An agentic data scientist in chat — LangGraph, deployed to Azure.
A coordinated network of LangGraph agents handles querying, cleaning, visualisation, and model training — letting non-technical users explore data through natural language and reducing analyst cycle time from days to minutes.
The client is a leading data science company working with enterprises across healthcare, retail, finance, and supply chain. Their teams manage large analytical workloads for clients — data exploration, modelling, forecasting, insight generation. With rising demand for faster turnaround and more accessible analytics, the organisation wanted a modern way for clients to interact with their data more naturally and independently.
Clients regularly required quick insights, ad-hoc analysis, visualisations, and predictive models — yet every request triggered manual cycles of data cleaning, scripting, validation, and reporting. These repetitive tasks slowed response times and created operational bottlenecks across teams. The organisation wanted a conversational interface that could act like an on-demand data scientist — analysing data, training models, creating visualisations, and generating reports on its own.
A fully agentic LangGraph-powered conversational data science assistant was developed and deployed entirely on Microsoft Azure. The system connects to any client database, understands natural-language instructions, performs end-to-end data analysis, trains models, handles long-running background jobs, and produces standardised reports — all through a chat interface.
The platform uses a network of coordinated LangGraph agents, each responsible for a specific analytical capability. When a user asks a question, the system determines the required steps, cleans the data, runs the appropriate analysis or model training in the background, and returns results through the conversational interface. All processing, storage, and agent coordination are fully deployed within the client's Azure environment — ensuring security, scalability, and compliance.
- ▸Clients depended on analysts for every question — interpret, pull, clean, explore, model, report.
- ▸Frequent delays and repetitive manual work for the data science team.
- ▸Inconsistent outputs depending on which analyst handled each request.
- ▸Limited ability for business users to explore data directly.
- ▸High effort to maintain standardised reporting formats across clients.
What was built and how it fits together.
Agentic architecture (LangGraph)
Specialised agents handle database querying, data cleaning, visualisation, model training, and report compilation. A central orchestrator decides which agent takes action.
Conversational data analysis
Natural-language questions — 'Show monthly trends', 'Run a churn model', 'Find outliers in sales data' — trigger the entire workflow automatically.
End-to-end data preparation
Agents handle cleaning, transformation, and validation. Users work with raw datasets without worrying about quality issues.
Long-running background jobs
Model training and heavy statistical computation run asynchronously on Azure, with results delivered when complete.
Dynamic in-chat visualisations
Charts, trends, comparisons, and forecasts generated directly within the conversational interface.
Pre-built report templates
Standardised report types — performance summaries, forecasts, KPI packs, anomaly reports — for uniform quality across clients.
Azure-native deployment
All agents, storage, orchestration, and processing run inside the client's Azure environment.
The operational result, measured against the starting state.
- ▸Self-service analytics for non-technical business users.
- ▸Insight generation reduced from days to minutes.
- ▸Consistent, high-quality reports across all client engagements.
- ▸Significant reduction in manual workload for the data science team.
- ▸Real-time modelling and predictions available directly through chat.
- ▸Scalable, secure Azure deployment suitable for enterprise environments.