Sigmoid helps enterprises modernize their data foundations, optimize engineering workflows, and deliver AI-ready data with speed and reliability. We embed autonomy, intelligence, and governance across your entire data lifecycle.
Data Engineering Services for the AI Era
Data Platform Modernization
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Automated ingestion and scalable data pipelines that reduce time-to-insights
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Cloud native migration and modernization from legacy/on-prem systems
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Multi-cloud and hybrid architectures designed for agility, real-time analytics, and enterprise-wide data availability
AI in Engineering Solutions
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End-to-end MLOps and LLMOps to operationalize ML, GenAI, and Agentic AI with governance-first design
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RAPID framework for AI governance enabling 2x faster time-to-market and 30–50% infrastructure cost savings
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Large-scale unstructured data processing with conversational data access bots for self-service information retrieval
AIOps Managed Services
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Intelligent monitoring and incident management powered by AI solutions
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Automated observability and 24x7 platform operations with built-in tools and accelerators that simplify data platform management
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FinOps-driven cost optimization and process efficiency for improved resource utilization and agility across internal operations.
Data Products
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Persona-based, role specific data products for faster adoption
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Built on modern architectures like data hubs, meshes, warehouses, and lakes
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Privacy-safe Data Clean Rooms (DCRs) enable secure data sharing with business partners, interoperability, and built-in governance
Data Engineering: Before vs After AI Agents
Before AI Agents
After AI Agents
Manual data ingestion and harmonization, with heavy reliance on data engineers for repetitive tasks
1
Flat files to structured data: Streamlined reporting automation reduces manual wrangling
Significant effort in cleansing and standardization across domains, leading to scalability issues
2
Dynamic cleansing and standardization: AI-driven scaling of data quality across domains
Metadata enrichment and cataloging are handled manually, slowing governance and discoverability
3
Automated metadata intelligence: Catalog enrichment accelerates governance and discoverability
Error detection and resolution are dependent on technical specialists, with longer turnaround times
4
Intelligent error classification and routing: Faster collaboration and resolution of issues
Limited ability to extract insights from unstructured data sources like PDFs, PPTs, and images
5
Natural language access: Democratized data usage, reduced dependency on tech teams
Access to data insights is bottlenecked by technical teams, restricting self-service
6
Self-healing pipelines: Automated recovery reduces downtime and boosts reliability
Manual data ingestion and harmonization with heavy reliance on engineers for repetitive tasks.
Significant effort in cleansing and standardization across domains, leading to scalability issues.
Metadata enrichment and cataloging handled manually, slowing governance and discoverability.
Error detection and resolution depend on technical specialists with longer turnaround times.
Limited ability to extract insights from PDFs, PPTs and other unstructured formats.
Access to insights bottlenecked by technical teams, restricting self-service capabilities.
Flat files to structured data: AI automates reporting & reduces manual wrangling.
Dynamic cleansing & standardization: Scalable data quality across domains.
Automated metadata intelligence: Faster governance and discoverability.
Intelligent error routing: Faster collaboration & quicker resolution.
Natural language access: Democratized data usage with minimal tech dependency.
Self-healing pipelines: Automated recovery boosts reliability.
Manual data ingestion and harmonization with heavy reliance on engineers for repetitive tasks.
Significant effort in cleansing and standardization across domains, leading to scalability issues.
Metadata enrichment and cataloging handled manually, slowing governance and discoverability.
Error detection and resolution depend on technical specialists with longer turnaround times.
Limited ability to extract insights from PDFs, PPTs and other unstructured formats.
Access to insights bottlenecked by technical teams, restricting self-service capabilities.
Flat files to structured data: AI automates reporting & reduces manual wrangling.
Dynamic cleansing & standardization: Scalable data quality across domains.
Automated metadata intelligence: Faster governance and discoverability.
Intelligent error routing: Faster collaboration & quicker resolution.
Natural language access: Democratized data usage with minimal tech dependency.
Self-healing pipelines: Automated recovery boosts reliability.
Why Choose Sigmoid
Proven Data Quality Expertise
Over a decade of delivering enterprise-scale, reliable, and governed data pipelines that ensure accuracy, consistency, compliance, and readiness for AI use cases.
AI-Powered Engineering Efficiency
Our data engineers harness AI to write, review, and optimize code with speed and accuracy. Vibe coding infuses creativity and intelligence into every engineering workflow, reducing development time.
Large Scale Data Processing
Enterprise-grade data processing that handles massive, complex, and distributed data workloads with consistency and precision. We enable real-time and batch processing at scale.
Ensure Responsible AI
Embed AI governance across the data engineering lifecycle to ensure compliance, transparency, and accountability. We deliver reliable, auditable, and ethical enterprise AI
AI-powered Data Engineering Accelerators
Pre-built frameworks and domain-specific solutions that accelerate data-to-value and AI adoption.
Success stories
70%
faster issue resolution in data operations with Agentic AIOps for a global consumer goods manufacturer
30%
quicker access to trusted data with a Centralized data products marketplace for health and wellness products.
90%
reduction in workload processing time streamlines finance data operations for a leading CPG company.
