Data & AI Enablement
Governed data. Practical Artificial Intelligence. Auditable outcomes.
Jiva Technologies designs, implements, integrates and supports cloud data platforms and Artificial Intelligence services — lakehouses, streaming and batch pipelines, semantic models, MLOps, Retrieval-Augmented Generation and Responsible AI guardrails — matched to real business use cases.
Lakehouse or cloud data warehouse, curated data products, semantic layer and catalog-managed metadata.
Model registry, deployment automation, monitoring and drift detection alongside application pipelines.
Retrieval-Augmented Generation, guardrails, human review and evaluation harnesses appropriate to each use case.
What we deliver
Data Lakehouse & Cloud Data Platforms
Cloud data platforms on Databricks, Snowflake, Microsoft Fabric and Google BigQuery — medallion architecture, dbt modeling, semantic layers and cost-aware storage tiers for analytics and Artificial Intelligence workloads.
Data Integration, ETL/ELT & Streaming
Batch and streaming pipelines using Apache Airflow, dbt, Apache Kafka and platform-native services — including Change Data Capture, real-time ingestion and validated data movement between source, lakehouse and downstream applications.
Semantic Models, Analytics & Business Intelligence
Governed semantic layers, curated data products and Power BI or platform-native analytics — consistent metrics reused across dashboards, embedded analytics and Artificial Intelligence use cases.
MLOps & Model Lifecycle Management
Feature stores, experiment tracking, model registry, deployment and monitoring on managed and open-source stacks — model versioning, drift detection and controlled promotion between environments.
Generative AI, RAG & Enterprise Search
Enterprise Generative AI grounded in Retrieval-Augmented Generation — embeddings, vector search, prompt management, guardrails and human review across managed model services on Azure OpenAI, Amazon Bedrock and Google Vertex AI.
Data Governance, Quality & Responsible AI
Data catalog, lineage, metadata management, data-quality rules, master-data alignment, Personally Identifiable Information classification, role-based access control and Responsible AI guardrails embedded in delivery.
Why teams call us
- Data spread across incompatible stores
Overlapping warehouses, extracts and shadow databases make it hard to trust the same metric across teams.
- Generative AI proof-of-concepts that never reach production
Prototypes bypass data quality, security, evaluation and cost review, and cannot be promoted safely to production.
- Analytics that operators cannot use
Dashboards exist in isolation from the systems where decisions are made, so recommendations rarely change behavior.
- No governance for sensitive data or model outputs
Classification, access control, lineage and evaluation are inconsistent, exposing the organization to compliance and reputational risk.
Our approach
A data and Artificial Intelligence lifecycle that pairs governed platform engineering with prioritized, evaluated use cases and ongoing operations.
- Assess
Data-estate discovery, use-case triage, data-quality baseline and Artificial Intelligence readiness across security, privacy, cost and operational risk.
- Design
Target data platform, integration patterns, semantic model, MLOps operating model and Responsible AI guardrails aligned to prioritized use cases.
- Implement
Build lakehouse, pipelines, analytics and Artificial Intelligence services incrementally, with each release validated against data-quality and evaluation criteria.
- Operate & Optimize
Ongoing platform support, cost management, model monitoring, drift response and continuous improvement of data products under the agreed scope.
End-to-End IT Integration and Support
From assessment and solution design through implementation, remediation, ongoing support, and lifecycle optimization.
Fix What's Broken — Remediation Sprints
A stalled project, a failed go-live, or an inherited design that isn't delivering the outcome? We run fixed-scope rescue and remediation engagements — defined scope, defined outcome, no open-ended hours.
- Architecture, design and delivery-plan review with corrective actions
- Integration, data and process remediation with test evidence
- Cutover, hypercare and knowledge-transfer for a clean handover
Support & Maintenance — AMC-Backed
Beyond go-live, retain us on tap — architecture guidance, enhancement backlog and lifecycle governance under a Jiva professional-services AMC.
- Retainer-based architecture, design authority and technical governance
- Enhancement backlog delivery with prioritisation and quarterly roadmaps
- Vendor coordination, upgrade planning and lifecycle refresh advisory
Continue exploring
Related services
- Application Modernization & APIs
Portfolio disposition, replatforming, microservices, cloud-native rebuilds and API-led integration to move legacy applications forward safely.
- DevOps & CI/CD Enablement
CI/CD pipelines, Infrastructure as Code, GitOps, container platforms, observability and DevSecOps to make software delivery repeatable and auditable.
- Low-Code, No-Code & Custom Development
Governed low-code delivery, citizen-developer enablement, workflow automation and full-stack custom web, mobile and SaaS engineering.
- Cloud Assessment & TCO
Workload discovery, dependency mapping, 6R disposition, landing-zone readiness and TCO / ROI modeling to build a defensible migration business case.
- Digital & Cloud Transformation
Coordinated cloud, application, data, DevOps, low-code and FinOps delivery under a single integration, implementation and support engagement.
Data & AI Enablement
Turn governed data into production-ready AI.
Request a data and AI workshop with Jiva Technologies. The session reviews the data estate, prioritizes use cases and outlines a phased plan across lakehouse, MLOps and Generative AI.
