We turn fragmented enterprise data into governed, contextualized, and observable pipelines that models, assistants, agents, and operational workflows can safely use in production.
80%
of AI projects never reach production
Projects fail when data meaning and ontology are not clear, the required data is unavailable, or quality data infrastructure and platforms are simply not there. RAND reports that more than 80% of AI initiatives fail and identifies data availability and data infrastructure among the leading causes.
$547B
wasted on failed AI initiatives in 2025
Individual AI apps get deployed without clear impact measurements and often without measuring the impact itself. Systematic data deduplication, anomaly detection, and intelligent outlier management that preserves analytical value while eliminating noise are ignored, leading to disappointment with AI initiatives.
72%
of enterprise RAG implementations fail in year 1
RAG implementations built without enterprise-scale experience, permission management, domain-specific vector stores, hybrid search, and evaluation frameworks in place are likely to fail.
85%
of companies miss AI cost forecasts by >10%
Many businesses rely on their own R&D and do not account for the industry’s already accumulated experience in AI implementation planning and business-case design. As a result, they underestimate implementation costs and miss their forecasts. The use of AI co-financing initiatives across small and medium-sized firms remains low.
Our AI Center of Excellence runs a 2-day to 2-week diagnostic across use-case fit, data availability, ontology, architecture, governance, operations, and impact measurement, drawing on experience from 100+ client AI journeys in different industries.
You get: an executive-level readiness scorecard, target architecture, impact measurement framework, and prioritized engineering backlog.
Backed by findings from the previous stage, we take one or several AI cases into 360-degree production, onboarding data through ontology mining and enrichment, adjusting the AI initiative and data governance, and deploying models, tooling, agents, and data pipelines.
You get: a governed, monitored data pipeline serving a defined model, assistant, or agent.
Building on what worked, we standardize, repeat, and scale it into reusable data capabilities for further AI initiatives while complementing data with organizational context to reduce hallucinations, adding the necessary harness, and introducing scalable AI sovereignty.
You get: a scalable foundation that accelerates new initiatives and improves reliability across AI systems.
We create reusable pipeline templates, optimize the data serving layer, and establish AIOps for automated monitoring, deployment, and cost control. Through paired delivery and structured knowledge transfer, your team progressively takes ownership.
You get: an operational AI data foundation your internal team can manage, optimize, and extend independently.
Data exploration, mapping, ontology enrichment, and AI pipeline generation
We accelerate access to AI-ready data, automating discovery, source mapping, ontology mining, enrichment, and pipeline generation so models, assistants, and agents reach reliable, contextualized data faster.
Co-financing, platform expertise, and architecture advice
Through our partnerships with Databricks, Snowflake, AWS, and Microsoft, customers gain specialized platform expertise, technical enablement, and access to eligible co-financing or cloud-credit programs.
Confidential computing patterns for sensitive data and AI workloads
We protect sensitive data and models during processing through hardware-backed isolation, encryption in use, controlled model access, private inference, and auditable data flows.
Sovereign cloud, open-core, federated, and open-source data platforms
We keep data, models, infrastructure, and operations within required jurisdictions and under customer control, while reducing vendor dependency.
As a part of UpCloud, STACKIT, Open Telekom Cloud, and the Gaia-X ecosystem, we combine European cloud infrastructure with open-source and open-core platforms, customer-managed keys, federated architectures, and workload portability.
Sigma Software optimized a cloud platform used for processing and governance of extensive video and research datasets for large-scale naturalistic research project. The new solution streamlined datasets ingestion, improved parsing and labeling of collected video data, and ensured stronger monitoring, rollback processes, storage protection (incl. secure third-party data handling), and cross-region replication.
Sigma Software redesigned the data platform behind a global advertising solution after its acquisition by Verizon Media. The architecture our team delivered processes more than 2.5 million events per second while supporting more than 30,000 companies worldwide.
Sigma Software built a multi-agent architecture with approximately ten specialized agents operating under one orchestrator. AI recommendations, conversational insights, and anomaly detection are embedded into campaign workflows rather than delivered as standalone tools.
Sigma Software is helping a regulated enterprise move from fragmented telemetry to a governed data fabric for predictive maintenance, service diagnostics, and quality monitoring. We unified sensor signals, logs, calibration records, images, and environmental context through a semantic layer with active metadata, lineage, and governed real-time access.
Our capabilities span source integration, data engineering, semantics, AI and ML, cloud infrastructure, security, application integration, and production operations. You don’t have to coordinate separate data, AI, security, and software vendors around the same enterprise data dependency.
We work across Snowflake, Databricks, AWS, Azure, GCP, hybrid environments, and open technologies. Our team leverages this diversity to choose the architecture that best fits your data estate, AI consumers, regulatory obligations, and long-term platform strategy.
We build semantic models, domain context, and governed data products so that models and agents work with the language of the business rather than raw schemas and disconnected document collections.
We create a capability your organization can operate and extend. Our delivery approach includes observability, runbooks, shared repositories, paired engineering, and structured handover.
