Your pilot proved the model earns its place, and the next step puts it on infrastructure you own. Relevant Software builds AI that runs inside your perimeter: a private serving stack, and audit evidence ready before the first production request.
Relevant’s architects size the compute, storage, and network your models need at your real traffic, then specify the nodes your procurement team quotes against.
Engineers stand up the inference layer on your hardware and tune it until throughput matches the demand your application produces, with the application logic built by our team sitting on top.
Our engineers package models, container images, and dependencies for sites that operate disconnected from public networks, and build the internal registry and offline update path your operators use to keep every site current.
Engineers build the retrieval layer inside your network, index your documents where they already sit, and tune search quality on your own corpus.
Our engineers run retraining, promotion, and drift detection on the cluster you own, so each model update ships on your schedule.
Access rules, isolation, and logging land as configuration your auditors read, and our team wires the same guardrails and adversarial tests into the models you host through AI security services.
Migration engineers move a system that currently calls a vendor API onto your hardware, match its answers against the baseline your users already accept, and shift traffic in stages your operations team controls.
Relevant’s architects review each AI feature against volume, latency, and residency requirements, then recommend where it belongs. You receive the reasoning per workload with the numbers behind it.
Traffic patterns from your logs drive the capacity model, and the output names specific nodes, GPU counts, and redundancy levels. Procurement quotes from the document as written.
Engineers install the inference, retrieval, and gateway layers on your hardware and tune them against load. By the end of this step, your application talks to a model inside your network.
Eval sets captured from the current system run against the new one until quality holds, and traffic shifts in controlled stages. The comparison table travels with the release.
Access rules, isolation, and log retention land as configuration and documentation, mapped to the frameworks you report against. Each control names the artifact that satisfies it.
Your engineers run releases, capacity changes, and incident drills with Relevant alongside them, then alone. Handover completes when your team ships a model update on its own.
98% client satisfaction. Teams come back to Relevant past the first delivery because the work holds up in production.
Up to 50% faster delivery and up to 20% fewer bugs through AI-assisted development, with every release still passing senior code review.
A roadmap in 14 days. Your team holds the placement decision, the capacity model, and the sequence of work two weeks after kickoff.
GDPR, HIPAA, and ISO 27001-aligned delivery. Banking, healthcare, and energy engagements shape how the platform gets logged, isolated, and documented from the first sprint.
The placement read names the workloads worth leaving in the cloud. Recommending owned hardware for every workload would cost you money the arithmetic says to keep.
Handover completes when your engineers ship a model update alone. Runbooks, dashboards, and the deployment pipeline stay with your team, and so does the ability to use them.
Most teams land on a split rather than a wholesale move. Volume, latency, and data classification decide each feature separately: a high-traffic internal assistant over confidential documents usually earns owned GPUs, while a low-volume summarizer for public content stays where it is.
Pull last month’s API bill and the token counts per feature. Features with steady daily volume in the millions of tokens, running near constant utilization, tend to repay hardware within two to three years, and spiky, unpredictable features keep their economics in the cloud. If your billing data stops at one line, our team reconstructs the per-feature volumes from your gateway logs in the first week, and you prepare nothing beforehand.
For the work most companies run every day, open models you host yourself hold up. Answering questions from your own documents, sorting and tagging records, pulling fields out of forms, and writing summaries all reach similar quality on open models in the 30B to 70B range. On that kind of task, the quality of your search layer decides the answer more than the model does, and training the model on a few thousand of your own examples closes most of what is left.
Frontier models still lead on long chains of reasoning, large-scale code generation, and agents that plan across many tools, so some teams keep one workload with a vendor and run everything else privately. Before anyone buys hardware, Relevant’s engineers test this on your own material: we take requests your system already handled, run the candidate models against them, and show accuracy and speed beside the API you use today.
Four variables move the number:
You can pre-compute the first one. Multiply the model’s parameter count by two for the gigabytes of VRAM a 16-bit deployment needs, add roughly 20% for context and overhead, then divide by the VRAM per GPU you are considering. A 70B model lands near 160 GB, which means two 80 GB cards for a single serving node before redundancy.
Fourteen days after kickoff you hold the full roadmap with node counts and prices attached, and 99% of Relevant engagements land on the dates in it. To put your own numbers against the model, contact us and bring last month’s traffic.
Your engineers operate it, and the work sits closer to running servers than to data science. A team that already manages Kubernetes and keeps an on-call rota holds most of the skills, and a few weeks of paired work covers the new parts, model serving and GPU capacity.
Handover runs as paired work. Your engineers ship releases with Relevant beside them, sit through real incidents, and run a drill where our team pulls a GPU node so everyone sees the platform recover. They keep the runbooks, the dashboards with thresholds already set, and the test suite that checks every model update. Handover completes when your team ships a model update on its own, and Relevant stays available on a support retainer for upgrades and capacity planning.
The deployment produces the artifacts an auditor asks for as a by-product of running it: infrastructure-as-code showing where inference runs, access logs naming every identity that reached the model, prompt and response logs with retention rules applied, model version history with approval records, and network configuration showing the isolation boundary.
Relevant maps each artifact to the control it satisfies in your framework, so the evidence arrives labelled. Your compliance lead reviews the mapping before the first audit window opens.
We size the hardware, build the platform, and hand it to your engineers running.
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