All three in full →
The deep infrastructure practice has its own site — Wirewalk Advisory — where the cluster engineering, the fabric work and the MPI and storage writing live in full. It is one team and one set of hands. This page exists because the answer to a business question is sometimes a machine, and the person who tells you that should be the person who can build it.
Twenty years of this, at real scale. The same discipline has run production clusters in a national laboratory, a health system and semiconductor manufacturing. Parallel filesystems that cannot drop. NDR and HDR InfiniBand fabrics. Schedulers carrying work that costs real money when it stalls. GPU estates where a bad image is a fleet-wide outage. This is not a practice that read about it.
Your sector already has a view. A law firm has bar guidance on client confidences. A defense supplier has controlled technical data that must not reach a public model at all. A practice has patient records and a business associate agreement written before any of this existed. What makes general advice usable is knowing which rules are yours, which is what the industry pages are for.
Every operation loses money in three ways at once, and most reviews are commissioned to look at one of them. What each one is called and where it hides changes by sector, which is why there is a page for your industry rather than one page asking you to translate.
What the review finds is what the review finds. Nothing above is a result we are claiming; it is where we look. We write down what we expect to find before fieldwork, so afterwards you can check us against it rather than take our word for what was always going to be in there.
Commissioning a proper look takes a little nerve, because whatever it turns up concerns people you hired and trusted. So: findings go to you and to nobody else, anything uncomfortable is a spoken conversation before it is written down anywhere, and we read systems rather than people. Almost none of what we find is anyone’s fault — concentrated loss is what busy, well-run operations produce. Exactly what happens to a finding, and the terms we work to.
Loss concentrates; it does not distribute. One contract, one queue, one account nobody reconciles. Thirty sampled transactions are the wrong instrument for finding something that only happened once — and a finance-only review never opens the contract file at all.
That coverage is what you are buying. The engagement that delivers it end to end across a single business unit is The Operating Review, one business unit, at a fixed fee. Multi-site and multi-entity coverage is quoted per engagement, because the number of units and systems drives everything.
What a review has to look at is set by your sector, not by us. A law firm has trust account reconciliation and a bar audit; a defense supplier has an SPRS score and an ITAR boundary. A page that says “vendor contracts and write-offs” makes you translate — one that says “realization, WIP aging and origination credit” does not.
Where the product is time, and three different numbers describe the same hour.
Law firms ·
Accountancy firms ·
Professional services ·
Agencies and media ·
Staffing and recruitment ·
Engineering and architecture
Where regulated cost is an operating line, and the operations underneath it are rarely priced.
Financial services ·
Insurance ·
Real estate finance
Where margin is calculated from standards, and the standards drift.
Manufacturing ·
Aerospace and defense ·
Warehousing and logistics ·
Transport and fleet ·
Construction and contracting
Where the asset is the business, and what is contracted is not what is collected.
Real estate ·
Energy and utilities
Where volume hides the unit, and the unit is where the loss is.
Retail and e-commerce ·
Hospitality and food service ·
Technology and SaaS
Where the funder, the regulator or the statute sets the operating model, and the cost of complying with it is nobody's line item.
Healthcare ·
Human services and behavioral health ·
Government contracting ·
Public sector ·
Education and research ·
Nonprofit and grant-funded
Not listed is not out of scope. The operating spine underneath is the same wherever you sit; what a sector adds is its own regulator, its own filings and its own words for the same losses. Tell us what you do and we will say what we would add, or start at the index.
A review of the whole operation reaches the parts of it that are regulated. Payroll, contracts, the general ledger, customer records and in some sectors patient or student records all sit inside somebody’s regime, and the review does not get an exemption from it. PHI under a signed BAA and the HIPAA Security Rule. CUI to NIST SP 800-171, with the SSP and POA&M that go with it. EAR and ITAR where they apply — including the part that catches research organizations, where a document request is a form of access. GDPR, FERPA, and PCI DSS handled by keeping cardholder data out of scope entirely.
Capability and commitment, not certification. There is no such thing as being “HIPAA certified”, and a firm that says otherwise has told you something useful about the rest of its claims. What we may hold, where, who may see it and how it is destroyed is agreed in writing before the first document moves — the whole of it is written down here, and a security questionnaire can be completed before you commission anything.
Six areas, one pass, whatever sector you are in. Every material finding sized in annual value, and ranked against how hard it is to act on. Including the ones we tell you not to fix, and the things already working that are worth protecting. Phase one stands alone: the report is yours, and phase two is not discussed until you have read it. The five weeks, week by week.
There is a full specimen report on the site — an invented diagnostic for a dermatology group that does not exist, written to the standard a real one is written to. Same structure, same vocabulary, same evidence attached to each finding. It is the fastest way to know whether this is worth $12,000 to you. Read the specimen report →
Phase two is never sold before phase one is delivered. When the findings are agreed and you want help landing them, implementation support is quoted against those findings. Not before them. Never as a condition of receiving them. The work is whatever the report actually found: scaling what already works, rebuilding the workflows that do not, renegotiating vendor terms, or building software where buying it would cost more.
Not ready to commission the whole thing? The operating diagnostic is the bounded version of exactly this at $12,000, and the operating sounding is $3,500: one question, three registers, five business days, no purchase order, credited in full against a diagnostic. If we look and there is nothing there, it says so.
A report nobody acts on is an expensive document. The work that lands is the work your people already wanted: the re-keying, the chasing, the third approval on a $200 invoice. Fix that and you get clean data and willing staff. Lead with the software and you get neither.
The strong performers and the clean processes get named in the report, because the fastest way to break one is to reorganize around the parts that are broken.
Re-keying, chasing paper, access requests, the third approval on a small invoice. Your people will tell you where these are in the first ten minutes, and nobody has ever asked them.
Incentives that reward clean finance and clean handoffs, rather than volume that somebody downstream has to correct.
Three dated predictions, written down before we open a single file — and one of them is always something we expect to find already working. Afterwards you mark us against it.
It shows the diagnosis came from experience, not hindsight.
The miss is documented too. The method stays testable.
The work can be checked after the engagement.
You can test the method before you buy the review. The predictions are written before fieldwork on every engagement, including the cheapest one — so the smallest thing here is also the one that proves the expensive thing works.
Pharmaceutical manufacturing. A national laboratory. Semiconductor equipment. A cancer hospital. An aerospace supply chain. Different industries, one problem underneath: operations, technology and risk are a single system, and almost nobody is paid to look at all three at once.
Where this experience was earned. Twenty years of operations, infrastructure and risk work inside these organizations — not a claim that each is a client of this practice, and nothing here reports an engagement or a measured result.
Tell us what you want AI to do, what you already run and when you need it working.
A brief conversation to establish the problem and the right engagement.
Systems, deliverables, timing and fee are written down before work starts.
A decision, an investigation or a working system, tested against the agreed scope.
If your procurement office moves before your calendar does, start there. Raise a purchase order against the engagement and we invoice on net 30 — the route most institutions are actually set up for, whether that is a health system, a university, a manufacturer, a city or county government or a federal prime. Prices, deposits and the PO form are on the ordering page.
Tell us about the workflow you want to improve, the system you need to build or the performance problem you need to solve. We will help identify the next practical step and agree the scope before you commit.
Considering an operating review? Download the review overview to circulate internally.
Prefer to call? 516-269-1517
Already know what you want, or need a quotation your procurement office can raise a PO against? Commission it directly — prices, deposits and the purchase-order form are all there.
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