Understand before changing
In a mature system, the safest shortcut usually starts with tracing how the current behavior works and why it exists.
About
I like understandingthe bigger picture.
I’m Aki Ovaska, a software engineer with more than eleven years of experience in ecommerce and SaaS. Over that time my work has moved across storefronts, merchant tools, backend systems, APIs, integrations, identity, checkout, payments, data, and infrastructure.
How I work
From an unclear problem to a useful product
I like getting to a useful version quickly, but not by skipping the thinking. I start by understanding the problem, the people affected by it, and the parts of the system that already shape the answer. From there I reduce uncertainty with research, prototypes, and small tests, then build the smallest version that can teach us something real.
Shipping matters because real users expose things planning cannot. A team can design and engineer from its current knowledge, but feedback shows which assumptions were right, what was missing, and what actually matters. I would rather get a solid version into use, learn from it, and improve with evidence than spend too long trying to predict every requirement in advance.
AI helps me move through this cycle faster — researching unfamiliar areas, comparing approaches, prototyping, tracing failures, reviewing work, and documenting what matters — but the decisions still need to come from the actual product, system constraints, and user feedback.
- 01
Understand the problem
Start with the user need, business context, existing behavior, and the outcome we actually want.
- 02
Trace the system
Find where the rules, data, dependencies, and existing promises live before deciding what should change.
- 03
Explore quickly
Compare approaches, use AI where it helps, and prototype the uncertain parts before committing to a bigger solution.
- 04
Build the useful core
Keep the first version focused. Solve the real problem well enough to put it in front of users without hiding behind unnecessary complexity.
- 05
Ship and observe
Get the product into real use. Logs, support, analytics, and direct feedback reveal things a design document never will.
- 06
Iterate with evidence
Use what we learned to improve the parts that matter, remove weak assumptions, and invest deeper only where the product earns it.
Stack evolution
The stack changed with the product
Storefronts led to application frontend work, then APIs, identity, payments, data flows, headless systems, cloud delivery, and AI-assisted engineering. Each step added range without erasing the older layers.
- 01
Storefronts and merchant tools
PHP, JavaScript, jQuery, Liquid, and responsive ecommerce interfaces formed the first working layers.
- 02
Applications and connected systems
Vue and Angular applications grew alongside APIs, identity, payments, data flows, and background processing.
- 03
Headless and AI-assisted work
React, Next.js, Node.js, GraphQL, cloud delivery, and coding agents expanded the same product-engineering practice.
Current direction
Where it is heading
AI-assisted tools are part of my day-to-day engineering workflow. I use Claude Code, OpenAI Codex, Cursor and Perplexity across research, investigation, planning, architecture exploration, prototyping, design, implementation, debugging, testing, review, and documentation.
The useful part is not generating more code. It is being able to investigate more of the problem, compare more approaches, and iterate faster. I still set the constraints, make the product and technical decisions, and verify the result against the actual system.
In current development work I also use multi-agent orchestration, project-specific agent skills, persistent project context, and longer-running agent workflows when they make the work easier to decompose, inspect, and verify.
I’m especially interested in what makes agent-assisted work dependable: the context an agent receives, how work is split between agents, the tools it can use, how results are evaluated and verified, and how longer workflows recover when something goes wrong. I’m also going deeper into retrieval, ML and data quality, and agentic commerce.
Engineering perspective
How I think about the work
A few principles that keep showing up whether I’m working on a mature product, a new system, or something I’m still learning.
Keep complexity earned
I ask what a new abstraction, service, or dependency actually buys us. If a simpler solution handles the real constraint, I prefer it.
Ship useful versions early
A smaller version in real use teaches more than a perfect version that stays in planning.
Let users correct assumptions
We can design from what we know, but real users show where the model is wrong. Their feedback should change the product.
Verify important assumptions
Running behavior, source material, tests, traces, and data carry more weight than a plausible explanation.
Follow the problem across the stack
I do not stop at a frontend or backend boundary if the real cause lives somewhere else.
Make systems easier to understand
Clear names, useful documentation, visible behavior, and predictable flows make future changes faster and safer.
Use AI for leverage, not judgment
I use agents to research, explore, prototype, debug, and review faster, but product and technical decisions still need human judgment and verification.
Keep learning
Tools and stacks change. The useful skill is being able to understand something new quickly, test it in practice, and keep what actually improves the work. Learning is every day goal not a once in a while thing. Once we stop learning, we stop growing.
Working remotely
A portable setup, a wider perspective
I work mostly remotely and spend time between countries. My full setup fits in a backpack — MacBook, iPad, keyboard, and mouse — so I can keep the same working environment almost anywhere.
Changing environments is refreshing to me, not disruptive. It resets my perspective, keeps me curious, and often gives me more energy for new projects. Living and traveling in different countries also makes product differences easier to notice: language, payment habits, expectations, workflows, customer behavior, and the small things people consider “normal” are not universal.
That matters when building products for an international audience. The more contexts you have seen, the less likely you are to assume your own way of using a system is the only one. I also like learning how different businesses work, because good product decisions need domain context, not only technical skill.
Outside work, I spend a lot of that travel time with a camera, learning languages, reading and paying attention to how people and businesses operate.
Interested in working together?
If my background looks relevant to your team, I'd be happy to hear what you're working on.