Your Data Layer for AI Insights

Connect Claude Code, GitHub Copilot, Cursor, and more to your SDLC data so you can measure AI adoption, impact, and ROI across every team.
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Effortlessly answer any question with rich, trustworthy data.

What's my Token Cost by Project ?

Start from pre-built reports and industry benchmarks.

Where is AI contributing to your process?

See which commits, pull requests, reviews, and CI/CD activity involve AI with minware’s AI Adoption and Performance report.

Where are bugs coming from?

Discover the source of quality issues and risk using minware’s Bug Creation Dashboard.

Is AI helping us deliver value faster?

Pinpoint where AI integration may be creating new bottlenecks on minware's Efficiency Dashboard.
Browse all pre-built reports

Create your own insights with minware's AI data models.

Create your own insights with minware's AI data models.

minware's rules-based, machine learning (ML), and large language (LLM) data models make it easy to build advanced insights without having to start from scratch.
Our models cover text classification (prompts, tickets, reviews, tool commands, etc.), cost attribution, historical timeline reconstruction, identity resolution, and more.
Easily extend or customize any model for your organization with Snowflake Cortex AI.

View and customize any data lineage step with minQL.

View and customize any data lineage step with minQL.

Easily configure metrics to fit how your organization works – no process changes required.
minware's semantic layer is fully transparent and customizable with minQL. The minQL query language offers the expressivity of Snowflake database functions in a concise syntax that simplifies joins, aggregation, and complex logic.
Learn about customizing minQL

Save your tokens and leave the rest to us.

Get trustworthy insights faster with our data infrastructure.
Agents are great at a lot of things – enterprise-grade data quality, performance, and security are not among them.

Automated Caching & Orchestration

The minQL engine dynamically materializes tables and orchestrates updates so your results are always fresh and fast for millions of rows.

Standard Data Modeling & Benchmarks

minware provides pre-built models for common use cases like time allocation, cost attribution, DORA workflow efficiency, Agile and more.

Vendor Data Linking & Normalization

minware normalizes and links AI usage, pull requests, tickets, and more with advanced heuristics for unstructured relationships.

Error Handling & Recovery

minware handles errors caused by vendor API issues, schema updates, data anomalies, and more so your data is available 24/7.

Identity Resolution

minware reconciles names, emails, and user IDs across all vendors and matches them to multi-level team structure from any source.

Role-Based Access Control

Easily control team and individual data access permissions for minware users based on their role and team membership.

On-Premise Ingest Agent

For added security, the on-premise ingest agent runs locally and uploads data to minware. You maintain full control over keys and data.

AICPA SOC 2 Type 2 Certified

minware is SOC 2 Type 2 compliant. Learn more and access the full SOC 2 report from our security documentation.

Frequently Asked Questions

What AI coding tool integrations does minware offer?

minware connects usage and prompt data from GitHub Copilot, Cursor, and Claude Code. We are adding more tools all the time!

What makes minware better than native AI tool reports?

Native dashboards for GitHub Copilot, Cursor, and Claude Code show usage in isolation. minware connects that data to your Git, ticketing, and CI/CD activity so you can see whether AI tool adoption is actually moving delivery metrics like cycle time and throughput.

Can minware compare adoption and usage patterns across multiple AI coding tools?

Yes. When teams use GitHub Copilot, Cursor, and Claude Code in parallel, each product exposes different dashboards and metric definitions. minware normalizes usage data across all three so engineering leaders can compare adoption rates and delivery patterns in one place.

How does minware measure the productivity impact of AI coding tools?

minware compares delivery outcomes before and after AI tool adoption, and can also compare AI-assisted work against non-AI work from the same teams during the same period. Because the analysis uses your own engineering data as the baseline, the results reflect your team's improvement rather than an industry average.

How does minware measure the ROI of AI investments?

minware connects token usage and spend to all your relevant data points so you can direcly evaluate AI investment ROI. You can see the cost associated with tickets, projects, and PRs to see the ROI of deliverables. You can also look at tokens and dollar cost over time and by team, as well as compare cost per AI tool and AI model.

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