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predictable delivery in the AI era

Prove the measurable engineering outcomes of your AI investment.

Get complete visibility into AI investment, impact, and engineering efficiency to maximize the value of your AI-SDLC.
Read-only access
SOC 2 Type II
Board ready insights
WHAT hivel does

Connect AI spend, impact, and engineering performance in one view.

AI Impact

See what AI changes in how your teams ship.

Compare speed, quality, and rework before and after AI, by team and by tool. See how much AI-written code reaches production, how much of it lasts, and where it moved the bottleneck in your AI-SDLC.

AI ROI

Put an ROI number on every AI coding tool you pay for.

Track spend by tool, model, team, and token. We trace every line of AI code that reaches production and size the budget you can win back from idle seats, overlapping tools, wrong-model spend, and zombie agents.

Engineering Insights

Trace where AI-SDLC work slows down, right to the pull request.

Link every ticket, commit, review, deploy, and incident in one context graph, so you can trace a slow release or a spike in bugs back to its cause. Alerts flag drift before the sprint review does.

Engineering Productivity

Measure engineering speed, quality, and throughput in the AI era.

Track DORA, SPACE, and SURGE metrics, review load, and team health in one place. Spot burnout signals and uneven workloads early, and coach with data instead of gut feel.

Hivel connects to 21 engineering tools, including GitLab, Jenkins, Azure Boards, Jira, Azure DevOps, Google Calendar, Slack and Outlook, with read-only access. It matches 400 engineers across them, links 48,210 commits to their pull requests, tickets, incidents and AI tools, marks the 31% of merged lines written by AI, and benchmarks AI adoption against 1,000+ teams. The first report found $7K a quarter in idle AI seats, a 41% longer review wait on Payments, and that 79% of AI-written code survives for 90 days, vs. 93% for human-written code.

tools

connected to one engine. Nothing to install, nothing to change.

Read-only access
engineers

matched to one identity across Jira, Slack and 17 other tools.

commits

linked to the pull requests, tickets, incidents and AI tools behind them.

of merged lines were written by AI, tagged by the tool that wrote them.

CopilotCursorClaude Code
teams

benchmarked, so you can see where your team stands on AI adoption.

a quarter in AI seats unused for 30+ days, across Copilot and Cursor.

Found in the first report

longer review wait on Payments, since AI made pull requests bigger.

Found in the first report

of AI-written code survives for 90 days, vs. 93% for human-written code.

Found in the first report
sample insights

This is just a sample of what your report surfaces. Get more insights like these.

21tools400engineers48,210commits
Get your sample report
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surge  · Hivel's ai roi framework

Five numbers that prove what AI returned.

Built on 20,000+ AI coding seats. Co-written with Nathen Harvey of Google's DORA team.

MODEL MIX
48%
on default model
Copilot · Cursor · Claude
Default model
48%
Chosen deliberately
52%
Analyzing model usage
HIVEL INSIGHT
48% ran on a default model nobody chose.
SEAT UTILIZATION
43 / 50
active seats
Active
Idle
Analyzing seat activity
HIVEL INSIGHT
14% of paid seats never used.
AI SPEND · QUARTER
$142.8K
across 3 tools
$31.4K recoverable
TOOL
USAGE
SPEND
Copilot
$61.2K
Cursor
$48.9K
Claude
$32.7K
Finding recoverable spend
HIVEL INSIGHT
$520K a year recoverable in one company.
CODE SURVIVAL · 90 DAYS
79%
AI-written, vs 93% by people
AI-written
People
93%79%Merge30d60d90d
Tracking code survival
HIVEL INSIGHT
79% of AI code live at 90 days.
REWORK RATE
2.5x
AI-written vs people
Per 1,000 lines
Written by people
1x
AI-written
2.5x
Comparing rework rates
HIVEL INSIGHT
2.5x more rework on AI-written lines.
SURGE paper cover: Measuring the ROI of AI in Software Engineering
The SURGE paper
With Nathen Harvey of Google's DORA team.
Read the paper

“For the first time, we can see AI’s impact across 1,500 engineers in one place.”

Murali Swaminathan, CTO, Freshworks
Murali Swaminathan
CTO, Freshworks

“We stopped guessing whether AI was paying off. Now we see it in cycle time.”

Jack Notarangelo, Sr. Director of Engineering, AvidXchange
TrustVerified for enterprise
SOC 2Type II
ISO 27001Information security
GDPRData privacy
SSO & RBACAudit logs included
Jack Notarangelo
Sr. Director of Engineering, AvidXchange

“Hivel shows our leadership exactly where AI speeds us up, and where it adds rework.”

Vaibhav Bansal, VP Engineering, W Energy
Vaibhav Bansal
VP Engineering, W Energy

“The impact on our engineering efficiency is undeniable.”

Koti Reddy, CTO, Conga
Koti Reddy
CTO, Conga
Freshworks
45%
Reduction in rework and maintenance
AvidXchange
56%
Reduction in cycle time
W Energy
40%
Increase in engineering throughput

Watch How Leaders Catch Delivery Risks and Prove AI ROI

See how CTOs and VPs use Hivel to spot what's slowing their teams, quantify AI's impact, and walk into board meetings with real data.

Security & scale

Enterprise-ready from day one.

Read-only by design · Never your source code
01

Passes InfoSec on the first review

SOC 2 Type II, ISO 27001 and GDPR. Read-only metadata access, never source code. Role-based access, audit logs and SSO included.

Metadata only · No source code
02

Built for orgs with hundreds of teams

Normalizes data across teams, regions and acquired stacks automatically, from 50 engineers to 5,000.

50 → 5,000 engineers
03

Outcomes, not just dashboards

Hivel's DevEx engineers and data-cleanup agents keep your data accurate, so your leaders spend time on decisions, not audits.

Experts + agents
04

Deploy where your code lives

Cloud, VPC, on-premise or air-gapped. Hivel fits your infrastructure, not the other way round.

Cloud · VPC · On-prem · Air-gapped
TrustVerified for enterprise
SOC 2Type II
ISO 27001Information security
GDPRData privacy
SSO & RBACAudit logs included

Questions engineering leaders ask us

Are our AI coding tools actually paying off?

Connect AI tool usage and spend on engineering delivery metrics to understand where AI is contributing to throughput, speed, and quality, rather than relying on license utilization alone.

Where are we wasting money on AI tools?

Identify underused licenses, token consumption patterns, and opportunities to right-size AI tool spend. Understand where your investment may be underutilized before your next renewal.

How much AI-generated code actually reaches production?

Trace AI-assisted engineering activity through code review and delivery to understand how much AI-generated work survives review and makes it into shipped software, rather than measuring code generation alone.

How do we prove AI ROI to the CFO or board?

Bring AI investment, adoption, and engineering outcomes into a consolidated view. Give leadership evidence to evaluate returns, understand costs, and make informed decisions about future AI investment.

Why do we need Hivel if we already have Jira, Git, and engineering dashboards?

Your existing tools capture important parts of the development lifecycle. Hivel connects engineering data with AI usage and investment to help answer a broader question: what is AI changing across your engineering organization?

How do we measure AI's impact on engineering performance?

Compare engineering performance before and after AI adoption using delivery, throughput, and quality metrics. Put AI activity in the context of the engineering outcomes your organization already tracks.

"The only tool our entire leadership team actually trusts"

- CTO, Enterprise
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