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Engineering Analytics - Turn engineering data into instant, actionable insights.
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Trusted by Users, Awarded by G2
    
  
Curious to know your ROI from AI?
AI ROI - Speed, debt, and impact in one view.
Engineering Analytics - Turn engineering data into instant, actionable insights.
AI Code Review Agent - Accelerate reviews, catch issues, and release with confidence.
Trusted by Users, Awarded by G2
Build vs Buy

The Real Cost of Building Your
Engineering Intelligence Platform In-House

Most engineering orgs think building in-house makes sense. Here's what the math actually looks like before you commit a team to it.

Calculate Your Real Cost
Cost Calculator
In-House Build
$4M
Hivel Platform
$500K
9 FTE Team
3yr Total Build Cost
8× ROI vs Build
The Visibility Trap

A DORA Dashboard Is Not an
Engineering Intelligence Platform

Most engineering orgs have basic DORA dashboards built with Power BI or Looker. Here's why the data is unreliable.

Data Integrity
30–40%
webhook failure rate, industry-wide
The numbers are wrong and nobody knows
Data disappears silently while the dashboard keeps displaying numbers. Decisions on that data carry false confidence, worse than no data at all.
Data Architecture
No drill-downs
available for root causes in a reporting layer
"Why did cycle time increase?" takes two days to answer
Root-cause drill-down requires row-level, event-level data architecture designed in from day one. It cannot be retrofitted, ever.
66%
of developers
don't believe current metrics reflect their contributions.
Metric distrust erodes engineering culture
Root-cause queries take days, not seconds
Silent data loss creates false confidence
Your Real Cost

The Team You'd Actually Need
to Build This Properly

Salary inputs — minimum viable team

Fully-loaded annual cost · 9 headcount total

Senior Backend Engineer
$
Data / ML Engineer
$
DevOps Engineer
$
Security Engineer
$
Product Manager
$
Designer
$
Number of integrations GitHub, Jira, Copilot, CI/CD...

Each = 15 working days at backend daily rate ($720/day)

Year 1 team cost

$1.31M

3 backend + ML + DevOps + Security + PM + Designer

Total 3-year cost of building

$3.97M

Includes team · $150K infra · integrations

The Architecture Gap

What the Six-Month Estimate
Doesn't Account For

What AI builds in days

The "surface layer" of your platform

Core UI and dashboards
Basic DORA metrics display
Simple API connections

The gap in your estimate

What the 6-month timeline misses

AI genuinely compresses the visual layer. But the remaining 80% (infrastructure, security, accuracy, and behavioral change) hasn't changed.

Without solving for these, you're building a dashboard that developers don't trust, which eventually becomes a maintenance burden rather than a growth engine.

What takes the majority of total effort

And what your estimate doesn't include

Production ETL that survives API failures, format changes, and webhook drops without silent data loss

Enterprise RBAC with role inheritance, row-level security, SSO, and audit logs designed in from day one

Real-time data accuracy — not last week's script run that developer teams don't trust

Maintenance Reality

Shipping It Is the Easy Part.
Owning It Is a Different Job Entirely.

Integration Maintenance

Breaking Changes & Silent Fails

100+ API changes monthly requiring 7–14 hours to fix, plus irregular token expirations that create permanent, irreparable gaps in your history.

Security Debt

Constant CVE & Library Patching

Maintenance is not one-time. Dependency trees require constant patching to resolve new CVEs and security vulnerabilities disclosed every month.

System Fragility

Architectural Complexity Multiplied

Adding a fifth integration isn't incremental — it multiplies the surface area for failure and complicates the architectural integrity of the entire platform.

Operational Burden

24/7 Monitoring & Infrastructure

In-house tools require dedicated on-call support. When a sync fails, your core engineers are the ones who pay the price in lost productivity.

Tokens Expire Silently
1 day
Jira default token lifetime
GitHub default: 30 days

When a token expires, data sync stops. The system keeps running. Numbers keep displaying.

A 5-day data gap isn't a bug — it's what silent failure looks like in production. The data is gone. There is no recovery.

What You Cannot Build, Period

Capabilities No Internal
Build Can Deliver

Access to these requires a platform that has already accumulated the industry's network data.

Cross-Company Benchmarking

Your platform tells you cycle time is 3.2 days. It cannot tell you whether that's good or alarming for your size, stack, and growth stage. That requires aggregated data across hundreds of organizations accumulated over years.

You cannot get there by building harder — the data only exists inside a platform that has already accumulated it across hundreds of deployments.

Data network problem
"You'll know your numbers. You'll never know what they mean."
AI Code Detection at Production Level

GitHub Copilot doesn't label AI-generated code in commits. Detecting what AI code actually did — bug rates, churn, review time, maintainability — requires pattern recognition trained across multiple companies over time.

GitClear, 2024: AI-generated code has a 41% higher churn rate than human-written code. Without measurement, you are making code quality decisions completely blind.

Single-company data is insufficient
"You're deploying AI code. You have no idea what it's doing to your codebase."
The Behavioral Layer

Knowing your metrics is not the same as improving them. The alerts that surface real bottlenecks before they become incidents, the goals your engineering leaders track movement against — none of this can be reverse-engineered from first principles inside a single org.

It comes from running this across 50+ deployments — a forward-deployed team that builds your success roadmap and runs automated cleanup across your Jira and Git environment.

Requires 50+ deployment baseline
"A dashboard tells you what happened. It doesn't change what happens next."

An in-house developer productivity tool is not scalable because nobody owns the roadmap. The platform drifts from what the team actually needs, and adoption quietly dies.

The Cost of Waiting

Every Month Without Measurement
Is a Month Your AI Spend Is Flying Blind

Analytical Framework

Without a measurement layer, you cannot answer any of these:

Which teams are getting real productivity lift from AI tools?

Which teams are generating rework that wipes out every speed gain?

Is the AI investment returning positive ROI?

How do you reallocate licenses from low-ROI to high-ROI teams?

71%

Abandonment Rate

of in-house IT builds eventually abandoned

The Compounding Gap

Orgs that started measuring in 2024

18+ months of optimization decisions already compounding

AI tool ROI proven, licenses reallocated to where they work

Engineering behavior already changed

Measurement built into daily workflow

Orgs without clear measurement in place

Zero optimization history

No idea which AI spend is generating returns

Behavioral change yet to begin

No measurement habits

What Hivel Actually Is

The UI Is Just 20%.
Here's What the Platform Actually Is.

The infrastructure, intelligence, and human systems that no internal build can replicate.

The Intelligence Engine

ML Models Trained on Multi-Company Data

Proprietary signals for anomaly detection, velocity scoring, and burnout—trained across hundreds of organizations. Information architecture that single-company builds cannot replicate.

The Partnership

Forward-Deployed Engineers

Our FDEs deploy data cleanup agents that ensure Jira/Git hygiene before showing you a single insight.

The Pipeline

Production ETL

Continuous normalization across 30+ integrations without silent data loss.

Governance

Enterprise Security

SOC 2 Type II and ISO 27001 certified. Engineered for instant InfoSec clearance.

Infrastructure

Flexible Deploys

Deploy on-prem, VPC, or air-gapped. Hivel adapts to your infrastructure, not the other way around.

Talk to the team

See What This Looks Like
For Your Org

Speak to an Expert

Join 200+ engineering organizations already using Hivel

Trusted by 1000+ Engineering Teams

Outcomes That Engineering Leaders Can Measure

Vaibhav Bansal
V.P. Engineering
"Hivel has been phenomenal in helping us present our progress to leadership. Its insights into leading indicators have empowered our team leads to enhance review efficiency and ship features faster. Now, we are able to show the impact of our process changes, clearly identify areas of opportunity, and bring in a data-driven culture without spending time pulling manual reports."
Murali Swaminathan
Chief Technology Officer
“We were looking for a unique solution to understand engineering productivity and efficiency, with a single tool that could give leaders complete visibility. We evaluated other comparable tools, but none were comprehensive enough. When we came across Hivel, it checked all the boxes for measuring developer productivity across both AI and non-AI workflows.”
Derek Mackie
Director of Engineering
"Using Hivel shone a light on the non-standard aspects of our work, emphasizing the need for crucial adjustments. The insights from Hivel helped us recognize areas for improvement, particularly in managing cycle times and streamlining development workflows. This guidance has been instrumental in our efforts to enhance operational efficiency and productivity."
Leonardo Pinheiro Ferrari
Chief Technology Officer
"Thanks to Hivel, we've seen a staggering 157% improvement in our deployment frequency. By identifying and addressing inefficiencies, and adopting best practices, we've significantly accelerated our progress. Hivel has been instrumental in our journey to operational excellence."
Chris Callander
Senior Engineering Manager
"Hivel has been instrumental in identifying areas for enhancement and guiding our strategic improvements. It has significantly contributed to our journey toward greater operational efficiency. The Activity and Dev360 features in Hivel have been particularly valuable for providing feedback to engineers during 1-1's, enhancing our communication and development practices."
Shiro Theuri
Chief Technology Officer
"Before Hivel, understanding our engineering team's dynamics was a game of guesswork. Now, with Hivel's clear dashboards and insights, our leaders have the clarity and time-saving tools they need to excel. It's been transformative in streamlining our operations."
Koti Reddy
Chief Technology Officer
"Incorporating Hivel into our daily operations has been a game-changer. It's not just a tool; it's a part of our strategic decision-making process, trusted by our leaders at every level. The impact on our engineering efficiency is undeniable and deeply valued."
Manoj Awasthi
Chief Technology Officer
"It has been great to partner with Hivel to get access to insights that help leaders at all levels gain visibility into bottlenecks and challenges that the teams face, build actionable plan to resolve them and track the progress over time. And thanks for the fantastic support!"
Ameen M
Engineering Manager
"Hivel has made a huge difference to how I manage my teams. Before every one-on-one or appraisal, I rely on it to dive into developer metrics and drive truly meaningful conversations. It helps me spot rework patterns, reward high performers, and benchmark our teams even against our US counterparts. The dashboards are intuitive, insightful, and honestly far more actionable than anything we get from Jira."
Paras Sood
Associate Director
"Hivel's insights have been pivotal in refining our engineering processes, elevating both efficiency and the developer experience. We're now making smarter decisions, faster, thanks to Hivel."
Vaibhav Bansal
V.P. Engineering
"Hivel has been phenomenal in helping us present our progress to leadership. Its insights into leading indicators have empowered our team leads to enhance review efficiency and ship features faster. Now, we are able to show the impact of our process changes, clearly identify areas of opportunity, and bring in a data-driven culture without spending time pulling manual reports."
Murali Swaminathan
Chief Technology Officer
“We were looking for a unique solution to understand engineering productivity and efficiency, with a single tool that could give leaders complete visibility. We evaluated other comparable tools, but none were comprehensive enough. When we came across Hivel, it checked all the boxes for measuring developer productivity across both AI and non-AI workflows.”
Vaibhav Bansal
V.P. Engineering
"Hivel has been phenomenal in helping us present our progress to leadership. Its insights into leading indicators have empowered our team leads to enhance review efficiency and ship features faster. Now, we are able to show the impact of our process changes, clearly identify areas of opportunity, and bring in a data-driven culture without spending time pulling manual reports."
Derek Mackie
Director of Engineering
"Using Hivel shone a light on the non-standard aspects of our work, emphasizing the need for crucial adjustments. The insights from Hivel helped us recognize areas for improvement, particularly in managing cycle times and streamlining development workflows. This guidance has been instrumental in our efforts to enhance operational efficiency and productivity."
Leonardo Pinheiro Ferrari
Chief Technology Officer
"Thanks to Hivel, we've seen a staggering 157% improvement in our deployment frequency. By identifying and addressing inefficiencies, and adopting best practices, we've significantly accelerated our progress. Hivel has been instrumental in our journey to operational excellence."

"The only tool our entire leadership team actually trusts"

- CTO, Enterprise
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