iftrue
iftrue · Coding · Productivity
iftrue is an engineering analytics platform that brings AI code attribution, DORA metrics and developer surveys into one signal, delivered inside Slack for engineering leaders.
About iftrue
What Is iftrue
iftrue is an engineering intelligence platform built for engineering leaders who need to show what AI coding tools are actually doing for their teams. It pulls three streams of data together: who wrote which code with which AI assistant, how fast work moves through the delivery pipeline, and how the people doing the work say they feel. The pitch is simple. AI adoption broke the old metrics, so the product rebuilds them from the code, the delivery system and the humans behind it.
The problem it solves is reporting. A VP of Engineering gets asked "is Copilot worth it?" and usually has no hard answer. iftrue sets an AI adoption date, then compares cycle time, PRs per day, review time, bug ratio and rework before and after, across the whole org and per team. It can also flag trade-offs, such as more PRs being created while review time climbs, so a backlog does not hide behind a velocity number.
There are limits worth knowing up front. Pricing runs per developer per month, and the public site does not list a number, so you have to book a demo for a quote. There is no free tier. The product reads pull request and review metadata, not source code, which is good for privacy but means it cannot inspect code quality line by line. And the whole thing is aimed at organizations large enough to have teams, DORA habits and a Slack workspace.
Getting Started
- Book a demo through the site so the team can size the rollout to your stack
- Connect source control and issue trackers, mainly GitHub, GitLab, Bitbucket, Jira or Linear
- Set your AI adoption date and the comparison window, usually 30, 60, 90 or 180 days
- Install the Slack or Microsoft Teams app so digests, surveys and alerts land where the team works
- Review the org summary and team heatmap, then hand the report builder output to leadership
Product Information
A quick look at iftrue's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Engineering leaders
- Engineering managers
- Tech leads
- Platform and DevEx teams
Tasks
- AI ROI reporting
- AI code attribution
- DORA tracking
- Developer experience surveys
- Capacity planning
- Executive reporting
Scenarios
- The budget conversation
- Uneven AI adoption
- A quiet morale problem
- Sprint planning week
- A regulated rollout
Key features
AI Impact Measurement
The core feature ties AI assistants to delivery outcomes. You set the adoption date, and iftrue compares metrics across the whole organization before and after, with a percentage improvement on each one. It attributes code to specific tools like GitHub Copilot, Cursor, Claude Code and Windsurf, so a team can see which assistant actually moved the numbers.
DORA and Delivery Metrics
iftrue tracks the 2025 DORA set automatically from your stack: lead time for changes, deployment frequency, change failure rate, failed deployment recovery time and rework rate. Because the data comes from GitHub, GitLab and pipeline events, nobody has to hand-compile a report each month. Sprint forecasting and issue cycle time sit alongside the delivery metrics.
Developer Experience Surveys
The platform sends surveys across 14 categories and tracks workload and burnout trends over time. Managers get a 1:1 prep view and career tabs built from the survey and delivery data, so a check-in starts from evidence rather than memory. This is the part that treats the humans in the system as a first-class signal, not an afterthought.
Slack and Teams Chatbot
iftrue runs in the background and surfaces insight where teams already work. The native Slack bot delivers daily digests, sends surveys, fires real-time notifications and answers plain-language questions on demand, such as asking for a team's cycle time that week. Microsoft Teams gets the same insights and alerts in channels.
Team Comparison and Heatmap
The impact heatmap lines every team up against every metric, flagging regression, no change or improvement in one grid. Spotting that the Platform team cut cycle time by 62% while the Mobile team managed 12% is the kind of comparison a leader can act on. The tool also names possible trade-offs when two metrics move in opposite directions.
Report Builder and Investment Distribution
A report builder turns live numbers into leadership reports with custom templates, which cuts the time spent assembling charts by hand. Investment distribution shows where engineering time actually goes, so planning decisions start from where effort landed last quarter rather than where people hoped it went.
Broad Integrations and On-Prem Option
Setup connects to six AI coding assistants, four source-control systems, Jira, Linear, Jenkins, Slack and Microsoft Teams, usually in minutes. Regulated teams can pick an on-prem or private-cloud deployment with SSO, SAML, LDAP, audit logs and data residency, and they can bring their own LLM provider so prompts never leave their chosen vendor.
Pros and cons
Pros
- Unifies AI attribution, DORA metrics and developer surveys in one place, instead of stitching three dashboards together
- Delivers insight inside Slack, so leaders act without leaving the tool the team already uses
- Before-and-after AI comparison answers the ROI question with numbers tied to a real adoption date
- Does not store repositories, which lowers the privacy bar for getting security approval
- On-prem option with SSO, audit logs and bring-your-own-LLM suits regulated organizations
Cons
- No free plan and no public price, so you cannot self-serve or budget without a sales call
- Value depends on wiring up source control, trackers and Slack first, which is a project in itself
- Only reads pull request and review metadata, so it cannot judge code quality or design decisions
- Aimed at orgs with several teams and existing DORA habits, so a small startup gets little from it
Frequently asked questions
iftrue measures how AI coding tools affect engineering delivery. It combines AI code attribution, DORA-style delivery metrics and developer experience surveys into one platform, so engineering leaders can see where AI helps, where delivery slows down and where teams are struggling.
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