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Whitesoft

Delivery · AI Architecture & Platforms

Foundations that let the second AI project cost less than the first.

Without shared foundations, every AI initiative rebuilds ingestion, identity, evaluation and monitoring from scratch. We design platform layers that are reusable, observable and cost-controlled, drawing on years of cloud architecture work across the three major providers.

What you get

  • 01

    A reference architecture your teams can build against

  • 02

    Model access, gateway and cost-management patterns

  • 03

    Clear separation between experimentation and production environments

How we do it

The same discipline every time: understand the business, quantify, test, then commit.

Deliverables

  • Reference architecture and decision records
  • Platform build or hardening
  • Cost model and guardrails
  • Environment and deployment automation
  1. Target architecture

    Ingestion, storage, retrieval, model access, orchestration, evaluation and observability laid out as reusable layers with clear ownership.

  2. Model gateway and cost control

    A single controlled path to multiple model providers with routing, caching, quotas, logging and budget alerts so cost never surprises you.

  3. Security and identity by design

    Least-privilege access to data and tools, tenant isolation, secret management and audit trails built into the platform rather than each application.

  4. Landing zones and environments

    Cloud environments that let teams experiment safely while production stays governed, with infrastructure as code throughout.

Questions we are often asked

Do we have to move clouds to do AI well?

Almost never. All three major clouds have credible AI services, and many organisations are better served by using what they already run. We recommend based on your constraints, not our preferences.

Review your AI architecture.

A short conversation is enough to tell whether this is the right starting point for you.