SCALED GENAI AND AI PLATFORMS
Scaled GenAI and AI platform services for enterprise delivery
We build shared AI platforms so teams can develop, evaluate and operate multiple use cases on a governed software foundation.
- Standardised MLOps and LLMOps on one governed foundation
- Retrieval, evaluation, and guardrails as shared services
- Compounding economics, so the twentieth use case barely registers
Choose the AI platform capability you need.
AI platform design and implementation
Architect and build the end-to-end platform for your cloud and security posture.
02MLOps
Versioned data and models, automated pipelines, drift monitoring.
03LLMOps
Prompt and version management, evaluation, guardrails, cost control.
04LLM fine-tuning
Adapt foundation models to your domain with PEFT techniques.
05AI agent builder
Shared tooling and patterns to compose and ship agents on one foundation.
06GenAI OS
The connective services and developer experience that make GenAI a managed capability.
A one-off model skips most of this.
A platform makes every step repeatable and observable, and closes the loop back to scope.
The steps most teams skip are the ones that decide production success: a real evaluation harness (so you can tell whether a prompt change regressed the long tail, not just the demo), cost observability, and a feedback loop that catches drift before users do.
The payoff is compounding. The second use case is cheaper than the first.
A seven-layer reference architecture, governed and reusable across teams.
This view separates the full platform into seven architectural layers. The implementation service groups the delivery work into six connected areas.
Applications
Assistants, copilots, agents, embedded product features.
Your product surfaces + agentic runtimes
Orchestration and retrieval
RAG, prompt management, routing, tool calls.
LangChain / LangGraph · hybrid search · rerankers
Models
Foundation and fine-tuned models, model registry.
Managed and open models · LoRA / QLoRA adapters
Vector and feature data
Embeddings, retrieval indexes, features.
pgvector · Pinecone · Weaviate · Qdrant · Feast
MLOps and LLMOps
Pipelines, CI/CD for models, versioning.
MLflow · SageMaker · Vertex AI · Databricks
Observability and eval
Traces, cost, quality, drift, guardrails.
LangSmith · Langfuse · Arize Phoenix · NeMo Guardrails
Governance
Access, audit, model inventory, policy.
Catalogue-based governance · responsible AI controls
The most expensive mistake we see is fine-tuning to fix a retrieval problem.
A thin adapter on top of retrieval, not one instead of the other. Retrieval keeps the facts current; the adapter keeps the form consistent.
And when a more constrained model is the better choice.
We do not sell platform for its own sake. Build the first use case pragmatically; extract the reusable parts once demand is real.
The signal to invest is specific: three or more teams solving the same plumbing, or a first system you cannot confidently evaluate or cost. We help you time that inflection rather than paying for a platform ahead of the work that justifies it.
Stood up fast, to spec
You know what you need and want the foundation delivered against a defined architecture.
A team that owns and evolves it
A dedicated pod that keeps the platform current as your use cases and models change.
You already have one
For a platform that has become slow, costly, or hard to govern. We find where and fix it.
What ML leads ask us first.
No. Build the first one pragmatically, then extract the reusable layers as demand grows. We help you find the point where platform investment starts paying for itself.
Usually both. MLOps governs classical and predictive models; LLMOps handles prompts, evaluation, retrieval, and guardrails for language models. We implement them on one foundation.
We build cloud-native on your platform of choice and stay tool-pragmatic across MLflow, SageMaker, Vertex AI, Databricks, and the wider LLMOps ecosystem.
Yes. Our assess-and-optimise track targets slow deployment, runaway cost, or weak governance on existing platforms.
What we are writing about platform economics.
Which layers do your use cases actually need?
Let's assess what the platform has to carry, and what to build first, so the investment lands ahead of the demand rather than behind it.