BizStats engineers the cloud, data, DevOps and AI behind growing businesses, so the tools you already have finally work as one.
Most organisations already have plenty of technology: applications, databases, cloud accounts, spreadsheets, scripts. Each was added for a good reason. Together they leave data copied by hand, releases that depend on one person, and reports that disagree.
That's where we work — in the connections. We bring integration, automation and engineering discipline to the systems you already have, and build new ones that fit.
Different problems, same discipline. Whether it's a migration, a data platform or a first AI model, the work moves through four stages — and each one leaves the system clearer than it found it.
We learn the business objective, the technical constraints and the systems already in place, until we can describe the real problem in plain words.
We design a solution that fits the requirements, with maintainability, security and day-to-day operations considered from the start.
We build, integrate, automate, test and deploy, so the solution runs in your environment rather than only in a demo.
We measure how it performs, improve reliability, simplify operations and adapt the system as the business grows.
Our work falls into six areas. Most projects touch more than one, which is exactly why we treat them as a single engineering discipline.
Cloud and infrastructure
We design, migrate and look after the infrastructure your applications run on: moving workloads from ageing servers to the cloud, designing networks and servers, and providing managed IT that works as your outsourced IT department.
DevOps and automation
We automate the path from a developer's change to a running release: development environments that set up in one step, continuous integration that builds and tests every commit, and release workflows you can repeat.
Data and business intelligence
We turn data spread across applications and spreadsheets into a warehouse, clearly defined KPIs and dashboards people trust, so every meeting starts from the same numbers.
AI and machine learning
We build machine learning models for specific business questions — classification, regression, clustering and forecasting — with evaluation built in, so you know how far to trust the output.
Big data and APIs
We process high-volume data and deliver the results through APIs, including real-time notifications for major events and KPI shifts. The APIs hide the complexity so your developers can plug capabilities into the systems you already run.
Testing and digital learning
We help coaching institutes and training teams run their own online test environments: question banks, scheduled exams, automatic correction and performance reports.
Pick any capability to see what it covers, a problem it typically solves and the services behind it. The lines show which capabilities usually work together on a project.
Bringing data from many systems into a warehouse built for analysis, with documented, repeatable transformations.
The same figures living in several systems, and disagreeing.
Moving applications from ageing servers and VMs to the cloud, planned and piloted before the big move.
Typical problem: Applications on on-premise servers that are costly to maintain and hard to scale.
Works closely with: Infrastructure, DevOps
Reproducible environments, continuous integration and release management that make delivery routine.
Typical problem: Releases that depend on manual steps and one person's knowledge.
Works closely with: Cloud, Automation, Software systems, Testing
Data-driven and education applications, with capabilities delivered through APIs your developers can integrate.
Typical problem: Business processes held together by spreadsheets, email and re-keyed data.
Works closely with: DevOps, Testing, APIs
Automated tests on every commit for software teams, and online test environments for educators.
Typical problem: Assessments run on paper or ad hoc tools that do not scale to many students and batches.
Works closely with: DevOps, Software systems
Network and server design, plus managed IT that works as your outsourced IT department.
Typical problem: Network or server problems that only get noticed when people cannot work.
Works closely with: Cloud, Automation
Taking repetitive builds, deployments and configuration out of human hands, with alerts when something needs attention.
Typical problem: Recurring configuration and deployment tasks repeated by hand every week.
Works closely with: Infrastructure, DevOps, APIs, AI
Clean interfaces that hide technical complexity and let systems exchange data reliably, including real-time notifications.
Typical problem: Systems that cannot share data without exports, re-keying or fragile scripts.
Works closely with: Software systems, Automation, Data
Bringing data from many systems into a warehouse built for analysis, with documented, repeatable transformations.
Typical problem: The same figures living in several systems, and disagreeing.
Works closely with: APIs, Analytics, Machine learning
Clearly defined KPIs, reports and dashboards built around the decisions people make.
Typical problem: Reports assembled by hand from spreadsheets before every review meeting.
Works closely with: Data, Machine learning
Classification, regression and clustering models built and evaluated for a specific business question.
Typical problem: Customer, transaction or operational data that could predict churn, fraud or demand, but is not used that way.
Works closely with: Data, Analytics, AI
Practical AI scoped to a real problem, with the data, evaluation and integration it needs to be useful.
Typical problem: Interest in AI, but no clear view of which problem it should solve first.
Works closely with: Machine learning, Automation
Four situations we see often. Each one starts as a business problem, gets an engineering approach, and ends with a system that is easier to run. These are illustrations of how we work, not accounts of specific client projects.
Illustrative scenario: a typical situation, not a client case study.
An organisation runs several systems across different environments, and keeps them going through manual operational work.
Assess the architecture, identify the bottlenecks, improve the deployment workflows and introduce automation where it pays off.
A clearer, more maintainable operating environment that is easier to change.
Services involvedIT infrastructureDevOps automationCloud migration
Choosing a technology partner is really choosing how decisions get made on your behalf. These five principles guide ours, on every project and at every size.
We understand what the business needs to achieve before recommending anything. Sometimes the right answer is a smaller change than expected.
Software, infrastructure, data and automation behave as one system in production, so we design them as one.
We aim for solutions your team can run, monitor and maintain, not impressive demonstrations that need us forever.
Tools are chosen for the requirements in front of us, not forced into the same architecture every time.
Maintainability, integration and reliability matter most in year three, so we plan for them in week one.
Almost every system we work on has the same six layers. Here is what each one does, and the technologies our team works with there. Problems rarely stay inside one layer, so neither do we.
The software people use: data-driven and education applications.
APIs and connectors that let systems exchange data.
Where information is stored, combined and prepared for analysis.
Analytics and models that turn data into decisions.
The compute, network and cloud platforms underneath.
How it is all built, deployed and kept running.
Names are trademarks of their owners. They show technologies our team works with, not formal partnerships or certifications.
BizStats has been building software and systems from Chennai since 2015. The team brings together IT professionals with different specialities, and that mix shapes how we work: most real problems cross the boundaries between infrastructure, software and data, so we look at the whole system before changing any part of it.
We like problems that are messy at the start — systems that have grown for years, data nobody fully trusts, processes that depend on a few people. And we plan for what comes after launch day: monitoring, maintenance, and changes that don't break what already works.
We would rather show than claim. These are the sequences we follow on real projects, documented on each service page so you can check them before you talk to us.
Technology keeps getting more connected, and so do the problems businesses bring us. These are the directions we are investing in — practical ones, grounded in engineering rather than predictions.
AI applied where the data and the process can support it, evaluated honestly and integrated into everyday work.
Automated pipelines with monitoring and alerts, so teams know what ran, what failed and why.
Systems designed with APIs and integration from the start, instead of bolted together later.
Reproducible environments and infrastructure described as code, so change is routine rather than risky.
Clear definitions and shared sources, so analytics informs decisions instead of starting arguments.
Tell us about a technology challenge, a system that needs improving or an idea you want to test. We'll help you work out what it would take.