AI turns the data layer into a runtime dependency

Once AI moves into production, the data layer has to support more than access and availability. It has to handle changing source systems, shifting schemas, shared feature logic, runtime access, audit evidence, and governance across every system that consumes the data.

Sigma Software builds these requirements directly into the data layer, ensuring its fit for:

Sustaining AI performance in production

Ingestion and validation set up to catch data quality issues before they reach the model

Continuous monitoring and alerting implemented for schema changes, distribution shifts, and quality regressions

Data contracts enforced at the boundary between systems

Preventing drift between training and serving

Identical feature definitions and computation logic deployed across training, development, and production

Point-in-time correct joins implemented to prevent training data leakage

Drift monitoring (PSI, KS, Wasserstein) configured to catch distribution changes before accuracy degrades

Generating audit evidence from the pipeline itself

Column-level lineage tracked from source data to AI output, structured for EU AI Act, DORA, and Basel audit

Access controls and audit logging built into the data layer

PII detection and tokenization applied at ingestion

Data minimisation and purpose limitation enforced for AI training and inference

Designing the data layer for reuse across initiatives

Reusable pipelines, feature definitions, contracts, and validation patterns built once and reused across initiatives

Data products built and published as discoverable services for new consumers

Governance patterns built to be reused by the audit team, not rebuilt per use case

Different AI consumers put different demands on the data layer. We serve all four

The model making decisions on your data

Production models need current data, shared feature logic, and monitoring as source systems change.

We help you to:

  • Build real-time and event-driven ingestion pipelines on Kafka, Flink, RedPanda, or hyperscaler-native streaming services
  • Serve training and inference from shared feature stores (Databricks, SageMaker, Feast, Tecton), with sub-10ms online access
  • Apply schema validation and quality gates at ingestion (Great Expectations, dbt tests)
  • Implement point-in-time correct joins to prevent training data leakage
  • Monitor drift (PSI, KS, Wasserstein) and trigger retraining at configurable thresholds
  • Add lineage and audit logs from data input to model output (Unity Catalog, Purview, OpenLineage)

The assistant or app answering questions on your content

LLM assistants depend on how content is prepared, retrieved, ranked, and governed.

We help you to:

  • Design retrieval architecture by content type, on dedicated vector stores (Pinecone, Weaviate) or hyperscaler-native vector search (OpenSearch, AI Search, Vertex AI)
  • Combine vector search, keyword search, and reranking
  • Prepare documents with semantic chunking and appropriate overlap
  • Build document-level access controls into the index from day one
  • Add a semantic layer (dbt Semantic Layer, Cube, AtScale) so the LLM queries from defined business metrics
  • Measure RAG quality across faithfulness, relevance, and precision (RAGAS)

The agent acting across your systems

AI agents need scoped access, current context, and a traceable record of what they read, call, change, or trigger.

We help you to:

  • Build and publish data products as discoverable services through the Model Context Protocol (MCP)
  • Enforce permissions by agent, data source, API, and operation
  • Set up audit logging for tool invocations and data access events
  • Encode entity relationships in knowledge graphs (Neo4j, Neptune, RDF) for semantic context
  • Design event-driven architecture to refresh agent context within seconds

The workflows triggered by data events

Some signals need to open tickets, route cases, trigger alerts, or start operational workflows.

We help you to:

  • Build event detection and routing inside the pipeline
  • Connect data signals to orchestration tools (n8n, Temporal) and operational systems
  • Add human-in-the-loop gates where decisions need review
  • Establish traceability from data event to business outcome

Tell us what your AI needs to do. We’ll help identify what the data layer has to support.

Sigma Software engagement models for production AI data systems

Most engagements begin with one of four entry points, depending on how clearly the problem is defined and how much of the architecture is already in place.

Data readiness assessment

Goal: identify which parts of the data layer are blocking production AI, ranked by impact and effort.

Sigma Software team reviews pipelines, feature logic, monitoring, and governance against what production AI requires.

Output: a readiness scorecard and a 90-day engineering backlog.

Best fit: the AI initiative is stalled, degrading, or hard to scale, and the team needs to know what to fix.

AI data pipeline sprint

Goal: get one priority AI use case running on a production-ready data pipeline.

We build the data pipeline behind it: ingestion, transformation, quality gates, lineage, and delivery into the model or assistant layer.

We use proprietary data onboarding solution to accelerate data onboarding where it fits, support sovereign or confidential deployment options, and co-finance engagements through Databricks, Snowflake, and AWS partnerships where applicable.

Output: a monitored pipeline in your environment, with the AI use case consuming it in production.

Best fit: the AI use case is defined, and you need the production pipeline behind it.

AI data operations

Goal: keep production AI stable after launch.

Our team works on drift detection, automated retraining, schema evolution, data contract enforcement, cost attribution, and observability. This is the operating layer that keeps AI stable between releases.

Output: monitoring, controls, and operating practices for production AI data systems.

Best fit: AI is in production, and the team needs to keep accuracy, cost, and governance under control.

Delivering AI in production, on enterprise data and at enterprise scale

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Driving AI transformation across advertising operations for a global digital publisher

  • Multi-agent architecture with ~10 specialized agents under one orchestrator
  • Up to 40% improvement in campaign performance and analytical insights
  • AI recommendations, chatbot insights, and anomaly detection embedded in daily operations

The Sigma Software team supported the Client in embedding agentic AI workflows into day-to-day advertising operations.

This included AI-driven recommendations at the input and output stages of campaign processes, an insights chatbot interface, and automated anomaly detection to identify data inconsistencies and performance deviations.

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AI-ready data fabric for predictive fleet intelligence

  • 10,000+ connected devices unified through a governed data layer
  • 20–40% faster root-cause diagnostics
  • 10–25% fewer unplanned service events expected

Sigma Software is helping a regulated enterprise client move from fragmented telemetry to a governed, AI-ready data fabric for predictive service and quality monitoring.

The solution unifies sensor signals, logs, calibration records, image data, and environmental context into a semantic layer with active metadata, lineage, and governed real-time access.

This creates the foundation for earlier issue detection, predictive maintenance, AI-assisted triage, and proactive service planning.

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Multimodal research data platform for large-scale video analysis

  • 100 years’ worth of video data streamlined for analysis
  • 300% faster historical processing for one core algorithm
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  • 50% lower storage costs

Sigma Software helped optimize a cloud-based data platform for large-scale naturalistic research data.

The work covered faster ingestion of diverse datasets, parsing and labeling of collected video data, secure management of third-party service data, stronger monitoring, rollback processes, cross-region replication, and storage protection.

View Case Study
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Near real-time analytics platform for high-volume event processing

  • 2.5M+ events processed per second
  • Used by 30K+ companies globally
  • Data latency reduced from 2 hours to 5 minutes

Sigma Software helped redesign the AWS-based Big Data platform and reporting layer behind a video advertising solution after its acquisition by VerizonMedia. The work focused on reducing latency, handling sharply increased data volumes, and keeping reporting services available under enterprise-scale load.

For this page, the relevance is the data-layer pattern: high-volume ingestion, near real-time processing, monitoring, alerting, and architecture that can support systems consuming live operational data.

View Case Study

Why teams choose Sigma over other service providers

No platform reseller bias, and we work with the data estate that exists

We deliver on Databricks, Snowflake, AWS, Azure, and GCP. We hold no exclusive partnership that pays us to recommend one over another. Most data estates do not need replacing. They need extension and correction. Our default is to work inside the existing architecture, not to propose migration as a precondition.

Platform engineering built for real production scale

We engineer end-to-end data platforms across hyperscalers and market-leading stacks, with proven delivery under extreme throughput, low-latency, and multi-region operational load.

Done means in production, with your team operating it

A pipeline is not finished when it runs in staging. It is finished when it runs in production under load, with monitoring in place, and when the client team can operate and extend it. Engagements are designed for that handover, with paired delivery, shared repositories, and documented runbooks.

Hosting where the data needs to be

We deliver on hyperscaler infrastructure, on confidential compute through NEAR and NVIDIA, and on EU sovereign cloud through UpCloud, STACKIT, T-Systems Open Telekom Cloud, and Gaia-X-aligned environments. Same engineering quality, different jurisdictional footprint, including options outside the US Cloud Act scope where regulation requires it.

Co-financing through platform partnerships

For qualifying engagements, implementation cost may be partly co-financed through our Databricks, Snowflake, and AWS partnerships. This reflects partnership tier, not a marketing claim, and it is not standard across the competitive set.

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The same data problems show up differently by industry

In financial services, the AI use cases are funded and the regulatory deadlines are real. Fraud detection runs on features that are hours old. Credit decisions cannot be explained to a regulator. Compliance teams produce manual evidence for AI systems that should have audit trails inside the pipeline.

We do:
  • Real-time feature pipelines for fraud, AML, and credit risk
  • Pipeline-level lineage to source data, column by column
  • Access controls and audit logging that meet DORA, EU AI Act, and Basel as engineering output
  • Model governance evidence the risk team can present, not assemble
  • Deployment on EU sovereign cloud where data residency requires it

In manufacturing, the AI use cases sit closer to operations than to IT. Sensor data feeds models that predict equipment failure, but features are computed in batch and arrive twelve hours late. The prediction lands after the failure, not before it.

We do:
  • Real-time streaming feature pipelines from sensor and IoT data
  • Consistent feature engineering across training and production
  • Quality gates and anomaly detection in the data feed, not only on model output
  • Edge-to-cloud data paths for plant-level inference
  • Deployment on EU sovereign cloud for defence-adjacent OEMs and regulated manufacturers

In logistics, the AI predictions are only as good as the data feeding them. Supply chain visibility models run on last night’s data. ERP, TMS, and WMS disagree on shipment status. Real-time exception management is impossible when the data layer settles overnight.

We do:
  • Order, shipment, and warehouse event streams unified into consistent pipelines
  • Real-time exception monitoring and routing decisions
  • Connections from the data layer into downstream operational workflows
  • Inventory, route, and fulfilment data products that survive partner system changes
  • Cross-system reconciliation between ERP, TMS, and WMS at event level

In telecom, the network produces billions of events per day. The AI model sees a sampled, batch-processed version, hours old. Real-time anomaly detection requires a pipeline architecture different from the one feeding analytics, and the BSS/OSS data underneath needs modernisation before AI-native operations are possible.

We do:
  • Streaming feature pipelines for network telemetry on Kafka, Spark Streaming, and Flink
  • Anomaly detection in the data feed, not only on model output
  • Consistent data products across BSS and OSS
  • Network, service, and customer context joined for live operational use cases
  • Pipeline architecture that supports billions of events per day at production latency

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Broader Sigma Software capabilities around AI-ready data

Data Engineering Services

Not every data challenge starts with AI. For broader platform modernization, pipeline development, analytics enablement, and reusable data products, explore Sigma Software’s wider data engineering services.

Cloud & Infrastructure

Design and operate cloud environments that support production AI workloads, secure data access, and scalable data processing.

Regulatory Compliance Consulting

Turn regulatory requirements into engineering controls across data, AI, cloud, and software systems.
Sigma Software has offices in multiple locations in Europe, Middle East, Northern and Latin America