Platform Engineering

Platform engineering for production-grade AI systems

Introduction

Build platforms that scale with your AI workloads

AI ambitions are easy to articulate. But turning them into systems that run reliably in production is an entirely different challenge. The right platform underneath, one that provides stability and reliability at scale, is what makes the difference.

At DTC Infotech, we design and build cloud-native platform architectures across Databricks, Snowflake, Azure Synapse, BigQuery, Neo4j, and Pinecone, connecting engineering depth to the business's aspirations.

Offerings

Data platform strategy

Data architecture decisions made early tend to stay for a long time. Getting them right means building for today's business needs and tomorrow's AI demands. Our cloud platform engineering services are designed to be AI-ready from the start, not retrofitted later.

AI, ML and GenAI strategy

AI, ML and GenAI strategy

Adopting AI at scale goes beyond choosing the right model; it is more about building the right conditions around it. Using platforms and tools that fit the existing environment, our digital engineering services help enterprises move from isolated pilots to production-grade AI.

Agentic AI and autonomous systems

Agentic AI and autonomous systems

Most enterprise workflows still wait on people at every decision point. Agentic AI changes that dynamic. We build intelligent agents that handle complex, multi-step processes end-to-end. Agents act on information, adapt to new inputs, and complete tasks without waiting for human instruction.

RAG-based search and decision systems

Enterprise knowledge sits in documents, databases, and systems that rarely talk to each other. A well-built RAG solution connects that knowledge to an LLM's reasoning capabilities, producing outputs that are accurate, context-aware, and traceable.

Trustworthy AI and responsible AI

AI that cannot be explained or audited creates liability. We embed governance and ethics across models, data, and decisions from the start, not as a sign-off process at the end.

Capabilities

INGESTION

Data ingestion and streaming

Data ingestion and streaming

Real-time data movement is where most platform projects either succeed or fail. We work across Kafka, Azure Event Hub, and Spark Structured Streaming to build pipelines that handle continuous data flows without manual intervention.

STORAGE

Data storage and processing

Data storage and processing

The storage layer needs to support both analytical workloads and access to AI models. We deploy and manage Databricks, Snowflake, BigQuery, Azure Synapse, and Delta Lake depending on what the workload demands.

ENGINEERING

AI and ML engineering

Model development and deployment need infrastructure that keeps pace with iteration. We work across MLflow, Azure ML, SageMaker, Vertex AI, and MosaicML to manage the full ML lifecycle.

LLMOPS

GenAI and LLMOps

GenAI and LLMOps

Running large language models in production requires more than a working prompt. We use LangChain, AutoGen, Pinecone, Qdrant, OpenAI, and Hugging Face to build and operate GenAI systems.

ANALYTICS

Visualisation and BI

Visualisation and BI

Data that cannot be understood clearly does not get used. We build reporting and visualisation layers across Power BI, Tableau, Looker, Databricks SQL, and Sigma so insights stay current.

GOVERNANCE

Governance and MLOps

Governance and MLOps

As AI spreads across the organisation, governance needs to keep pace. Unity Catalog, Alation, MLflow Registry, Azure Purview, and DataOps.live give teams the visibility and control they need.

AGENTIC

Agentic analytics

Agentic analytics

The next layer beyond dashboards is systems that reason and act on data. We build agentic analytics capabilities using CrewAI, LangGraph, and AutoGen, moving from reporting to recommendations.

Case studies

Retail

Retail

30% uplift

A customer insights engine built on Databricks, Pinecone, and LangChain delivered a 30% increase in campaign conversion for a retail client by making personalisation faster and more accurate.

Our approach

Enterprise Platforms

Core systems carry critical business logic. They are integrated with data and digital platforms, enabling them to work alongside modern AI services rather than operating in isolation.

Quality and Test Data Platforms

Synthetic data is used to create realistic test environments, helping teams validate systems quickly while keeping sensitive information protected.

AI and ML Platforms

Models that work in notebooks rarely translate to production. Platforms are set up so models can be built, tested, and deployed into real workflows reliably.

Workflow and Decision Platforms

Automation creates value only when teams can see and trust system decisions. Workflows are designed to handle routine processes without turning operations into a black box.

Application Platforms

Applications need to stay stable while adapting to change. Foundations are built using cloud best practices so software can run reliably, update smoothly, and scale as business evolves.

Integration and Event Platforms

Data and event flows are connected so information moves smoothly across systems without breaking when changes happen elsewhere.

What next

If your AI ambitions are outpacing your current platform infrastructure, we can help identify where the architecture needs to evolve. Talk to our cloud platform engineering specialists to find out what that looks like for your business.

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