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Most AI projects fail on data, not models — silos, poor quality, stale batches and warehouses built for reports, not for AI. We build reliable, AI-ready data pipelines, lakehouse architectures, feature stores and streaming systems, so every model, dashboard and AI feature runs on data you can trust, at scale.
Enterprises, SMEs and fast-growing teams trust ZTS India
Organisations struggle to operationalise AI due to inefficient data pipelines, inconsistent data quality and a lack of scalable architecture. Click a panel to see the challenge, how we fix it, and what changes.
Customer data in the CRM, events in one warehouse, files in another, definitions that differ by team. Every AI project starts by rebuilding the same plumbing, and no two answers to 'how many customers?' ever match.
Duplicates, gaps, inconsistent formats and no validation. Models trained on it learn the mess faithfully, dashboards contradict each other, and nobody trusts the numbers enough to act on them.
Nightly batch jobs mean AI acts on yesterday. Fraud, personalisation and operations decisions that need the last five minutes are stuck waiting for the last twenty-four hours.
Classic BI pipelines were never designed for feature stores, embeddings, vector search or ML training loads. Bolting AI onto them means constant workarounds and a foundation that fights you.
Volume grew, pipelines that once ran in minutes now take hours, costs climbed, and the architecture that got you here cannot get you to next year. Every new source makes it worse.
Traditional pipelines were built for reports. AI data engineering is built for systems that retrieve, reason and act in real time.
Every capability we deliver is a means to a business end. These are the ends our clients measure.
Streaming pipelines let AI and teams act on what is happening this minute, not last night.
Automated validation and governance mean the numbers agree and people act on them.
Infrastructure built for ML and RAG, so AI projects plug in instead of rebuilding plumbing.
A lakehouse architecture that absorbs new sources and growing volume without a rebuild.
Reusable pipelines and a semantic layer get new questions answered without a project each time.
Partitioning, tiering and elastic compute stop your data bill running away with itself.
Every model, dashboard and AI feature draws from one governed source instead of its own copy.
Catalogue, lineage and access control so you can prove where every number came from.
From pipelines and lakehouses to feature stores and streaming systems — the full data foundation your analytics and AI run on.
The backbone of everything else — automated, reliable pipelines that move and transform data from every source into AI- and BI-ready datasets, batch or streaming.
A modern, scalable home for your data — warehouse, lake or lakehouse — designed for both the BI you run today and the AI workloads you are building toward.
Move from yesterday to now. Streaming pipelines that ingest and process events as they happen, so fraud, personalisation and operations run on live data.
Make your data trustworthy and auditable. Validation, cleansing, cataloguing, lineage and access control so people and models can rely on what they're given.
Turn raw data into what models actually need — labelled, transformed, feature-engineered and versioned — with the pipelines to keep it flowing as you scale.
The AI-specific layer classic warehouses lack — feature stores for consistent ML features, and vector databases for embeddings and retrieval.
Move off the legacy warehouse without losing a byte or a day. Planned, validated migration to a modern platform, with the old and new running in parallel until you trust it.
Turn the foundation into value people can see — dashboards, self-serve analytics and internal data products that make the trustworthy data actually usable.
More than a decade delivering measurable results for enterprises, SMEs and technology companies worldwide.
Three teams whose AI finally worked once the data foundation did.
Challenge: Recommendations ran on day-old data from a warehouse built for reports. Personalisation was always a step behind the customer.
Solution: A streaming lakehouse with real-time feature computation feeding the recommendation models, alongside the existing BI.
Challenge: Three earlier AI attempts had failed on data — silos, poor quality, no lineage. Nobody trusted the numbers enough to model on them.
Solution: We rebuilt the foundation first: unified pipelines, automated quality, a catalogue and full lineage, then AI-ready feature datasets.
Challenge: A RAG initiative had no infrastructure — documents scattered, no embeddings, no vector store, no permission model.
Solution: Embedding pipelines, a tuned vector database and permission-aware data access, delivered as reusable infrastructure for every future AI feature.
Get a free 30-minute data assessment. We'll review your pipelines, quality and architecture, and tell you what to fix before the next AI project — no pitch.
A structured path from mapping your sources to a governed, scalable platform your team can run and extend.
Map your sources, use cases and the AI and BI workloads the platform must serve.
Audit current data quality, architecture and gaps against where you need to be.
Design the pipelines, warehouse or lakehouse, and AI-ready layers around your stack.
Engineer ingestion, transformation, quality and feature pipelines, batch and streaming.
Add catalogue, lineage, quality monitoring and access control across the platform.
Test freshness, correctness, performance and cost against agreed targets.
Roll out with CI/CD and monitoring; migrate off legacy with parallel-run validation.
Tune cost and performance continuously, and extend the platform to new sources.
Book a free, no-obligation data assessment. We'll review where your data stands, tell you honestly what's blocking your AI, and give you a costed plan to fix it.
The disciplines that separate a data platform that survives production from one that quietly falls apart under load.
We design for the volume you will have in two years, not just today — elastic, partitioned and cost-aware from the start.
Validation, testing and monitoring built into every pipeline, so bad data is caught before it reaches a model or a report.
Access control, encryption, lineage and policy enforcement designed in — not bolted on after an audit finding.
Partitioning, tiering and right-sized compute so pipelines stay fast and the data bill stays predictable.
Freshness, volume and quality monitoring with alerting, so pipeline failures surface before your dashboards lie.
CI/CD, infrastructure-as-code and version control applied to data pipelines, so releases are safe and reproducible.
Catalogued, documented pipelines and datasets, so the next engineer can understand and extend without archaeology.
Depth across pipelines, platforms, streaming, feature stores and governance — the engineering an AI-ready data foundation actually needs.
Reliable batch and streaming pipelines that move and transform data from any source into AI- and BI-ready datasets.
Snowflake, Databricks and BigQuery platforms designed for both analytics and ML at scale.
Kafka, Kinesis, Flink and Spark pipelines that process events as they arrive for live AI.
Feature engineering, labelling, versioning and reproducible transforms that models depend on.
Consistent ML features and tuned vector databases for embeddings and retrieval.
Validation, cataloguing, lineage and access control that make data trustworthy and auditable.
Scalable, cost-aware data platforms on AWS, GCP and Azure, cloud or hybrid.
CI/CD, Terraform and version control applied to data, so pipelines ship like software.
Semantic layers, dashboards and data products that turn the foundation into visible value.
A vendor-agnostic data stack across clouds and tools — chosen to fit your workloads, team and budget rather than a platform we resell.
An AI-ready data foundation shaped around your sources, your workloads and your compliance obligations.
Choose the model that fits your goals, your internal capacity and how far along you already are.
Best for: teams with engineers who need direction. Data assessment, architecture design and a modernisation roadmap your team implements.
Get a data assessment →Best for: teams who want it built. We design, build and run the whole platform — pipelines, warehouse, quality and AI-ready layers.
Discuss implementation →Best for: ongoing scale. Embedded data engineers building and extending your platform as an extension of your team.
Build my team →Data engineering is the foundation. Explore the AI services that run on top of it.
Readiness assessment, use-case prioritisation and a costed roadmap.
Deploy, monitor and scale ML in production.
Bespoke ML, vision and decision systems trained on your data.
Production-grade LLM, RAG and agent systems.
Connect AI to your CRM, ERP and existing workflows.
Automate the workflows RPA cannot handle.
The reason teams trust us with the foundation, not just the models.
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"Thanks to ZTS developers, highly experienced and skilled to develop web applications with API integrations. I was expecting user-friendly options for both web app development and UI/UX designs and they provided me with that. And, the most impressive thing I found is ..."
They delivered beyond expectations — our project saw real growth thanks to ZTS Infotech's passion and commitment
Ms. Shikha Bhatia shares her positive experience with Zebra Techies Solution, where she talks about how our Organic Social Media Management Services helped her brand achieve consistent engagement and authentic growth!
They went above and beyond to support us during challenging times. The ZTS Infotech team delivered with dedication, professionalism, and care — truly a partner we can count on!
They helped us turn hurdles into wins — Sam's e-commerce transformed thanks to ZTS's committed support
Outstanding collaboration and flawless execution — ZTS Infotech made the entire process smooth and truly result-driven!
A partner that builds data foundations for AI — trustworthy, real-time and ready for the models you're building toward.
We engineer data for ML training, RAG and real-time AI — feature stores and vector infrastructure, not just another reporting warehouse.
Pipelines, platforms, MLOps and the models themselves under one roof — we build the foundation and what runs on it.
Snowflake, Databricks, BigQuery, AWS, GCP, Azure — we design around your stack and budget, not a platform we resell.
Validation, lineage and access control from day one, so your data is trustworthy and auditable, not just plentiful.
CI/CD, infrastructure-as-code and testing applied to data — 15+ years of shipping software, brought to your pipelines.
Encryption, access control and data-retention policy designed in from the first sprint, for regulated environments.
Tell us where your data is holding you back. We'll come back with an honest assessment, a plan to make it AI-ready, and a transparent estimate — free.
Traditional data engineering was built mainly to feed BI dashboards — structured, batch, report-oriented. AI data engineering is built to feed machine learning, RAG and real-time AI: it handles structured and unstructured data, streaming and batch, and adds the layers AI needs — feature stores, vector databases, embedding pipelines and versioned training datasets — with automated quality throughout. Same discipline, a foundation designed for AI rather than reports.
It is the most common reason AI stalls. Models are only as good as the data behind them, and if that data is siloed, poor quality, stale or missing the features a model needs, no amount of modelling saves it. We fix the foundation first — pipelines, quality, governance and AI-ready datasets — so your AI projects build on something solid instead of failing on the same problem repeatedly.
Usually not. In most cases we modernise and extend what you have — adding streaming, quality, governance and AI-ready layers where they are missing — rather than ripping out a warehouse that already serves your reporting. Where a migration genuinely is the right call, we run old and new in parallel and validate before cutover, so nothing is lost.
Yes. We build streaming pipelines with Kafka, Kinesis, Pub/Sub, Flink and Spark that ingest and process events as they happen, so fraud detection, personalisation and operational AI act on live data rather than last night's batch — alongside the batch pipelines you still need.
A feature store gives training and serving one consistent source of features, killing the train-serve skew that quietly wrecks model accuracy. A vector database stores embeddings so AI can retrieve by meaning — the backbone of RAG and semantic search. If you are doing ML or LLM work at any scale, you will need both, and classic warehouses do not provide them.
We build validation, cleansing and deduplication into the pipelines, add a data catalogue and end-to-end lineage so you can trace any number to its source, and set up quality monitoring that alerts when something drifts. The goal is data people and models actually trust, not just data that exists.
We are vendor-agnostic. We work across Snowflake, Databricks, BigQuery, Redshift and Synapse, with dbt, Airflow, Kafka, Spark and the rest, on AWS, GCP or Azure. We recommend the stack that fits your workloads, team and budget rather than one we resell.
Yes. Access control, encryption, lineage and policy enforcement are designed in from the start, which matters most in regulated sectors like finance and healthcare. You get a platform where you can prove who accessed what and where every number came from.
Yes. We hand over documented, catalogued, infrastructure-as-code platforms and train your team to operate and extend them. You can run it yourself or keep us on as a dedicated data engineering team — no lock-in either way.
A data assessment and architecture is a small fixed-price engagement. Building the platform is priced fixed-scope or as a monthly dedicated team, depending on how much you want built versus advised. The initial assessment call is free.
