Solwey

Solwey We build custom software solutions that combine data, design and usability to elevate businesses. That’s where Solwey shines.

Established in 2016, Solwey Consulting is a woman-owned boutique design and development agency focused on customer success. Recently featured in TechCrunch: https://tcrn.ch/3yfocUG

Often, the “simple” solutions that businesses need to succeed are anything but simple to create. It takes years of experience and expertise, an understanding of strategy and design, and an agile, process-driven approac

h to turn a complex solution into a streamlined, easy-to-use, “simple” product. We partner with our clients to create top-tier UX/UI design and custom-tailored software solutions. Our dedicated team members are experts in their fields with decades of combined experience in our industry. Specializing in web and mobile app development, with expertise in Ruby on Rails, Node.js, Dart, React and React Native, Flutter, SQL and noSQL, among other technologies, we deploy highly scalable systems using trusted cloud providers such as Amazon Web Services, Google Cloud, and Heroku. Whether you need custom-tailored e-commerce systems, innovative social networks, event management systems, fintech applications, or even a new CRM, Solwey can deliver a “simple” world-class product quickly and affordably. Contact us here, or through a LinkedIn message, or live chat on our website and we’d be happy to schedule a call and discuss our former projects and experience, and share with you our ideas on how we can help you to grow your business and achieve your goals.

05/28/2026

As AI moves from experimental "cool feature" to core enterprise infrastructure, one question is dominating C-suite conversations: How do we know we can trust it?

The answer lies in Evals (Evaluations). Think of them as the modern version of Test-Driven Development (TDD) for the age of LLMs. Because models are nondeterministic - meaning the same prompt can yield different results every time - traditional software testing isn't enough. Evals provide the structure needed to turn that unpredictability into a measurable, scalable system.

Designing an effective evaluation isn't just a technical task; it's "human problem engineering." Before writing code, you have to define what "good" looks like for your specific use case. Is it accuracy? Safety? Tone? Compliance?

In practice, this looks like a layered approach. Online Evals act as real-time guardrails, checking AI responses for compliance before they ever reach a customer. Offline Evals review performance trends across thousands of conversations to identify "context rot" or subtle drifts in behavior over time.

A common mistake is confusing Benchmarks with Evals. Benchmarks tell you how a model performs "out of the box" compared to its peers. Evals tell you how your product performs using your specific prompts, your data, and your unique business logic.

For regulated industries like finance and insurance, the margin for error is zero. Moving from a prototype to a production-ready agent requires a data-driven feedback loop. By running A/B tests on prompt versions and tracking metrics over time, teams can iterate with the same discipline used in traditional software engineering.

The "move fast and break things" era of AI is ending. Success now belongs to the organizations that can prove their AI is safe, reliable, and compliant through rigorous, continuous evaluation.

The goal is to ensure your technology supports your growth rather than defining its limits.

Read more in our Blog: https://www.solwey.com/posts/the-role-of-evals-in-better-ai

05/26/2026

Agentic AI is moving faster than governance. That’s the real risk.

A recent article from Fortune, featuring insights from the Yale Chief Executive Leadership Institute, makes one thing clear: 2025 was about AI capability, but 2026 is about ex*****on. Companies are no longer experimenting with models, they are deploying AI agents that can take actions, interact with systems, and complete multi-step tasks. And that changes everything.

Unlike traditional AI, these systems don’t just generate answers. They make decisions, trigger workflows, and operate across business functions like banking, healthcare, retail, and supply chain. This means the risk is no longer about what AI says, but what it does.

Governance frameworks, policies, and regulations are still built for a world where AI is passive. They assume human review, static outputs, and clear boundaries between systems. But agentic AI breaks those assumptions because it acts autonomously, learns from outcomes, and continuously adapts. So companies are entering a new phase of risk without the infrastructure to manage it.

We are already seeing real deployment across industries. Financial institutions are using AI agents in credit workflows and operations, delivering measurable efficiency gains. Healthcare and supply chains are rapidly adopting similar models to automate decisions and optimize processes. The problem is not adoption. It’s control.

Agentic AI introduces a new layer of operational risk where actions can compound quickly, decisions can scale instantly, and errors are no longer isolated. Traditional governance models were not designed for systems that can act independently across multiple environments. This is why governance is becoming a strategic capability, not a compliance exercise.

The companies that will succeed in this next phase will not be the ones that deploy the most agents. They will be the ones that build the right control systems around them. Clear accountability, real-time monitoring, and defined boundaries for what AI is allowed to do will become foundational.

A great read on this topic: https://fortune.com/2026/05/02/agentic-ai-governance-framework-banking-healthcare-retail-supply-chain-yale-celi-sonnenfeld/

05/21/2026

The American manufacturing sector is facing a projected gap of 2.1 million unfilled jobs by 2030. While headlines often focus on automation or offshore competition, the real crisis is a human one: a widening "perception gap" and a need for a culture shift.

For decades, manufacturing was seen as a last resort - repetitive, rigid, and manual. But modern industry is fueled by innovation, high-tech problem-solving, and Industry 5.0 - a model where technology serves to elevate human potential rather than replace it.

While tax breaks and higher wages make great headlines, they don’t solve for the human element. Workers today stay where they feel valued. According to self-determination theory, retention is driven by three key needs:

-Autonomy: Giving employees control over their decision-making.
-Relatedness: Fostering a genuine sense of community and belonging.
-Competence: Investing in upskilling so workers can master their craft.

The roadmap for leadership to close the talent gap, manufacturers need to rethink their approach to human capital:

-Rethink Job Structures: Move away from rigid descriptions toward adaptive models that encourage initiative.
-Broaden the Talent Pool: Strengthen connections with vocational schools, create dedicated pipelines for veterans, and actively mentor underrepresented groups like women in leadership.
-Technology as a Partner: Use AI, data analytics, and robotics not to limit humans, but to handle the "dull, dirty, and dangerous," allowing workers to focus on creative strategy.

The industry is at a turning point. Manufacturers that invest in their people through learning and empowerment will be the ones that define the next chapter of industrial success.
The goal is to build a workplace where people don't just work - they thrive.

Read more in our Blog: https://www.solwey.com/posts/whats-driving-the-manufacturing-talent-shortage-and-the-path-forward

05/19/2026

Why plugging AI into broken data makes things worse

AI doesn’t fix data problems.
It amplifies them.

When data is fragmented, inconsistent, or poorly understood, adding AI doesn’t create clarity - it creates faster, more confident confusion. The outputs look polished, but they’re built on shaky foundations.

This is where many AI initiatives go wrong. Teams rush to deploy models before aligning on definitions, ownership, and data flow. AI then produces insights that don’t match reality, eroding trust and slowing decisions instead of improving them.

The irony is that the better the AI, the worse the outcome can be. High-quality models generate convincing answers even when the inputs are flawed - making errors harder to spot and easier to act on.

The companies that succeed don’t start with AI.

They start by fixing data flow, agreeing on what “truth” means, and designing workflows around decisions. Only then does AI become a force multiplier.
AI on broken data doesn’t just fail to help.

It makes the system louder, faster, and more wrong.

05/14/2026

Is what we’ve built still worth it, or do we need to start over?

It’s one of the most polarizing questions in any SaaS company. A full rewrite can be a massive risk - stalling your roadmap and burning through capital. But sometimes, clinging to a legacy system is even more expensive.

How do you know when to pull the trigger? Here are the three scenarios where a rewrite actually makes business sense:

1. The "Obsolescence" Liability If your tech stack is so old it’s facing security vulnerabilities or hosting issues that cost as much to patch as they would to rebuild, it’s no longer an asset. It’s a debt.
2. The Architecture is Blocking Growth If you built a monolith years ago but now need to integrate mobile apps or third-party APIs, retrofitting that logic might take longer than starting fresh. In these cases, the old system becomes a blueprint, not a burden.
3. Developer Retention This is the hidden cost of "bad" code. If your engineering team is constantly leaving because they’re tired of fighting a brittle, disorganized codebase, you aren’t just losing time - you’re losing institutional knowledge.

The Golden Rule: Don't Rewrite for "Scalability." Many founders rush into a rewrite because they’re worried about scaling to millions of users. The reality? Scalability is usually a "good problem" to have later. Unless the codebase is unmaintainable, you should focus on building features people want today, not theoretical traffic spikes tomorrow.

What’s the alternative? The smartest move is often a "micro-rewrite." Instead of a full teardown, you overhaul a specific subsystem or library. This strikes a balance between modernizing your tech and keeping the business moving forward.

Focusing on the architecture behind the code ensures that your technology remains a scalable asset rather than a bottleneck. Before you start over, define the mission: What are you keeping, what are you changing, and why?

The goal is to ensure your technology supports your growth rather than defining its limits.

Read more in our Blog: https://www.solwey.com/posts/code-rewrites-how-to-know-when-you-need-one

05/12/2026

AI models are choking on junk data.

That is not just a headline. It points to a deeper structural issue most companies are still ignoring. The real constraint on AI performance is no longer the model itself, but the quality of the data behind it.

A recent article from Fortune highlights how AI systems are increasingly struggling with low-quality, synthetic, and poorly labeled data. As models consume more of this noise, their outputs become less reliable and harder to control. In other words, the smarter the model becomes, the more sensitive it is to what you feed it.

This challenges the dominant narrative in the market. Most organizations are still focused on building or adopting more powerful models, assuming capability will solve the problem. In reality, poor data does not get fixed by better AI, it gets amplified.

We are also entering a phase where the problem compounds. A growing share of online content is now AI-generated, which means models are being trained on outputs created by other models. Over time, this creates a feedback loop where quality degrades instead of improves.

The real question is no longer how advanced your AI is. The question is how structured, clean, and governed your data is. Without that foundation, even the best models will underperform.

The companies that will win in this next phase will look different. They will invest in data discipline, not just AI experimentation. They will prioritize unified systems, clear ownership, and ongoing data quality as core capabilities.

At scale, AI is not a model problem. It is a data problem. And the organizations that understand this early will have a significant advantage.

Great Read: https://fortune.com/2026/05/03/ai-models-are-choking-on-junk-data/

05/07/2026

The phrase "custom code" usually brings to mind a dark room and endless lines of green text. In reality, it’s a business decision about ownership and limits.

Most companies start with great off-the-shelf tools like Shopify, Airtable, or Zapier. These are perfect for validation. But eventually, you hit a wall where you’re "babysitting" your tools instead of growing your business.

If your automation requires constant manual intervention, or your integrations are failing under load, it’s a sign that your technology is holding you back.

The Secret of Great Engineering Great software isn't built 100% from scratch anymore. It’s about balance. We use existing APIs for things like payments or SMS so we can focus the "custom" energy on the features that actually define your unique value.

3 Mistakes to Avoid When Going Custom:

1. The "One-Man Army" Trap: Relying on a single freelancer is a massive risk. If they go MIA, your project dies. You need a team for continuity and breadth of expertise.
2. Overbuilding Too Soon: You don’t need a system that supports millions of users on day one. Build for the load you have, but plan the architecture for the growth you want.
3. Seeking Perfection: Start with an MVP. Building incrementally is cheaper, faster, and allows you to pivot based on real user feedback.

The goal is to ensure your technology supports your growth rather than defining its limits.

More in our Blog: https://www.solwey.com/posts/when-custom-software-solves-real-business-challenges

05/05/2026

The difference between AI features and AI systems

Most companies say they’re “using AI.”

What they usually mean is: they’ve added AI features.

An AI feature solves a narrow task. It summarizes a report, suggests a next step, or generates content inside an existing tool. Useful, but isolated. When the workflow ends, the AI stops adding value.

An AI system is different. It connects data across tools, fits into how decisions are actually made, and keeps working over time. It doesn’t just produce output - it supports action, learning, and feedback.

That’s why AI features feel impressive in demos but rarely move the needle. They don’t change how work gets done. AI systems do.

The companies seeing real ROI aren’t asking, “Where can we add AI?”
They’re asking, “What decisions or workflows should AI support end-to-end?”

AI features make software smarter.
AI systems make organizations better.

And the difference matters more than most teams realize.

04/30/2026

Thinking about applying to a startup accelerator?
It’s a massive commitment, and the "investor intros" are only half the story.

Most founders see accelerators as a shortcut to funding, but the real ROI is often the forced evolution of your business.

These programs condense years of networking and trial-and-error into a few intense months. If you’re a first-time founder, that hands-on education is gold - it helps you navigate the legal and financial pitfalls that usually kill young companies.

But here’s the reality check: The "cost" isn't just the equity.
The biggest risk is time. Every hour you spend in a workshop is an hour you aren’t building your product or talking to customers. If the program isn't a perfect fit for your industry, it can quickly become a distraction. You have to be ruthless about protecting your focus.

How do you actually get in?

Accelerators aren't looking for "ideas" - they’re looking for ex*****on. To stand out, you need a tangible Minimum Viable Product (MVP) and a team that’s clearly all-in. They want to see that you’ve already started the journey and just need their fuel to go faster.

More in our blog: https://www.solwey.com/posts/the-real-value-and-risks-of-joining-a-startup-accelerator

04/28/2026

Wait, what exactly is an AI Agent?

If you’ve found yourself nodding along in meetings while secretly Googling AI jargon, you aren’t alone. Even as we move through 2026, the terminology is evolving faster than most businesses can keep up with.

At Solwey, we believe that complexity is the enemy of progress. You shouldn't need a PhD in Computer Science to understand the tools that are supposed to be scaling your business.
We’ve synthesized the latest "AI Glossary" (shoutout to the team at TechCrunch) into the 5 core concepts every leader actually needs to know right now:

1. AI Agents vs. Chatbots: A chatbot talks to you; an Agent works for you. Think of Agents as autonomous team members that can book travel, file expenses, or maintain code without constant hand-holding.
2. Chain of Thought: This is why your AI suddenly seems "smarter" but slower. It’s breaking problems into logical steps before answering - much like a human using a scratchpad.
3. Hallucinations: The industry term for when AI confidently makes things up. At Solwey, we tackle this through Fine-Tuning - narrowing the AI’s focus to your specific domain to ensure accuracy.
4. RAMageddon: There is a global shortage of memory chips (RAM) because AI labs are buying them all. This is driving up hardware costs and making efficient software architecture more important than ever.
5. Tokens: The "currency" of the AI world. Every word processed costs tokens. Understanding your "token burn" is the key to managing your AI ROI.

The goal isn't just to use AI - it's to use it predictably and profitably. Whether we’re talking about Inference (running the model) or Distillation (making it faster/cheaper), the focus should always be on the business outcome, not the buzzword.

Insightful piece: https://techcrunch.com/2026/04/12/artificial-intelligence-definition-glossary-hallucinations-guide-to-common-ai-terms/

04/23/2026

Thinking about AI for your business? Most people are currently stuck in the "User" lane, but the "Builder" lane is where the real competitive advantage lives.

I just finished an insightful piece on the shifting landscape of AI implementation. The takeaway is clear: Using AI (off-the-shelf tools) boosts productivity, but Building AI (custom systems) creates value.

Here are the 3 biggest shifts happening in the AI stack right now:

1. The Data Foundation
Generative AI thrives on unstructured data - the PDFs, emails, and call logs that have been sitting in "digital junk drawers" for years. The winners won’t be those with the best algorithms, but those who modernize their infrastructure to make this data accessible and trustworthy.

2. The Move to Hybrid Cloud
Public cloud is great for starting, but the "GPU tax" is real. We are seeing a massive trend toward hybrid infrastructure. Companies are moving high-volume inference tasks back on-premise to save costs and maintain control over their most sensitive IP.

3. Problem First, Tool Second
As the saying goes, "If I had an hour to save the world, I’d spend 55 minutes defining the problem." Many businesses rush to buy a tool before they understand the friction point. Whether it’s identifying underwater mines or predicting genetic mutations in tumors, the most successful AI applications are purpose-built for a specific domain.

AI isn't a one-size-fits-all solution. It’s an architecture. To move from "generic assistant" to "domain expert," your technology stack needs to be as unique as your business data.

Read more in our Blog: https://www.solwey.com/posts/why-the-right-ai-stack-is-your-next-strategic-move

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