Make your engineering team measurably faster.
See your true velocity, find what's slowing you down, and know exactly what it costs, so engineering, leadership, and finance are working from the same numbers.
Most teams have the tools. Almost none have the visibility.
Without it, adoption looks like impact, bottlenecks stay hidden, and progress is a guess. CodieDev closes that gap, guiding the transition to AI-first development, then turning every session into the data, visibility, and insight your team needs to keep accelerating.
One full view of the software development lifecycle.
Your code, your tickets, and your AI coding tools each tell part of the story, in separate systems that never line up. CodieDev brings those siloed streams together into a single, connected view, so technical leadership and finance work from the same picture: how work moves from plan to merge, where it stalls, and what it costs along the way.
See your whole engineering org, and exactly where to accelerate.
One dashboard for the entire team's velocity, not a wall of vanity metrics. Understand how fast work actually ships, where the hours really go, and what's slowing you down, so you can fix the bottleneck instead of guessing at it, and watch the team get faster over time.
Real velocity. Lead time from start-coding to merge, sessions per merge, and where time is spent (coding vs. review wait) so you manage the actual constraint.
Bottlenecks, surfaced. Watch the review backlog form before it bites, see which repos are slowest, and catch the moment opened work starts outrunning merged work.
Adoption progress you can act on. Track how the team is taking up AI: power users vs. stuck engineers, steering quality, and a real-time alert the moment a developer is blocked by a rate limit.
Predictability. Re-anchor estimates to where work lands now, with ship-time by percentile instead of stale story points.
See how the work actually happens, not just what ships.
For the first time, the how behind your team's work is visible. CodieDev turns every AI coding session into a readable story: the problem, the approach, the tools used, and the lessons learned, tied to the engineer and the ticket. The reasoning that used to live invisibly in one person's head becomes shared knowledge the whole team can learn from.
Grepped patterns, inspected method signatures, and confirmed downstream parameter types to plan boundary replacements for BILL-475.
Memory file confirms LegacyTimestamp cleanup is a phased effort; BILL-475 is a follow-up phase.
- Fetched ticket BILL-475 via mcp__tracker__getIssue
- Read memory file for prior BILL-460 LegacyTimestamp context
- Grep found 15 controller files containing LegacyTimestamp references
- Created and switched to branch feature/BILL-475
Large refactors are split into phases; memory files track which phase shipped what, so follow-up tickets always know the baseline.
Individual and team view.
Drill into one engineer's sessions or zoom out to the whole team's activity in a single place, so you always know who is working on what.
Patterns worth copying.
See how your most effective engineers approach problems, then turn their habits into team standards the rest of the org can follow.
Readable, not raw.
Every session is summarized in plain language: the problem, what happened, and what was learned, so insight takes seconds, not log-diving.
Know the ROI of your engineering org, and manage its fastest-growing cost.
As your team accelerates with AI, a new variable enters the budget: AI cost, climbing quickly and largely untracked. CodieDev gives you the full economics, so you can calculate return, control spend, and forecast with confidence.
Cost per unit of output. See the true cost to ship, per merge or per feature, trending over time, not a lump-sum bill.
Cost attribution. Break spend down by feature and by type of work (building, fixing, investigating, refactoring) so you know where the money actually goes.
Model optimization. See what the same work would cost across different AI models, and get guidance to switch or blend models to cut spend, with no vendor lock-in.
ROI & forecasting. Compare AI spend against engineering labor cost, calculate return on the investment, and forecast future spend on real usage data.
Waste, eliminated. Spot spend on work that never shipped, and recover it.
A flywheel for a faster engineering org, not a one-time report.
Transformation
Not AI-first yet? We bridge the gap and certify your team's capability. (Skip if you're already there.)
Connect
Link GitHub/GitLab, Jira/Linear, and your AI coding tools. No rip-and-replace.
Measure
One view of velocity, bottlenecks, adoption progress, and cost.
Improve
Act on the insights, ship faster, then measure the lift again.
CodieDev doesn't just measure speed. It creates it.
Measurement tells you where you stand. These features move you forward, automatically, without adding work for your engineers.
Skills that apply themselves.
Your team's best practices, captured once and applied automatically across everyone's work, so code comes out better-crafted the first time and needs fewer iterations. No effort required from the developer.
An agent that clears the backlog.
The bugs, tests, and stale tickets that never get done, handled autonomously and pushed to PR, in volume. Your engineers stay focused on building features, not grinding through the boring queue.
Institutional memory.
The patterns and hard-won lessons your team keeps rediscovering, surfaced from real work, so knowledge compounds instead of walking out the door.
We'll bridge the gap and certify your team.
Every engineering team is making the move to AI-driven development. If yours hasn't yet, the dashboards come later; first we build the capability. Our hands-on program gets your team productive with AI coding tools fast, and engineers earn certification by shipping real production code with AI, automatically verified. No quizzes, no theater. Then the platform takes over to sustain the momentum and measure the lift.
Bring the same lens to the AI in your product.
If you're building AI features into what you sell, CodieDev's cost, optimization, and forecasting tools extend there too, so product and finance can see what those AI features cost, optimize the model mix, and calculate return.