AXON Soft

Gartner prediction in 2025 was that 40% of enterprise applications will include task-specific AI agents by end of 2026.

According to OutSystems' 2026 State of AI Development report based on 1,900 global IT leaders, 96% of organizations are already using AI agents in some capacity. Yet Deloitte found only 11% are running them in full production.

Main blocker = integration with existing systems.

This is precisely why we've been deliberate about where we stand: we are AI-tooling focused, not AI-agentic by default. That's not a limitation, but a considered position.

Our observations on agents in 2026:
✔️ Agents work well in bounded, verifiable workflows like code generation, test ex*****on, structured data processing
✔️ They struggle where governance, accountability, and auditability matter most (which is exactly where our clients in banking, defense, and logistics operate)
✔️ Many organizations that rushed to agents are now rebuilding the governance layers they’ve skipped

Our approach: we use where it demonstrably improves output quality and delivery speed. We add when it makes sense. We don't add complexity just to follow a trend or to window-dress innovation KPIs.

A narrative has taken hold that AI is replacing software engineers. Let’s agree to disagree.

Spending less time on boilerplate and more time on decisions that matter (i.e. architecture, system design, edge cases, security boundaries) is not replacement. It’s evolution.

CIO research published this year put it well: the engineer of 2026 is "orchestrating a dynamic portfolio of AI agents, reusable components and external services." Their value lies in thinking and rigorously validating output.

It’s not replacement. It’s upskilling. It’s focusing on what truly matters.

Over the years, at AXON Soft we’ve developed custom solutions for financial organizations worldwide, including AI copiloting and other AI projects. This experience has shown us that successful implementation depends on much more than the AI technology itself.

That is why we do not start with “What can AI do?” but with “Where can an AI copilot create measurable value without compromising security, compliance, reliability, or human accountability?”

We talk about in financial services in our recent blog post 👉 https://axon-soft.com/ai-copiloting-in-banking-human-expertise-meets-intelligent-software/

A drone flies over a hunting ground, a border zone, or a logistics hub. It captures thousands of frames per hour.

Raw footage is useless without insight. That's where computer vision systems come in and this is one area we've focused over the past few years.

At AXON Soft, we build the software layer that:
⏳ Processes drone imagery in real time or near-real time
🔎 Detects patterns, anomalies, and objects across large geographic areas
📊 Turns visual data into structured, actionable intelligence

We've applied this in civil and defense contexts, monitoring areas where human presence isn't feasible or safe.

But the same technology stack applies across retail (inventory), logistics (yard management), agriculture, and infrastructure inspection. The vertical changes. The underlying capability doesn't.

If you're building a product that involves visual data at scale, we should talk about what's possible today, not in two years.

There's a distinction worth making clearly:
1️⃣ An AI product is built around an AI capability (recommendation engine, generative interface, vision system). The AI is the product.
2️⃣ Adding AI to your product means identifying where machine intelligence adds value. The product is still yours. AI can help make it better.

Most product companies we work with are in the second category.

We're pragmatic about AI. Not every scenario needs it. But when it fits, it changes what's possible.

In banking and fintech, the window between concept and compliance deadline is narrow, regulatory pressure is high, and customer expectations don't pause while teams debate architecture.

For over two decades, we've worked with financial institutions and fintech companies and built digital products for environments where "good enough" isn't acceptable.

We’ve learned that:
👉 Needs and opportunities in finance are more complex; ambiguity is expensive
👉 QA cannot be an afterthought; it's a regulatory and reputational necessity
👉 Cloud and data architecture decisions compound over time

If you're a product company in banking or finance looking for a delivery partner who understands your risk landscape, we'd welcome a conversation.

What does it actually mean to be an AI-driven software company?

It doesn't mean replacing developers with agents.

It means that (almost) every step of the product delivery cycle - from detailing specs to writing code, running QA, and planning sprints - is augmented by AI that actually understands the context.

At AXON Soft, AI copiloting is not a side experiment but a conscious choice in how we work:
✔️ Specs are assessed and risk-flagged before code is written
✔️ QA runs AI-generated tests that adapt as the product evolves
✔️ Project management uses predictive planning

How this helps: faster delivery, fewer surprises, improved quality. For our clients, this translates into one thing: more shipped features per quarter, less rework.

We've been building software for 25+ years. AI didn't change what we do. It changed how well we do it.

There's a difference between a vendor who executes on tickets and a partner who thinks about your product.

Product companies (especially those scaling fast) don't just need more developers. They need a team that understands:
☑️ Where architectural decisions affect business operations
☑️ How to sequence features to reduce risk, not just to match the roadmap
☑️ When to build in-house and when to leverage existing tooling (including AI)

We work with product companies because that's one conversation we're built for.

Our consultancy layer is part of the engagement. We come in, assess the needs, flag the risks, and propose the most efficient path. Then we build it.

25+ years.
100+ engineers.
Clients across many verticals, worldwide.

We're not the biggest.
We're the ones who care about the product like it's ours. 💯

The CTO wants microservices.
The CPO wants new features yesterday.
The CFO wants ROI by Q3.
Digital transformation sits at the intersection of all three.

When others only speak one of those languages, we enter transformation engagements differently:
1️⃣First, we assess. What's the current state of the product, the team, and the delivery process? Where is the risk?
2️⃣Then, we propose the path. Not the most technically impressive one, but the most efficient and least disruptive one.

We don't believe in big-bang migrations.
We believe in sequenced, validated progress.

Our role: part engineering partner, part strategic advisor, full accountability. 💯