Your AI chat prompt is just step 1.
Because your best engineers are spending more time reconstructing context than writing the code that needs it.
10 years in business. All work guaranteed.
By now, most engineering teams have delivery down: sprints planned, tickets closed, releases shipped. That was never really the question.
Say you're the VP of Engineering. A senior engineer just left, and the new hire's first real task is a service nobody's touched in eight months. The design doc is out of date, the reasoning behind three key decisions lived entirely in the departed engineer's head, and the fastest way to get answers is Slack archaeology.
Every onboarding into existing code starts the same way: piecing together intent from comments, commit messages, and whoever's still around to ask.
Which means your team isn't building on institutional understanding. It's rebuilding it, one engineer at a time, from whatever's left behind.
And code built without the original context is already carrying risk no one's written down.
By the time the missing context causes a real incident, the person who could have explained it is long gone.
Recent research on developer time allocation found engineers spend just 14% of their time actually writing new code, consistent with prior studies putting the figure between 11% and 18%. The rest goes to reviews, meetings, coordination, and reconstructing context that was never captured.
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Hours your team spends on coordination and context-reconstruction every week
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Full-time engineers' worth of capacity going to context, not code
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Annual cost of engineering capacity that isn't shipping product
Based on developer time-allocation research: engineers spend just 14% of their time writing new code, with roughly 23% going to meetings, coordination, and reconstructing context that was never captured. Cost estimate assumes a 20-person mid-market engineering team at $110/hr average fully-loaded cost.
This isn't just about busier engineers. Every hour spent reconstructing context that already existed once is an hour not spent building what's next, and that gap is exactly what separates an engineering team that ships from one that ships faster every quarter instead of slower.
Here's the shift that actually closes the gap:
From
To
Which side of this shift are you on?
Share your work email and we'll show you what closing this gap looks like for your engineering team.
Video placeholder: CEO talk track on Engineering Intelligence, approximately 45 seconds
If a past AI effort didn't stick, that's not a reason to wait. It's usually a sign no one diagnosed the right starting point first.
Our edge isn't more automation. It's activating the human advantages AI can't replace. Practically, that means we diagnose before we build, never the other way around.
Put AI to work for your people.
The Rumble is a fixed-price engagement that generates an AI activation roadmap, then tells you whether you need Guidance, Execution, or both.
Single Core Function
$10,000
Pinpoint one Intelligence domain and roadmap where to activate AI first.
Recommended
Business-Wide Diagnosis
$25,000
A full cross-functional view, prioritized by impact.
Coming out of the Rumble, you'll know exactly which comes next:
Case Study
[Client Name]
[One or two sentence summary of the client's challenge and how Engineering Intelligence solved it.]
An AI system that turns fragmented engineering context into a single, structured source of truth: verified, current, and queryable by every engineer, not just the ones who were there. Engineering knowledge becomes infrastructure. Captured. Searchable. Trusted.
Queryable system context
Engineers ask why a system works the way it does and get answers drawn from design docs, commit history, and decisions that were actually made, not whoever's still around to ask.
The ProblemEndless Slack archaeology. Countless hours spent reconstructing decisions someone already made once.
Automated onboarding acceleration
New engineers get the reasoning behind the code on day one, not after months of osmosis and interrupting senior staff.
The ProblemEndless onboarding drag. Countless senior engineers pulled off real work to answer questions the system should answer.
Institutional engineering memory
Every architectural decision and hard-won lesson stays captured and searchable, so the system's history survives every departure.
The ProblemEndless engineering knowledge lost to turnover. Countless incidents caused by context nobody wrote down.
The fragmented-data problem on this page isn't unique to Engineering. It shows up everywhere else in your business, and costs growth in each one. Explore where AI activation moves you forward next.
01
Company Intelligence
Activate the knowledge layer your AI is missing.
02
Finance
Activate the forecasting layer your finance team is missing.
03
Sales
Activate the revenue layer your sales team is missing.
04
Marketing
Activate the attribution layer your marketing team is missing.
05
HR
Activate the workforce layer your HR team is missing.
06
Operations
Activate the process layer your operations team is missing.
07
Engineering
Activate the context layer your engineering team is missing.
08
IT
Activate the infrastructure layer your IT team is missing.
09
Security
Activate the posture layer your security team is missing.
Ready to put AI to work for your people?
Let's get started!