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TAIP

Solutions

Different teams.
One platform that fits.

TAIP is built around the people who actually do AI work — and the people who keep the platform running underneath them. Here's how it shows up for the most common teams we work with.

01 / 04

AI research labs

From notebook to training run, without leaving the platform

Give researchers GPU-ready dev environments, curated training with live progress, and a clean handoff from experiments to production — all under the same identity and quota model.

Outcomes

  • Researchers go from idea to a running fine-tune in hours, not weeks
  • Every job runs under live quota, with streamed logs, progress, TensorBoard, and an MLflow run record
  • An interrupted run resumes from its checkpoint instead of spending the GPU-hours twice
  • Existing Slurm scripts run on the same cluster, under the same identity and quota
  • Findings move toward production on the same platform, not a rewrite

02 / 04

AI product teams

Ship LLM-powered products on infrastructure you control

Stand up OpenAI- and Anthropic-compatible endpoints, host fine-tuned models, and meter every token on dedicated capacity — with the cost, latency, and privacy your product needs.

Outcomes

  • Drop-in OpenAI compatibility means no application rewrites
  • Cost and latency you can plan against, not hope for — with prefix-cache-aware routing for long-context agent traffic
  • Answers grounded in your own documents, cited to the page, instead of plausible guesses
  • Sensitive data stays in your environment, end to end

03 / 04

Platform & infrastructure teams

Run a serious internal AI platform without building one from scratch

TAIP gives platform teams the multi-tenant control plane, policy, and observability that turn a GPU cluster into a real product for the rest of the company.

Outcomes

  • Multi-team quotas, policy, and audit out of the box
  • One token authority, one SSH gateway, one CLI — not a credential store per product
  • Storage accounted for like compute: backends, capacity projection, per-tenant GiB-hours
  • Chargeback on a real ledger: every reserved GPU-hour billed to the account that used it, with budgets that stop new work, never running work
  • Self-service for users with guardrails for operators
  • One platform across notebooks, training, registry, and inference

04 / 04

Regulated & on-prem deployments

AI that meets your compliance bar — in your environment

TAIP installs into your data center, your VPC, or air-gapped environments. Bring your IdP, your network policy, and your hardware — TAIP fits in.

Outcomes

  • On-prem, VPC, and air-gapped installs supported by design
  • Bring-your-own identity and policy from day one — with MFA enforceable cluster-wide
  • Vulnerability policy set once for the registry, exempted per namespace only by an admin
  • Auditable from cluster up: every action, every artifact