Services
AI that reaches production, on infrastructure you control.
Strategy, GPU infrastructure, generative AI, MLOps and governance, delivered by the people who also build the data centers and networks underneath.
AI
What this covers.
Most AI programs stall in the gap between a convincing demo and a system a risk committee will approve. We close that gap from both ends: the accelerators, networks and data platforms underneath, and the retrieval, evaluation, guardrails and monitoring on top. One team owns the whole path, so the model, the data and the hardware are designed together rather than negotiated between vendors.
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AI strategy and readiness
Use cases chosen on value, data and risk before anything is built. -
Generative AI and agents
Retrieval, agents and guardrails a risk team will sign off. -
MLOps and model operations
Training, serving and monitoring models as a reliable service. -
AI governance and security
AI inventory, risk classification and controls for the AI Act. - Data Centers and Infrastructure
AI data centers and GPU clusters
GPU clusters planned, racked, networked and validated for AI.
Start from the problem
Which of these sounds like your week?
Choose a situation and see the service that starts there, with what it includes.
- The board wants an AI strategy and the organization has a list of ideas rather than a plan.
- Several pilots are running and none has a path to production.
- An AI prototype demos well, but nobody will sign off on putting it in front of customers.
- Staff paste company data into public chat tools because there is no approved alternative.
- Models are trained on one person's machine and nobody can reproduce last quarter's version.
- GPUs are idle half the time and oversubscribed the other half.
- Nobody can produce a list of the AI systems in use across the organization.
- A customer questionnaire asks how you govern AI, and the honest answer is that you do not yet.
Choose the situation that sounds most like yours.
Where that starts
AI strategy and readinessUse cases chosen on value, data and risk before anything is built.
- Use case discovery
- Value and feasibility scoring
- Data readiness
Where that starts
Generative AI and agentsRetrieval, agents and guardrails a risk team will sign off.
- Retrieval augmented generation
- Agents and tool use
- Model selection and hosting
Where that starts
MLOps and model operationsTraining, serving and monitoring models as a reliable service.
- Training pipelines
- Model registry
- GPU scheduling
Where that starts
AI governance and securityAI inventory, risk classification and controls for the AI Act.
- AI inventory
- Risk classification
- Management system
A common path
From a list of ideas to AI in production.
- AI strategy and readiness Choose the use cases worth building, and confirm the data they need exists and may be used. Open the service
- Generative AI and agents Build the first one against an evaluation set agreed with the people who will approve it. Open the service
- AI governance and security Put the inventory, the risk classification and the security testing around it before a second system follows. Open the service
- MLOps and model operations As models multiply, run training, serving and monitoring as one platform rather than one project each. Open the service
Each step is bought on its own, and you can start at any of them.
Reference architecture
AI that answers from your data, on infrastructure you control.
Your documents and systems feed a private retrieval index and model, served on GPUs you control, behind guardrails and an evaluation loop that decide what reaches users.
A question is answered from your own documents, checked, cited and logged.
An agent acts through scoped tools, and anything consequential waits for a person.
New knowledge is indexed as it changes, and a model is replaced only when it measurably does better.
Outcomes
What changes.
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Production, not pilots
Use cases are chosen against measurable tasks, evaluated before launch and monitored after it, so success is a number rather than an impression.
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Infrastructure sized to the work
Training, fine-tuning and inference capacity is planned from the workload, on premises, in the cloud or both, so accelerators are neither idle nor oversubscribed.
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Governance a regulator can read
Model inventory, risk classification, data lineage and human oversight are documented as the system is built, against the AI Act and ISO/IEC 42001.
From the GPU to the governance, one team answers for the model.
Commercials
How engagements run.
Three commercial shapes, and none of them is an hourly meter.
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It starts with a paid assessment
Anything carrying real technical risk begins with a fixed-fee technical assessment, one to three days, producing a written findings document and a scoped fixed price for the build.
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Fixed price for defined scope
Once the scope is known from your systems rather than from a conversation, the build is priced as a fixed figure, with the assumptions it depends on written down alongside it.
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Monthly retainer for operations
Ongoing operational ownership runs on a monthly retainer against an agreed service level, so the cost of running a system is a number you can plan against.
Where it is bought
AI, by sector and region.
The sectors that ask for it most, and who you would contract with in each region.
Sectors that ask for it
Questions
Questions we get asked.
Do we need our own GPUs?
Often not at first. Hosted models and cloud accelerators answer most early workloads. Owning capacity starts to pay when utilization is high and predictable, when data cannot leave your boundary, or when cloud capacity is the constraint. We model the comparison before anything is bought.
Which models do you work with?
Commercial APIs from Anthropic, OpenAI and Google, and open-weight models such as Llama, Mistral and Qwen served on your own hardware. The model sits behind one interface so the choice stays reversible.
How do you stop an AI system saying something it should not?
With layers rather than a single filter: grounding in approved sources, limits on which tools the model may call, output validation, adversarial testing before release and monitoring after it. Each layer is documented so your risk team can review it.
Can you help us comply with the EU AI Act?
Yes. We inventory the AI systems in use, classify them by risk, and produce the documentation, logging and human oversight the Act asks of providers and deployers, mapped to ISO/IEC 42001 where you want a management system around it.
Ask about your situation
Answered from this site, with its sources.
The stack
Where one layer meets the next, one team owns the seam.
AI is the highlighted layer. Integration connects it to the other six, so work here is planned with the layers around it in view.
CONTACT
Take one AI use case all the way to production.
Bring the use case with the clearest value. We will assess the data, the infrastructure and the evaluation plan before anything is bought or built.
- Senior-led
- Accountable
- Direct
- Fixed-price options
- Compliance-ready
- 24/7 capable
Prefer to talk it through? Call +92 (333) 32 11011
What happens next
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You pick the time
We schedule the call at your convenience, not around our pipeline.
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Thirty minutes, with an engineer
A direct answer on what we would do and whether we are the right fit at all.
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A written assessment
A technical assessment and proposal, and the document is yours either way.