Perimattic builds scalable AI data pipelines that ingest, transform, validate, and deliver production-quality data to your machine learning models — handling batch and streaming workloads at enterprise scale with built-in data quality checks.
Pipeline Tools and Frameworks We Build On — Apache Airflow, dbt, Kafka, Spark, Snowflake, Databricks
An AI data pipeline is an automated workflow that collects raw data from multiple sources, cleans and transforms it, engineers features, and delivers it in the format machine learning models need for training and inference. Unlike traditional ETL pipelines built for business intelligence reporting, AI pipelines handle unstructured data, maintain feature stores for consistent training-serving alignment, and support both batch and real-time streaming workloads.
The practical implication is direct: a model is only as accurate as the data it trains on. A pipeline that introduces silent data quality issues — schema drift, null value spikes, distribution shifts, or feature computation inconsistencies between training and serving — will degrade model performance in ways that are expensive to diagnose and slow to fix. Most production ML failures trace back not to the model itself, but to the data arriving at it.
Perimattic builds AI data pipelines that treat data quality as a first-class engineering concern. Automated validation gates, lineage tracking, feature store integration, and observability are built into every pipeline from the first sprint — not retrofitted after a production incident forces the issue.
Processing model
Batch-only, nightly scheduled jobs
Processing model
Unified batch and real-time streaming
Data quality
Manual checks, often discovered in production
Data quality
Automated validation gates at every stage
Scalability
Breaks under volume spikes, requires rearchitecting
Scalability
Elastic horizontal scaling built in from day one
Feature reuse
Feature logic rebuilt per model, prone to skew
Feature reuse
Centralised feature store, shared across teams
Observability
Failures discovered by downstream users or models
Observability
Full lineage tracking and proactive alerting
The distinction matters most in production ML systems where silent data quality issues compound over time. AI data pipelines treat quality enforcement as infrastructure, not an afterthought.
Seven specialist service lines covering the full AI data pipeline lifecycle.
Connect to databases, APIs, event streams, file systems, and third-party SaaS tools with reliable, fault-tolerant connectors that handle schema evolution, authentication, and rate-limiting automatically.
Build Apache Kafka and Apache Flink streaming pipelines that process events with sub-second latency, supporting complex event patterns, windowing, and stateful computation across high-throughput workloads.
Automated feature extraction, transformation, and storage in a centralised feature store so the same feature definitions are used consistently across training and serving — eliminating training-serving skew at the root.
Automated data quality gates that catch schema changes, null rate anomalies, distribution drift, and referential integrity violations before they corrupt model training or degrade inference quality.
Distributed batch processing with Apache Spark, Databricks, and Delta Lake that handles terabyte-scale transformations reliably, with incremental processing patterns to minimise compute costs on large datasets.
End-to-end pipelines that assemble, version, and deliver training datasets for machine learning experiments, with dataset versioning so every model training run is fully reproducible and auditable.
Full pipeline observability with Apache Airflow or Prefect for orchestration, lineage tracking, SLA monitoring, and failure alerting so data quality issues are caught before they affect model performance.
A structured six-stage process from free data audit to live deployment and ongoing pipeline optimisation.
We map your existing data sources, understand your model requirements, and identify data quality issues before we write a line of code. You leave with a clear picture of what pipeline architecture you need and why.
We design the pipeline architecture: ingestion patterns, transformation logic, feature store strategy, quality gate checkpoints, and orchestration approach. We select the right stack for your scale and cloud environment.
We build a working pipeline against a real slice of your data. The PoC surfaces data quality issues, schema inconsistencies, and integration complications before the production build begins — saving significant rework.
We build the full production pipeline, connecting to all your data sources, implementing transformation logic, and integrating with your data warehouse or lakehouse. Quality gates and lineage tracking are built in from the start.
We test the pipeline end-to-end against production volumes, validate output quality against agreed schemas and statistical bounds, and stress-test under peak load. We establish data quality baselines from day one.
We deploy the pipeline to your infrastructure and hand over observability tooling. We monitor pipeline health, data freshness, and quality metrics in the first weeks, resolve any issues, and plan the next pipeline iteration.
We map your existing data sources, understand your model requirements, and identify data quality issues before we write a line of code. You leave with a clear picture of what pipeline architecture you need and why.
We design the pipeline architecture: ingestion patterns, transformation logic, feature store strategy, quality gate checkpoints, and orchestration approach. We select the right stack for your scale and cloud environment.
We build a working pipeline against a real slice of your data. The PoC surfaces data quality issues, schema inconsistencies, and integration complications before the production build begins — saving significant rework.
We build the full production pipeline, connecting to all your data sources, implementing transformation logic, and integrating with your data warehouse or lakehouse. Quality gates and lineage tracking are built in from the start.
We test the pipeline end-to-end against production volumes, validate output quality against agreed schemas and statistical bounds, and stress-test under peak load. We establish data quality baselines from day one.
We deploy the pipeline to your infrastructure and hand over observability tooling. We monitor pipeline health, data freshness, and quality metrics in the first weeks, resolve any issues, and plan the next pipeline iteration.
Select an industry to see how purpose-built data pipelines improve model quality and reduce data engineering overhead in that domain.
Financial institutions process terabytes of transaction, market, and customer data daily — and models for fraud detection, credit risk, and trading require clean, versioned, low-latency pipelines to perform reliably in production.
Financial institutions process terabytes of transaction, market, and customer data daily — and models for fraud detection, credit risk, and trading require clean, versioned, low-latency pipelines to perform reliably in production.
Healthcare AI pipelines must handle unstructured clinical data, enforce strict data governance, and maintain HIPAA-compliant lineage from raw source through to model prediction — all without compromising patient privacy.
Recommendation engines, dynamic pricing models, and demand forecasting systems require continuous, high-frequency data pipelines that keep features fresh as customer behaviour and catalogue data change.
Predictive maintenance and quality inspection models rely on high-frequency sensor data streams that must be ingested, cleaned, and aggregated in near-real-time before machine degradation causes costly downtime.
Software companies running LLMs, ranking models, and behavioural analytics at scale need multi-source data pipelines with real-time feature serving, cost-aware compute, and full observability across every stage.
Logistics AI systems — route optimisation, demand sensing, and carrier performance models — depend on pipelines that unify fragmented data from carriers, warehouses, and ERP systems into consistent, model-ready features.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy → Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate production-grade pipeline engineering from BI-retrofit approaches.
We design data pipelines specifically for ML workloads from day one — with feature versioning, training-serving consistency, and data quality validation built into the architecture, not added later as patches.
Every pipeline we build includes automated quality gates at every stage. Schema conformance, null rates, statistical distribution bounds, and referential integrity are checked continuously — not discovered during model debugging.
We own the full pipeline from raw data ingestion through feature engineering to model-ready output. No split responsibility between data engineers and ML engineers — one team, one accountability.
We select compute, storage, and orchestration tools based on your data volumes, latency requirements, and existing cloud investment — not our platform preferences. We actively optimise for cost as part of the build.
“Unlike teams that treat data pipelines as plumbing after the fact, Perimattic builds data quality enforcement and feature consistency into the architecture from day one — so your models get the data they were promised, not the data that survived the journey.”
An AI data pipeline is an automated workflow that collects raw data from multiple sources, applies transformation and validation logic, engineers features, and delivers clean, structured data to machine learning models for training and inference. Unlike traditional ETL pipelines built for business intelligence reporting, AI pipelines handle unstructured data, maintain consistent feature definitions across training and serving environments, and support both batch and real-time streaming workloads.
Traditional ETL pipelines move data from source systems to a data warehouse for reporting and SQL querying. AI data pipelines go further: they engineer ML features, maintain a feature store for training-serving consistency, handle real-time event streams, run automated data quality checks at every stage, and version datasets so every model training run is fully reproducible. A data pipeline that is adequate for BI reporting will introduce silent errors and training-serving skew when used to feed machine learning models.
A focused single-source pipeline with basic feature engineering typically takes three to six weeks. A complex multi-source pipeline with real-time streaming, a feature store, and automated quality gates typically takes eight to sixteen weeks. Timelines depend on the number of source systems, the complexity of transformation logic, and the integration work required. We provide a more accurate estimate after reviewing your data architecture on the free scoping call.
Our stack is selected based on your requirements, not our defaults. For orchestration we use Apache Airflow, Prefect, and Dagster. For processing engines we work with Apache Spark, Apache Flink, Databricks, and Delta Lake. For transformation we use dbt and custom Python. For storage we work with Snowflake, BigQuery, PostgreSQL, ClickHouse, and Apache Iceberg. For streaming we use Apache Kafka and Apache Flink. For cloud infrastructure we deploy on AWS, Azure, and GCP using Docker, Kubernetes, and Terraform.
A feature store is a centralised repository where computed ML features are stored and served consistently for both training and inference. Without one, teams often compute the same feature differently in their training code and serving code — a problem known as training-serving skew that silently degrades model accuracy in production. If you have more than one ML model, are deploying to production, or have multiple data scientists working on features, a feature store will prevent significant quality and maintenance problems.
We build streaming pipelines using Apache Kafka as the event transport layer and Apache Flink or Spark Structured Streaming for stateful stream processing. We handle windowing, late event arrival, exactly-once semantics, and backpressure management as part of the core architecture. Real-time feature computation is stored to a low-latency feature store so models can retrieve fresh features at inference time without adding pipeline latency.
Every pipeline we build includes automated data quality gates at each stage. We use Great Expectations, dbt tests, and custom validation logic to check schema conformance, null rates, referential integrity, and statistical distribution bounds. When a check fails, the pipeline raises an alert and optionally halts to prevent corrupted data from reaching models. We also instrument pipelines with lineage tracking so you can trace exactly which source data contributed to any model prediction.
Yes. We have experience migrating legacy ETL scripts, on-premises SQL Server pipelines, and custom Python data workflows to modern cloud-native architectures on AWS, Azure, and GCP. We conduct a data architecture audit first, identify the highest-risk migration steps, and build the new pipeline in parallel with the existing one before cutover. We do not perform big-bang migrations that risk production disruption.
We have built data pipelines for clients in financial services (fraud detection, credit risk, market data), healthcare (clinical records, drug discovery), e-commerce (recommendation engines, demand forecasting), manufacturing (sensor data, predictive maintenance), SaaS (product analytics, LLM training data), and logistics (shipment tracking, carrier performance). Our pipeline architecture is designed to meet the data sensitivity and compliance requirements common across these sectors.
Costs depend on the number of data sources, the complexity of transformation and feature engineering logic, whether real-time streaming is required, and the cloud infrastructure involved. A focused single-source pipeline starts from approximately USD 12,000. A complex multi-source pipeline with real-time streaming and a feature store is typically USD 35,000 to USD 90,000. We scope every engagement before quoting so there are no surprises.
Tell us about your data sources, model requirements, and current pipeline challenges. We will show you exactly where clean, reliable data will improve model performance and reduce the engineering overhead consuming your team.