Solwey

Combining data, design and usability, Solwey Consulting delivers completely customized software solutions that focus on customer satisfaction.

[Solwey @SolweyCo]

The "large-or-bust" era of AI is hitting a plateau. For a long time, the narrative was simple: more parameters equals more value. But in the back offices and data centers of forward-thinking companies, the move toward Small Language Models (SLMs) is gaining serious momentum.

The future of production-grade AI isn't about raw power; it’s about strategic fit.

An SLM is defined by its practicality. It’s a model that can run efficiently on a single enterprise-grade GPU. This removes the massive overhead of networking expensive processors just to handle one workload. For a business, this means lower latency, reduced costs, and total control over data privacy.

Choosing a model shouldn't start with size; it should start with value. The best approach is to test your idea on the most powerful frontier model available via API first. If the smartest model on earth can’t solve your problem, a small one won't either. But if it can solve it, the real engineering begins: migrating that logic to a right-sized SLM tuned specifically for your needs. Why pay for a model that can translate 150 languages when you only need it to analyze financial spreadsheets?

We are also moving away from the "one-agent-to-rule-them-all" approach. The new standard is a service-oriented architecture: a workforce of specialized SLMs. You might have one agent for data analysis, one for code generation, and another for client communication. By breaking a monolith into specialized services, you drastically cut computational costs while increasing accuracy.

Fine-tuning used to be a niche skill for ML PhDs, but new frameworks and synthetic data generation are making it accessible to general developers. This democratization is the final piece of the puzzle - it allows you to turn a general-purpose model into a proprietary asset tailored to your unique business logic.

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

Read more in our Blog: https://t.co/EpMMQ0vM5c

[Solwey @SolweyCo]

When not to use AI agents

AI agents are powerful - but they’re not always the right answer.

You shouldn’t use AI agents when the problem isn’t well defined. If success criteria are vague or constantly shifting, an agent will automate confusion instead of reducing it.
You also shouldn’t use agents when the data is unreliable or fragmented. Agents act on what they see. If the inputs are wrong, they’ll move fast in the wrong direction - and make it harder to spot the issue.

Another red flag is when human judgment is still the primary value. Decisions involving nuance, ethics, or high-stakes tradeoffs need people in the loop. Agents can prepare information, but they shouldn’t own the outcome.

Finally, if there’s no owner for the workflow, don’t add an agent. Without accountability, agents become background automation that no one trusts or uses.

The best use of AI agents isn’t to replace people.
It’s to support clear decisions, clean data, and well-owned workflows.

If those aren’t in place yet, AI agents won’t help - they’ll just make the mess more efficient.

[Solwey @SolweyCo]

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://t.co/Xwj0ftiZxy

[Solwey @SolweyCo]

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 execution. 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://t.co/10YIZm3qkN

[Solwey @SolweyCo]

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://t.co/geSucif7Tt

[Solwey @SolweyCo]

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.

[Solwey @SolweyCo]

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://t.co/6rymPXeFZB

[Solwey @SolweyCo]

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.

[Solwey @SolweyCo]

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 execution. 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://t.co/oWi9zmK0yU

[Solwey @SolweyCo]

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://t.co/CAPoUZZW6Y

[Solwey @SolweyCo]

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://t.co/OgA4CaTvbV

[Solwey @SolweyCo]

Why “AI literacy” is now a leadership skill

AI literacy isn’t about knowing how to build models or write prompts.

It’s about understanding what AI can and can’t do - and making better decisions because of it.

Leaders don’t need to be technical experts, but they do need to know where AI adds leverage, where it introduces risk, and how it changes the way work gets done. Without that understanding, AI initiatives either stall, get overhyped, or quietly fail.

The gap shows up quickly. Teams adopt tools, but workflows don’t change. Data exists, but decisions don’t improve. Leaders ask for “AI solutions” without being clear on the problem that actually needs solving.

AI-literate leaders do something different. They ask sharper questions. They focus on outcomes instead of features. They understand that AI works best when paired with human judgment and redesigned workflows - not dropped in as a shortcut.

In the AI era, leadership isn’t about having all the answers.
It’s about knowing how to guide humans and machines to work better together.

And that makes AI literacy less about technology - and more about leadership.

[Solwey @SolweyCo]

Modern manufacturing doesn’t run on spreadsheets and whiteboards anymore.

It runs on MES - Manufacturing Execution Systems.

MES sits in the middle of the factory’s digital nervous system. It connects business systems like ERP with the realities of the shop floor - making sure production actually reflects business priorities.

For operators, MES answers simple but critical questions:

• What should I work on next?
• Which materials and recipes should I use?
• What quality checks are required?

For leadership, it answers much bigger ones:

• Where did this raw material come from?
• What happened during production?
• Why did this process slow down?
• How are KPIs like OEE trending?

But MES is evolving fast.

Today’s systems go far beyond execution. With AI, IoT, and real-time analytics, MES is becoming the intelligence layer of manufacturing.

It can now help manufacturers:

• predict equipment failures before they happen
• detect quality issues in real time
• optimize production schedules
• connect supply chain signals directly to factory operations
• balance efficiency with sustainability goals

In other words, MES is no longer just tracking what happened on the factory floor.

It’s helping companies decide what should happen next.

And as manufacturing becomes more complex - with global supply chains, labor shortages, and pressure to “sweat the assets” - that shift from visibility to intelligence is becoming essential.

Read more in our blog: https://t.co/ghlVkmgATH

[Solwey @SolweyCo]

Is your AI strategy a "transformation" or just an "experiment"?

Most engineering teams are stuck in Stage 1 or 2: individual developers using Copilots in silos. The result? Faster coding, but uneven delivery and fragmented workflows.

According to a recent piece by Vention on TechCrunch, the real competitive advantage doesn't come from better prompt - it comes from a staged maturity model that moves AI from an assistant to a system-level architect.

The 5 Stages of AI SDLC Maturity:

Individual Exploration: Pockets of creativity, but no scalable impact.
Consistent Team Usage: Shared tools for repetitive tasks (docs, refactoring).
Integrated AI Workflow: AI becomes context-aware, utilizing structured specs to unify project knowledge.
Orchestrated AI Development: Multi-agent systems coordinate the full feature lifecycle.
AI-Driven Development: Autonomous execution is the default; developers shift to being strategic reviewers and governors.

You don’t get transformation by enabling AI at the edges. You get it by building an architecture where the entire lifecycle - from planning to testing - works together.

As Sergei Kovalenko (CEO of Vention) puts it: "Until the impact of AI is visible in delivery metrics and financial results, it’s still an experiment."
The goal isn't just to write code faster; it's to reduce "defect-driven work" and free up engineers to focus on high-value system design and innovation.

Great read: https://t.co/mipNQvhQfa

[Solwey @SolweyCo]

One of the biggest mistakes mid-market companies make with custom software isn’t technical.

It’s treating it like a typical IT project.

For SaaS startups, building software is the business. They ship, learn, break things, and iterate constantly. Mistakes are expected.

But for manufacturers, retailers, logistics companies, or service providers, software often supports mission-critical operations. When a system fails, it doesn’t just frustrate a product team - it disrupts deliveries, customer experience, and revenue.

That’s why the real risks usually appear long before development even starts.

The most common ones:

• Lack of stakeholder alignment – Sales, operations, and IT often expect completely different outcomes.
• Unclear problem definition – Companies start building before fully understanding what success actually looks like.
• The build vs. buy trap – Teams assume a platform can be customized, only to discover months later it cannot support critical requirements.

By the time these problems surface, the project is often 80% complete - and fixing them becomes incredibly expensive.

The companies that succeed approach software differently.

They slow down before building.
They validate assumptions early.
They align stakeholders across the business.
And they test ideas through prototypes before committing major budgets.

In software projects, speed matters. But clarity matters more.

Read more in our blog: https://t.co/YBQMV8wpEj

[Solwey @SolweyCo]

What an AI agent actually is (and isn’t)

An AI agent isn’t just a chatbot with a fancy name.

At its core, an AI agent is a system that can observe context, make decisions, and take action across tools - not just generate text. It can pull data, apply logic, trigger workflows, and adapt based on outcomes. That’s the agent part.

What it isn’t:
It’s not autonomous magic. It doesn’t “know” your business out of the box. And it doesn’t replace human judgment. Without clear goals, clean inputs, and defined guardrails, an AI agent is just another automation script with a UI.

The real value of agents shows up when they’re paired with people. Agents handle the repeatable work - gathering information, monitoring signals, preparing options - while humans provide context, priorities, and final decisions.

The companies getting this right aren’t asking, “How powerful is the agent?”
They’re asking, “What decision or workflow should this agent support?”

Because an AI agent isn’t a replacement for humans.
It’s a multiplier - when it’s designed to work inside the right system.

[Solwey @SolweyCo]

One of the hardest decisions SaaS founders face isn’t building the product.

It’s figuring out how to fund it.

Many founders immediately think about venture capital because that’s what dominates startup headlines. But in reality, most startups are funded in far less glamorous ways.

The early stages are often a mix of:

• Bootstrapping – using personal savings or reduced salary
• Customer funding – getting paying users as early as possible
• Grants or soft funding – government or innovation programs
• Small loans or debt financing – extending runway without giving up equity
• Side income or consulting work – reinvesting earnings back into the product

There is no universal formula.

The right funding strategy depends on the stage of the company, the type of product, and the founder’s tolerance for risk.

Bootstrapping can build discipline and force a deep focus on customer value.
Customer-funded growth proves demand early.
Debt can accelerate progress without diluting ownership.
And sometimes consulting work keeps the lights on long enough for the product to take off.

What matters most is timing.

One of the biggest mistakes founders make is approaching investors only when they’re running out of money. Fundraising works best when you still have runway, traction, and options.
Because the goal isn’t just to raise capital.

It’s to build a company strong enough that you can raise money on your terms - or choose not to at all.

Startup funding is rarely a straight line. The founders who succeed are usually the ones who stay flexible, combine multiple funding strategies, and focus relentlessly on building something customers actually want.

More in our blog:

[Solwey @SolweyCo]

There’s a growing trend of companies using AI coding tools to “vibe code” their own CRMs instead of relying on established platforms. For smaller teams with very specific workflows, the appeal is obvious. AI makes it possible to build something quickly, customize it exactly to how the sales team operates, and potentially avoid the cost and complexity of large enterprise systems.

But a CRM is not just another internal tool. It’s one of the most critical systems inside a company because it holds the most sensitive business data: customer relationships, pipeline information, revenue forecasts, contracts, and communication history.

When a system like that is built quickly with AI-generated code, the bigger question becomes who is responsible for the parts that aren’t immediately visible.

Enterprise software vendors spend years building protections that many teams never have to think about - permission structures, audit trails, encryption, data governance, reliability at scale, and recovery processes when things break. Without those layers, a custom CRM can easily become a high-stakes gamble, especially for companies that depend on their data to run sales and operations.

AI is making software creation dramatically easier, and that will absolutely change how companies build internal systems. But working code is only one part of the equation. The real challenge is building systems that are secure, reliable, and trustworthy enough to run a business on.

The question isn’t whether companies will build more of their own tools. They will. The question is whether those tools will be built with the same level of protection and discipline that critical business systems require.

Great read: https://t.co/cs0SyLAbR0

[Solwey @SolweyCo]

For a long time, building software started the same way: long workshops, abstract discussions, and weeks of translating business ideas into technical documents.
But something is changing.

AI is moving the most important part of software development to the very beginning of the process - discovery.

Instead of starting with vague conversations and blank documents, teams can now turn business discussions into visual prototypes and early product concepts within hours.
This changes how companies think about software.

Many organizations struggle not because technology is hard, but because the problem itself isn’t clearly defined. Misalignment, unclear expectations, and misunderstood workflows are still the biggest reasons projects fail.

AI-driven discovery helps solve that.

It allows teams to:

-translate conversations into wireframes and prototypes quickly
-reveal gaps in workflows earlier
-align stakeholders faster
-uncover opportunities that might never surface in a traditional workshop

For many businesses - especially in industries that historically relied on off-the-shelf SaaS tools - this is opening a new door.

Instead of forcing their workflows into generic systems, they can start asking a different question:

What would our business look like if the software actually matched how we operate?

The real impact of AI in software development may not be writing code faster.

It may be helping companies finally define the right problem to solve.
Because once that clarity exists, execution becomes much easier.

More in our blog: https://t.co/KFnEhPSxgP

[Solwey @SolweyCo]

What companies get wrong about AI ROI

Most companies look for AI ROI in the wrong place.

They ask:

·      Did we deploy the model?
·      Are people using the tool?
·      Is the output accurate?

Those are table stakes - not ROI.

Real AI ROI shows up when decisions get faster, cleaner, and more consistent. But many initiatives stall because AI is treated like a feature instead of a system.

Models are added on top of fragmented data, dropped into workflows that were never redesigned, and expected to deliver value without changing how teams work.

Another common mistake is expecting immediate, linear returns. AI impact compounds. The first gains often come from removing manual work and friction. The bigger wins come later, when teams start trusting AI outputs and building new habits around them.

The companies that succeed don’t ask, “What’s the model’s accuracy?”
They ask, “What decision is this improving - and who owns acting on it?”

Until AI is tied to real decisions, clear ownership, and measurable outcomes, ROI will always feel disappointing - no matter how advanced the technology is.