AI DLC for Data & Analytics
Intellify Solutions · Software Development
Certification per Microsoft Marketplace.
Evidence tier Source Confirmed · 2 captures on record
What the publisher says
As described on Microsoft Marketplace.
Solution Overview
Intellify’s AI-DLC Accelerator helps enterprises move from isolated AI experiments to a governed, repeatable AI delivery model for Microsoft Fabric, Azure Databricks, and modern data platforms.
Show the rest of the publisher’s description (30 more lines)
It brings discovery, architecture, data engineering, QA, deployment, analytics, and DataOps into one coordinated lifecycle—powered by specialized AI agents, reusable accelerators, secure MCP integrations, and human validation. This enables organizations to accelerate selected delivery activities by up to 7×, achieve up to 70% faster end-to-end delivery, and reduce repetitive engineering effort by up to 60%.
AI-DLC is designed for enterprise control from day one. Every AI-assisted workflow operates within defined security, evaluation, audit, and approval controls, providing 100% governed workflow coverage across the delivery lifecycle. By capturing reusable assets, metadata, code, tests, decisions, and operational learnings in a continuously evolving Knowledge Book, the framework helps teams improve delivery speed, consistency, quality, and scalability over time.
Solution Highlights
- Up to 7× Faster Execution: Accelerate repeatable activities such as data discovery, requirements creation, code generation, testing, documentation, and issue triage through specialized AI agents and reusable accelerators.
- Up to 70% Faster Delivery: Reduce manual handoffs, repetitive work, rework, and late-stage defects across the end-to-end data and analytics delivery lifecycle.
- Up to 60% Less Engineering Effort: Automate and standardize repetitive engineering, QA, documentation, deployment, and maintenance activities so teams can focus on higher-value business outcomes.
- 100% Governed AI Workflows: Apply defined policy checks, security controls, evaluation criteria, audit trails, and human approval gates to every AI-assisted workflow before production deployment.
- Specialized AI Agents Across the Lifecycle: Purpose-built agents support discovery, architecture, data engineering, QA, DevOps, analytics, and DataOps using the right context, tools, and controls for each task.
- Reusable Enterprise Accelerators: Pre-built templates, patterns, code assets, test suites, and delivery playbooks improve consistency and reduce the time required to deliver common data and analytics use cases.
- Secure MCP and Enterprise Tool Integration: Connect AI agents securely with approved enterprise systems, data platforms, repositories, APIs, CLIs, testing tools, and deployment environments.
- Human Validation for Critical Decisions: Keep experts in control by requiring review and approval of high-impact requirements, designs, code, test outcomes, deployment decisions, and remediation actions.
- Continuous Learning Through a Knowledge Book: Retain requirements, metadata, lineage, code, tests, decisions, incidents, fixes, and operational learnings to make every future initiative faster and more informed.
- Flexible, Enterprise-Ready Configuration: Configure models, prompts, tools, accelerators, evaluation rules, and governance controls independently for each lifecycle phase—without redesigning the full framework.
Capabilities
- AI-powered discovery, profiling, and requirements generation
- Automated data engineering for pipelines, SQL, Python, PySpark, and semantic models
- Continuous testing for data quality, code, pipelines, reports, and performance
- CI/CD, deployment validation, rollback, and environment automation
- AI-assisted analytics, KPI logic, dashboards, and reporting
- Production monitoring, incident triage, root-cause analysis, and remediation
- Secure MCP integration with enterprise systems and developer tools
- Central agent orchestration, model routing, governance, and human approvals
- Continuous Knowledge Book for reusable assets, lineage, standards, and learningsBenefits
- Up to 7× faster execution of repeatable delivery tasks
- Up to 70% faster end-to-end data and analytics delivery
- Up to 60% less repetitive engineering effort
- 100% governed AI workflow coverage with policy checks, auditability, and human validation
- Higher quality through continuous testing and earlier defect detection
- Greater consistency through reusable accelerators and standardized delivery patterns
- Faster production support, maintenance, and controlled scaling across teams
Preview
5 images
Agent build and provenance
See the full provenance
The layer-by-layer build, the evidence behind each claim, the risk basis and the cross-marketplace links are open to any account. Some rows are disclosed, some the source leaves Unknown; a free account shows you which.
Compliance
- FedRAMPConfirmedNot listed90%, registry-checkedNo FedRAMP Marketplace entry matched this vendor's domain, checked 2026-08-27registry recordas observed 2026-08-27
Confirmed means matched to a public authoritative registry. Claimed means the vendor or its listing states it, not yet cross-checked. A framework not shown was not found in any source we hold, which is not evidence against it. Not listed means a scoped registry check found no match for this vendor's domain: a No is a scoped registry check, not a compliance judgment. Confidence bands: 95% domain-verified, 90% registry-checked, 80% self-attested, 70% weak signal. Self-attested items marked “vendor's site” are gathered from the vendor's own website and are not verified by us.
Vendor
External enrichment
Sources
Publisher resources
1 linkLinked repositories
Unknown means this listing does not publish a repository. It is not a statement that the code is closed, and a linked repository is not a claim that the publisher wrote it: the registry computes that relationship privately and does not publish it.
Evidence risk is the share of the build you cannot see before you deploy, not a security rating. Sign in to see the layer-by-layer basis for this band.