Service
AI Product Development
Custom AI product development for established SMBs that need useful features, safe integrations, and a clear path from PoC to production.
Pixelswithin took our ideas and made them better.
Who this is for
This might be you.
You want AI that solves a real business problem.
Most established SMBs do not need another demo. They need AI product development that improves a workflow, reduces manual work, or gives customers a better experience without forcing the company to rebuild everything around it.
Your team is already doing the kind of work AI handles well.
Reviewing documents, routing requests, searching internal knowledge, classifying records, and flagging exceptions are all strong candidates when the process is repetitive but still needs judgment.
You need a practical path through the AI hype.
If you are responsible for a 50-500 person business, you need straight answers on quality, security, integration, and ROI. That usually means starting with a small proof of concept before committing to a full build.
The approach
How I handle this specifically.
Start with the workflow, not the model.
The right AI feature begins with a specific operational pain point. I map the existing process, identify where AI adds value, and separate true automation opportunities from tasks that should stay human-led.
Validate value with a proof of concept first.
For established SMBs, the safest path is a focused PoC or MVP that proves quality, integration, and user adoption before the company commits to a larger investment.
Design quality controls into the product.
AI will be wrong sometimes, so the product needs evaluation, human review steps, logging, and clear override paths. Good AI product development makes errors visible and manageable instead of hiding them.
Deliverables
What you get at the end.
A production-minded AI product, built around your business workflow and ready to test with real users.
- Working AI feature or prototypeA usable starting point tied to one clear business case.
- Quality and evaluation frameworkDefined checks for accuracy, failure cases, and human review.
- Implementation roadmapA practical plan for PoC, MVP, integrations, and rollout.
- User testing and feedback loopReal feedback from the people who will actually use it.
- Source code you ownBuilt so your business is not locked into one vendor or model.
- Post-launch supportHelp while the product is adopted, tuned, and stabilized.
“If I had a concern about any component of the deliverables, Diana fixed them within 24 hours.”
Adriani Coleman · Lead Digital Learning Specialist, Digital Learning & Pedagogical Solutions
If this is what you've been looking for, let's talk.
Start a Conversation“She seemed to effortlessly understand what it was that we were looking for. She took our ideas and made them better.”
John G. McCabeSenior Consultant · Decision Analysis Inc., Los Angeles
Client and collaborator experience includes
Common questions
Before you reach out.
Why not just use no-code or low-code AI tools?
No-code and low-code tools are useful for quick experiments, but they usually break down when you need custom workflows, stronger security, deeper integrations, or a product your team will rely on every day. If the use case is simple, I will say that upfront. If it needs to last, custom development is usually the better fit.
How do you ensure quality when using AI?
Quality starts with the product design, not the model. I define success criteria, test edge cases, add human review where needed, and use structured evaluation so the system can be measured instead of guessed at. The goal is dependable output, not just impressive output.
What kinds of AI products make sense for an SMB?
The strongest fits are workflows with repeated decisions or heavy information handling: document review, customer support assistance, internal search, intake triage, lead qualification, reporting, and operational copilots. If the company has a clear process and enough volume, AI can usually add value.
How do you handle privacy, compliance, and sensitive data?
Those questions are part of discovery, not an afterthought. I look at what data is involved, what should stay internal, what can be sent to a model, and what logging or access controls are required for your industry and risk profile.
What if AI is not the right solution?
Then I will recommend a simpler path. Sometimes rules, workflow automation, or a better data structure solve the problem more reliably than AI. The point is to improve the business, not force AI into every process.
I’m Diana Lopez.
I’m a senior product engineer and digital transformation consultant who turns messy business operations into clean, usable software.
I work directly with business owners to understand their problem, design the right system, and build it.
Senior-led, solo practice — you work directly with Diana, not a team of junior developers.
Start a conversation
Drop me a line.
If the tools aren’t keeping up with the business, let’s talk. A real conversation about what you’re trying to fix.