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Why Your AI Team Isn’t Shipping Faster (And What’s Actually Slowing You Down)
Engineering teams spend more time building AI infrastructure than product. Learn why and how to reduce complexity with a unified AI platform.
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Mar 24, 2026
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If your engineering team has been building an AI product for the past few months, there’s a good chance most of that time hasn’t actually gone into building the product.
It’s gone somewhere else.
Into infrastructure.
The Work No One Plans For
At the start, building with AI feels fast.
You connect to a model, get responses, build a quick demo—and it works.
But then reality sets in.
Instead of focusing on what your users actually care about, your team ends up spending time on:
Wiring tools together
Managing databases
Building memory systems
Handling document processing
Integrating multiple models
Maintaining connections across providers
Individually, each of these problems seems manageable.
Together, they become the majority of the work.
The Shift From Product to Plumbing
This is where things start to break down.
Your engineering team isn’t spending most of its time improving the product.
It’s maintaining the system around it.
The actual user-facing experience becomes just one small part of a much larger, more complex stack.
And that stack isn’t optional.
If your AI system needs:
context across interactions
document understanding
reliable responses
flexibility across models
Then this infrastructure is required.
We’ve Solved This Problem Before
This pattern isn’t new.
There was a time when teams built everything themselves:
Authentication systems
Payment processing
Data infrastructure
Today, they don’t.
They use platforms like:
Okta for identity
Stripe for payments
Because rebuilding those layers doesn’t create differentiation—it slows you down.
AI Is at That Same Inflection Point
Right now, many teams are still rebuilding the same AI infrastructure from scratch:
Memory and context management
Model integrations
Document processing pipelines
Orchestration between tools
Switching between providers
This is the hidden cost of building AI products today.
Not the model.
The system around it.
What This Means for Your Team
If most of your engineering effort is going into infrastructure, a few things happen:
Time to market slows down
Systems become harder to maintain
Bugs and inconsistencies increase
Product velocity decreases
And most importantly:
Your team is not spending time on the thing your customers are actually paying for.
A Different Approach to AI Infrastructure
Instead of stitching together multiple systems, some teams are starting to treat AI infrastructure as a layer—just like authentication or payments.
That’s the idea behind Backboard.
A single platform that provides:
Memory and context management
Document processing
Model access across providers
The ability to switch models without rewriting your system
Instead of managing multiple integrations, your team works with one.
Why This Matters
The advantage isn’t just convenience.
It’s focus.
When infrastructure is handled:
Engineering time goes back into product development
Systems become more stable
Iteration cycles get faster
Teams can adapt as models evolve
The Real Tradeoff
Every AI team eventually faces the same question:
Do you want to spend your time building infrastructure…
Or building your product?
Final Thought
AI makes it easier than ever to build something quickly.
But building something that lasts is a different problem.
And increasingly, that comes down to the infrastructure you choose not to build yourself.
Learn More
If your team is spending more time on infrastructure than product, it might be time to rethink your stack.
Rob Imbeault