Softobiz

AI PLATFORM DESIGN AND IMPLEMENTATION

AI platform design and implementation

We design and implement the underlying AI platform, from data access and model services to deployment, security and observability.

  • Six connected implementation areas with clear ownership and contracts
  • Built cloud-native on your platform, reusing sound tooling
  • Governance wired in from day one, not bolted on later
WHY A PLATFORM, NOT ANOTHER PROJECT

The moment the same plumbing gets rebuilt twice, the answer is architecture.

Use cases solve one problem. A platform makes the next ten cheaper.

The broader Scaled GenAI and AI Platforms service uses a seven-layer reference architecture. This implementation view groups the build into six connected areas, so teams can assign ownership and deliver the platform incrementally. The foundation supports MLOps for predictive models and LLMOps for language models.

AREA 01

Applications

Copilots, assistants, agents, and embedded features that sit on top of the platform. Product teams move fast without owning infrastructure.

AREA 02

Orchestration and retrieval

RAG, prompt management, routing, and tool calls that connect models to context. One retrieval path, evaluated once, reused everywhere.

AREA 03

Model serving

Foundation and fine-tuned models behind a registry and gateway. Swap or add models without touching the applications above.

AREA 04

Data and vector

Embeddings, indexes, features, and access control for governed data. Governed data access, not per-app copies.

AREA 05

Observability and eval

Traces, quality, cost, drift, and guardrails across the whole stack. You can prove a change is safe before it ships.

AREA 06

Governance

Access, audit, model inventory, and policy applied as a platform property. Compliance is built in, not a per-team scramble.

The discipline is in the seams. A clean contract between orchestration and serving reduces the effort and risk involved in changing a model provider.

Every next use case should start higher up the stack, not back at the plumbing.

WHAT IS INCLUDED

A foundation your teams can build on without us in the loop.

  • Target architecture and layer design mapped to your security posture, data residency, and cloud.
  • Reference implementation of the shared services: retrieval, serving gateway, evaluation, and observability.
  • Golden-path templates designed to shorten the setup for a new use case.
  • CI/CD and environments with promotion gates across dev, staging, and production.
  • Governance wired in: access controls, audit logging, and a model inventory from day one.
  • Enablement and runbooks so your teams can build on the platform independently.
OUR APPROACH

Five steps, and the first one is proving the platform case.

STEP 01

Frame the platform case

Confirm real, repeated demand before building shared infrastructure.

STEP 02

Design the layers

Architecture, contracts between layers, and the build-versus-reuse boundaries.

STEP 03

Build the thin slice

Stand up one end-to-end path through every layer with a live use case on it.

STEP 04

Harden and generalize

Templates, guardrails, cost observability, and governance across the stack.

STEP 05

Transfer or operate

Hand over to your team, or run it as an embedded pod.

TOOLS AND TECHNOLOGIES

Tool-pragmatic, built on what you already run well.

A representative stack across the architecture. We reuse sound existing tooling rather than replacing it wholesale.

Orchestration and retrievalLangChain / LangGraph, hybrid search, Cohere Rerank and cross-encoder rerankers.
Model servingManaged and open models, model gateway, LoRA / QLoRA adapters, BentoML / KServe.
Vector and datapgvector, Pinecone, Weaviate, Qdrant, Milvus; Feast; Databricks.
MLOps / LLMOpsMLflow, SageMaker, Vertex AI, Kubeflow.
Observability and evalLangSmith, Langfuse, Arize Phoenix; NeMo Guardrails.
GovernanceUnity Catalog-style catalogs, model inventory, policy-as-code.
HOW WE ENGAGE

Three ways in, matched to where you are.

Platform buildYou need the foundation stood up fast, to a defined spec.
Embedded platform podYou want a dedicated team owning and evolving it long-term.
Assess and optimiseYou have a platform that is slow, costly, or hard to govern and want it re-architected.
RELATED SERVICES

The tracks that run on the platform.

PREDICTIVE

MLOps

Industrialize the ML lifecycle: versioned pipelines, a registry, and drift monitoring.

LANGUAGE

LLMOps

Evaluation harnesses, guardrails, and cost control for language-model apps.

ABOVE THE PLATFORM

AI Agent Builder

The agent layer that scaffolds and ships multi-step systems on the foundation.

MODELS

LLM Fine-Tuning

Adapting model behaviour where prompting and retrieval alone fall short.

ABOVE THE PLATFORM

GenAI OS

The shared operating layer that use cases plug into, hosted on the platform.

CATEGORY

Scaled GenAI and AI Platforms

The parent practice this build track belongs to.

FREQUENTLY ASKED QUESTIONS

What platform owners ask us first.

Use cases solve one problem; a platform makes the next ten cheaper. We design shared layers so retrieval, evaluation, and governance are built once and reused, not rebuilt per team.

Yours. We build cloud-native on AWS, Azure, GCP, or Databricks, reusing sound existing tooling rather than replacing it.

We start with one working path through the required platform layers. We agree its scope, access requirements and delivery timing during discovery, then expand after the first team has validated it.

DESIGN YOUR AI PLATFORM

Map the layers your use cases actually need, and what to build first.

A governed foundation many teams share, built cloud-native on your environment.

Talk to our AI platform teamSee the implementation view