Deliver more value with every token

minware is the engineering productivity platform that gives you ready-to-work actions ranked by impact to eliminate wasted tokens and human time.
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minware AI Efficiency dashboard: token spend and engineering time split into value and waste, with recommended actions ranked by priority

minware · AI Efficiency

Chart: Token Spend Overhead History, by Value, Interventions, Tool Waste, Context Bloat, from Jul to Dec.

Chart: Engineering Time Overhead History, by Value, Interventions, Tool Waste, from Jul to Dec.

Recommended Actions & Estimated Savings
TypeActionEst. Token $ / yEst. Time / yPriority
InterventionsDocument the staging database setup in AGENTS.md$45.6k504hHigh
Tool WasteReplace full-repo grep with a scoped code search tool$34.8k216hHigh
InterventionsRun lint and type checks before agents open PRs$19.2k312hMedium
Tool WasteCache dependency installs in agent sandboxes$10.8k372hMedium
Context BloatRemove unused MCP servers from the default config$25.2kLow

See the evidence behind each action.

minware’s agent reads each session with full context from tickets, PRs, and more. It identifies concrete actions with examples, historical cost, and estimated savings.
A Human Intervention Report finding: agents never see the ticket description, with the action, its estimated savings, its cost by month, and an example session

minware · Human Interventions

Agent doesn’t see ticket description

  • Est. token savings: $8,400 / y
  • Est. human time savings: 170h / y

Explanation: People copy details from a ticket later in a session, resulting in rework.

Action: Connect the Linear MCP server and add a CLAUDE.md instruction to read the current ticket first.

Chart: Historical Cost, by Tokens ($), Hours ($100/h), from May to Oct.

Example 1: Session 3fa2c1d0
EventDetail
Promptadd CSV export to the invoices page
Agent[18 Read, Grep and Edit calls; adds a button that exports every invoice]
Interventiononly export the rows that match the filters Cause: Repeat of acceptance criteria listed in ENG-2481
Agent Rework[Reworks the export to pass the active filters to the query] Cost: $3.80 in tokens and 12 min

Uncover every important action.

minware’s agent doesn’t rely on preset checks. It investigates with full context to discover actionable issues, then builds AI classifiers to measure their impact, just like a member of your team.
The Analysis Agent repeats four steps, measure, segment, investigate and classify, until every cause is explained

Analysis Agent

  1. Measure: Activity with no known cause
  2. Segment: Slice by every dimension
  3. Investigate: Read the full context
  4. Classify: Build a classifier for the cause

Investigate and classify activity until every important action is found.

Discover actions across the whole development lifecycle.

minware’s agent analyzes metrics with full context to find actions across the SDLC, from AI adoption and ROI to delivery, quality, and finance.

AI Developer Productivity

Cut wasted tokens and the time engineers spend babysitting agents.
Example actionDocument the staging DB setup in AGENTS.md

AI Adoption

See which teams and practices get value from AI tools, and where they’re stuck.
Example actionShare one team’s agent setup with three teams that are lagging

AI ROI

Tie AI spend to delivered work and cut spend that’s not driving value.
Example actionMove routine review agents to a cheaper model

DORA Metrics

Find what’s slowing reviews and deploys, and what’s causing failures.
Example actionAdd new code review agent for first-pass review

Agile Metrics

Spot scope creep, carryover, and planning gaps before they derail sprints.
Example actionAdd agent to check for underestimated tickets

Cost Capitalization

Automate capitalization reports and identify actions that decrease non-capitalizable overhead.
Example actionCreate AI workflow for bug triage

Analyze every session without wasting tokens.

minware’s agent runs on the patented minQL data engine, which links every session, PR, and ticket, letting the agent query any metric by any dimension. The agent reads relevant context at a fraction of the cost vs. a do-it-yourself setup.
Git, tickets, and agents linked into one model, read by the minware agent, which produces actions and metrics
  1. Development Process: Git (API), Tickets (API), and Agents (OTel)
  2. minQL Data Engine
  3. minware Agent, reading Prompts, Tools, PRs, and Tickets
  4. Ranked Actions and Metrics
  5. Improve & Repeat: back to Development Process

Already Linked

Sessions, PRs, tickets, and people are joined and resolved up front, so the agent gets full context in one query.

Reads Only What Matters

minQL computes metrics and finds the relevant slice first, so the model only spends tokens where the signal is.

Classifies Every Event

System One AI, machine learning, and conventional classifiers process every event and stay fresh as new data arrives.

minware integrates with every major development tool.

minware reads Claude Code and Codex sessions through OpenTelemetry and Cursor and GitHub Copilot usage through their APIs. It also supports custom CSV/JSON and OpenTelemetry data for spreadsheets and internal tools.
See the full list of integrations

Built for enterprise scale and security.

We designed minware from the ground up for Fortune 500 customers to handle homegrown systems, fragmented tools, large orgs, and secure environments.

Performance at Scale

The minQL data engine handles caching and materialization so reports stay fast for organizations of 1,000+ people.

Identity Resolution

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

Custom Integrations

Connect custom data with file uploads or OpenTelemetry, then map it into the data model with minQL to support custom and in-house systems.

Data Residency & AI Training

Custom data residency is available for enterprise customers. minware never uses your data to train AI models for other organizations; models built on it serve only yours.

Role-Based Access Control

Control team and individual access to reports based on role and team membership. The agent can limit identifying information in reports, and access controls govern who sees reports with personal information.

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.

What engineering leaders say.

“I use minware for our SDLC metrics and appreciate its ability to provide quality metrics throughout our SDLC. It gives us clear visibility into code quality, defect rates, and overall development health.”
George Varagas
Senior Director of Engineering, Redica Systems
Read the review on G2
“The platform comes with a great set of engineering reports out of the box, so you can start getting value immediately. At the same time, those reports are highly customizable, and minQL makes it possible to build your own metrics and dashboards tailored to your team's workflow.”
Artur Yafimchik
Head of PMO, 4dev
Read the review on G2

Frequently Asked Questions

What is minware?

minware is the engineering productivity platform that gives you ready-to-work actions ranked by impact to eliminate wasted tokens and human time. It links coding agent sessions, pull requests, and tickets, and its agent analyzes them to find actions with estimated savings.

Which coding agents does minware support?

minware reads Claude Code and Codex sessions through OpenTelemetry, and Cursor and GitHub Copilot usage through their APIs. It also connects to GitHub, GitLab, Azure DevOps, Bitbucket, Jira, Linear, and Azure Boards.

How is minware different from asking a coding agent to analyze its own sessions?

A general agent with API access sees one repository or ticket at a time and runs out of context long before it has read a year of history. minware’s agent runs on the patented minQL data engine, which has already linked every session, PR, and ticket, so it can query any metric by any dimension and read only the relevant context, at a fraction of the token cost.

How does minware estimate the savings for each action?

minware’s agent builds a classifier for each issue it finds and runs it over every event, not a sample, to measure the tokens and engineering time the issue has cost. The estimated savings come from that history, and each action links to example sessions that show what happened.

Does minware use my data to train AI models?

No. minware never uses your data to train AI models for other organizations; models built on it serve only your organization. Custom data residency is available for enterprise customers, and an on-premise ingest agent is available to keep keys and data under your control.

Try minware today

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