Data engineering services that give your AI something reliable to work with
Innostax builds the data infrastructure that AI systems run on — pipelines, warehouses, vector databases, and the data quality layer that determines whether your AI produces reliable outputs or unreliable ones. A dedicated Tech Lead accountable for what gets built and how it performs.
The data problem that limits every AI project
Your AI is only as good as the data it runs on. Most teams discover this too late.
The most common reason AI projects fail to deliver in production isn’t the model. It’s the data.
LLMs hallucinate when the retrieval layer doesn’t surface the right information. Matching systems produce wrong results when the underlying embeddings are built on inconsistently formatted data. Automation pipelines fail when the documents they process arrive in formats that weren’t anticipated. Analytics dashboards show numbers that nobody trusts because the pipeline that produces them has undocumented transformations and no data quality checks.
The pattern is consistent: teams invest in AI capabilities and discover that the data infrastructure those capabilities depend on wasn’t designed to support them.
The vector database that needs clean, well-chunked documents to retrieve accurately is being fed raw text with inconsistent formatting. The analytics platform that needs a reliable data warehouse is reading from a production database with no transformation layer. The AI pipeline that needs structured, validated inputs is receiving whatever the upstream system happens to produce.
Data engineering is the work that makes AI reliable. Not the most visible work. Not the work that gets demonstrated to stakeholders. But the work that determines whether the AI system you build actually works in production.
AI Data Infrastructure
Data Systems We Build for Scalable AI
Every engagement is different. Use the links below to explore focused pages for this service.
Data pipelines (ETL/ELT)
End-to-end data pipelines that extract data from your source systems, transform it into the format your downstream systems require, and load it reliably — on schedule or event-driven. Built with the error handling, retry logic, idempotency, and monitoring that makes a data pipeline trustworthy in production rather than requiring constant intervention when something changes upstream.
Learn more →Data warehouses and lakehouses
Structured data storage designed for the analytical queries and AI workloads your business needs to run — not the operational queries your production database was designed for. Schema design, partitioning strategy, and query optimisation that keeps analytical workloads fast as data volumes grow. On your cloud of choice: AWS (Redshift, S3 + Athena), Azure (Synapse, Data Lake), GCP (BigQuery).
Learn more →Vector databases and embedding pipelines
The data infrastructure that RAG systems and semantic search run on — document chunking, embedding generation, vector indexing, and the retrieval quality evaluation that determines whether your AI surfaces the right information. Built for your specific data type and retrieval requirements: Pinecone, pgvector, Weaviate, or Elasticsearch depending on your scale, latency requirements, and existing infrastructure.
Learn more →Data quality and validation
Data quality checks built into the pipeline — schema validation, completeness checks, consistency rules, and anomaly detection that catches data problems before they propagate to downstream AI systems and analytics. The difference between an AI system that produces unreliable outputs because the data is wrong and one that flags data quality issues before they become output quality issues.
Learn more →Real-time data streaming
Event-driven data architectures for teams that need data to flow in real time rather than in scheduled batches — Kafka, AWS Kinesis, or cloud-native streaming services. For AI systems that need to act on current data, not yesterday's data.
Learn more →Analytics and reporting infrastructure
The data layer that business intelligence and reporting tools sit on — clean, documented, reliable. Data models designed for the questions your business actually asks, transformation logic that's version-controlled and testable, and the documentation that makes the data trustworthy to the people who use it.
Learn more →Data migration
Moving data from legacy systems, on-premise databases, or fragmented data stores to modern cloud infrastructure — with the validation, integrity checking, and rollback procedures that make a data migration safe. Every record validated. Zero data loss as the standard, not the aspiration.
Learn more →
The data layer is the foundation. Everything built on top of it inherits its quality.
For LLM integrations and RAG systems
The retrieval quality of a RAG system is determined almost entirely by the quality of the data layer beneath it — how documents are chunked, how embeddings are generated, how the vector index is structured, and how retrieval is evaluated. We design the data layer for the retrieval task, not generically. A RAG system built on a well-designed vector pipeline retrieves accurately. One built on raw, unprocessed data retrieves inconsistently.
For agentic AI systems
Agents that reason across data sources are only as reliable as the data those sources contain. Inconsistent data formats, missing fields, and undocumented transformations are the failure modes that cause agents to make wrong decisions confidently. We build the data layer that gives agents reliable, well-structured inputs — and the validation layer that catches data quality issues before they enter the agent’s reasoning chain.
For workflow automation
Automated pipelines that process documents, extract data, and route decisions are sensitive to data format changes. We build the data transformation layer that normalises inputs from diverse upstream sources into the consistent format the automation requires — and the monitoring that detects when upstream formats change before the automation breaks.
For analytics
Analytics that business leaders trust require a data layer that’s clean, documented, and consistent. We build the warehouse layer and transformation logic that makes analytics reliable — not the kind where every number requires a caveat about how it was calculated.
Designed for the AI workloads it needs to support, not generically.
Data audit before architecture.
Before we design any data infrastructure, we understand what you have — your source systems, your data formats, your data quality issues, your current pipeline architecture, and the AI or analytics workloads the new infrastructure needs to support. The audit surfaces the specific data quality problems that will limit your AI system’s reliability if they’re not addressed before the AI layer is built.
Schema and data model design.
Data models designed for the queries and AI workloads they need to support — not adapted from the operational schema of your production database. Schema decisions made with the downstream retrieval, transformation, and analytical requirements in mind from the start.
Pipeline reliability as a first-class requirement.
Data pipelines are built with error handling, retry logic, idempotency, and dead-letter queues for failed records. A pipeline that fails silently — dropping records, producing incorrect transformations, or falling behind without alerting — is worse than no pipeline. We treat pipeline reliability the same way we treat application reliability: as an engineering requirement, not a nice-to-have.
Data quality checks at every layer.
Schema validation, completeness checks, consistency rules, and statistical anomaly detection — at ingestion, at transformation, and at the point where data enters AI systems. Data quality problems caught in the pipeline are cheap. Data quality problems that reach AI outputs are expensive.
Version-controlled transformations.
Every data transformation is code — version-controlled, reviewed, testable. The transformation logic that determines what your analytics and AI systems see is treated as a first-class engineering asset, not a collection of undocumented SQL queries that nobody fully understands.
Reliable Data Infrastructure for AI Systems
2-week free trial on real infrastructure.
Your data, your source systems, your actual pipelines. You’ll see within two weeks whether the data infrastructure we build is reliable and well-structured or whether the quality issues are already visible. If the latter, walk away. No invoice.
1-day termination notice.
If the engagement isn’t delivering the data infrastructure your AI systems need, you’re out tomorrow. No lock-in, no notice periods.
Engineers who stay for the long build.
Great Place to Work certified — the engineer who designs your data model and pipeline architecture in month one is still accountable for it in month six. Data infrastructure is the layer everything else depends on. Continuity in the team that built it is how it stays trustworthy as your data grows and your AI workloads evolve.
Data Engineering Case Studies and Production Results
Semantic matching infrastructure for AI-powered hiring
Vector embedding pipeline using Hugging Face sentence transformers — candidate and job data converted into AI-consumable embeddings stored in PostgreSQL with pgvector and Elasticsearch. The retrieval layer that powers semantic candidate-job matching at scale, with similarity scoring that explains why a match was made. Geospatial data integrated via PostGIS and Nominatim for location-based matching. Data infrastructure designed specifically for the AI matching workload it needed to support — not a generic pipeline adapted to a semantic search use case.
Multi-model document processing pipeline for banking Document ingestion and transformation pipeline using PyPDF and PDFPlumber
Extracting and structuring data from property appraisal documents for multi-model AI processing via AWS Bedrock. Event-driven architecture on AWS (EC2, Lambda, SQS, S3) with end-to-end encryption and audit logging at every stage. Data pipeline designed for the compliance requirements of a regulated banking environment — every input traceable, every transformation logged, every output evidenced.
Full data migration, zero data loss
Complete data migration from legacy systems to Azure — 100% successful, zero data loss, with automated validation at every stage. The migration architecture that makes a high-stakes data move safe: parallel environments, record-level validation, rollback procedures tested before cutover. Data migration where the quality of the data engineering was the entire value.
Data Engineering for CTOs, Analytics Teams, and Scale-Ups
CTOs and engineering leads building AI systems
Who’ve discovered that the data infrastructure their AI depends on wasn’t designed to support it. The retrieval that’s inconsistent, the pipeline that’s unreliable, the data quality that’s limiting output quality. Data engineering is the fix that makes the AI layer work.
Data and analytics teams
Whose current pipeline is a collection of scripts and undocumented transformations that works until it doesn’t — and whose analytics are trusted by nobody because the numbers can’t be traced back to their source. Modern data infrastructure built to a production standard.
Growth-stage companies
whose data architecture was designed for their previous scale and is now a constraint — slow analytical queries, unreliable pipelines, data quality issues that compound as data volumes grow.
FinTech and HealthTech teams
where data infrastructure needs to meet compliance requirements — HIPAA data residency, encrypted storage, audit logging, and the access controls that regulated data environments require.
Tech stack
Data Engineering Tech Stack and Tools We Use
We build scalable data systems using modern tools for pipelines, streaming, storage, transformation, and cloud to ensure reliable data flow.
- Great Expectations
- dbt tests
- Custom validation layers
FAQ about Data engineering services.
AI systems inherit the quality of the data they run on. An LLM integration with poor retrieval data produces inconsistent outputs. A matching system built on inconsistently formatted embeddings produces wrong matches. An automation pipeline fed unvalidated inputs fails on edge cases. The model is only as good as the data layer beneath it — and most AI project failures trace back to data infrastructure that wasn't designed to support the AI workload built on top of it.
A data pipeline moves and transforms data — extracting it from source systems, applying transformations, and loading it into a destination. A data warehouse is the destination — structured storage designed for analytical queries and AI workloads, not operational transactions. Most data engineering projects require both: pipelines to move data reliably and a warehouse to store it in a format optimised for how it will be used.
We start with the retrieval task — what questions the RAG system needs to answer, what data it needs to retrieve, and what the acceptable latency and accuracy requirements are. From there we design the chunking strategy, the embedding model, the vector index structure, and the retrieval evaluation framework. The vector database choice (Pinecone, pgvector, Weaviate, Elasticsearch) follows from the scale, latency, and infrastructure requirements — not from a default preference.
We start with an audit — mapping the current pipeline, identifying the transformations, and profiling the data quality issues. From there we implement validation checks at the points where data quality problems are most likely to originate, add monitoring that alerts when quality degrades, and fix the root causes incrementally. We don't require a full rebuild to improve data quality in an existing pipeline.
Error handling, retry logic, idempotency, dead-letter queues for failed records, and monitoring that alerts on pipeline failures, processing delays, and data quality anomalies — before they compound into downstream problems. We treat pipeline reliability the same way we treat application reliability: as an engineering requirement built in from the start, not a property we hope for.
Yes — and it's the most common scenario. We assess what exists, identify the specific gaps that are limiting your AI or analytics workloads, and improve incrementally. New pipelines connect to existing systems. New data quality checks layer on top of existing transformations. The goal is a data layer your AI systems can trust — not a full infrastructure replacement that creates more risk than it resolves.