Services
Every layer, one operator.
From the power plug to the prompt. Infrastructure, deployment, fine-tuning, knowledge, and AI agents — one team, one contract, no handoffs.
01 — Infrastructure
The physical layer AI runs on.
GPU servers don't plug into thin air. They need power, cooling, racks, fiber, and a room engineered to keep them running. We build that layer — from civil works to production cluster.
Assessment
Facility inspection. Power, cooling, space, network. We tell you what's ready and what it costs.
Design
Power layout, cooling architecture, rack config, network topology for your specific GPU hardware.
Procurement
NVIDIA H100/H200/B200, AMD MI300X. We source, you own.
Build
Civil works, electrical, cooling, cabling, server installation. One crew, one schedule.
Handoff
Documentation, training, remote management. Running cluster. We can walk away or stay via Managed Services.
| GPU | VRAM | Best for |
|---|---|---|
| NVIDIA H100 | 80 GB | Inference, fine-tuning |
| NVIDIA H200 | 141 GB | Large model inference |
| NVIDIA B200 | 192 GB | Frontier open-weight, training |
| AMD MI300X | 192 GB | Inference, cost efficiency |
02 — Deployment
Three ways to run your AI. All private.
No data leaks to consumer chatbots. The difference is where the hardware lives and who manages it.
On-premises
Open-weight models on your hardware, in your facility. Data never leaves. Air-gapped option available. Model families: DeepSeek, Llama, Mistral, Qwen, GLM.
Private GPU cloud
Private GPUs, your model, our infrastructure. Dedicated instances — no multi-tenant sharing. Monthly operational cost, no hardware purchase.
Via API
Frontier models — Claude, GPT, Gemini, Grok — via endpoints we configure and secure. Pay per token. Zero infrastructure.
03 — Fine-tuning
Your model, your behavior.
Shape open-weight models to your organization — tone, terminology, behavior. We use LoRA and QLoRA. One GPU. Days, not months.
- Tone — Formal for legal, conversational for support
- Terminology — Your jargon, product names, acronyms
- Output format — JSON, templates, compliance language
- Behavior — Refusal boundaries, escalation rules
- Fast — hours to days
- Cheap — single GPU, no cluster
- Portable — adapter files in megabytes
- Stackable — multiple adapters per base model
| Fine-tuning | Knowledge (RAG) | |
|---|---|---|
| What it changes | How the model behaves | What the model knows |
| Best for | Tone, format, style, refusal patterns | Facts, documents, current information |
| They work together | Fine-tune for behavior… | …RAG for knowledge. |
04 — Knowledge
AI that reads your documents.
Connect AI to your documents. Answers grounded in your data, with sources you can verify. Retrieval-Augmented Generation (RAG) with multi-agent validation — what we deploy for production.
Legal
Search contracts, regulations, case law, internal policies.
Finance
Query financial reports, forecasts, compliance documents.
Operations
Access SOPs, maintenance logs, equipment specs.
Human Resources
Search benefits policies, onboarding docs, compliance training.
Sales
Query product specs, pricing history, competitor intelligence.
Customer Service
Search knowledge base, troubleshooting, warranty terms.
05 — AI Agents
AI that acts, not just answers.
Multi-step workflows, tool use, autonomous decision-making. From customer service to IT diagnostics.
“Here's how to reset your password.”
Looks up the account, verifies identity, sends the reset link, logs the request in the ticketing system, and follows up if the user doesn't complete the reset.
| Before agents | After agents | |
|---|---|---|
| Team | 5 support engineers | 2 engineers + agents |
| Tickets/day | 200 | 300 |
| Auto-resolved | 0 | 220 (73%) |
| Avg. resolution time | 4 hours | 12 minutes (auto) / 2 hours (escalated) |
Illustrative scenario — IT support at a midsize enterprise
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