Think AI could help your business but not sure where to start? In the first call, we'll map what makes sense, what doesn't, and what the realistic next step is.
I usually respond within 24 hours.
I help companies deploy AI into production — without chaos, unnecessary costs, or dead ends.
Lukas Hruby
- For 15+ years, I've helped companies build digital products that keep working as they grow. Over 8.5 years as CTO, I led delivery from first versions to operations in 100+ countries.
- I don't just help with AI ideas. I help deploy it so it's reliable, measurable, and safe in daily operations.
Whether you want faster customer communication, better lead handling, or less manual work for your team, I help you choose the right approach and put it into practice.
I combine CTO-level engineering leadership with extensive experience in customer-facing work, international deployments, hiring, and operations.
Location: Prague / Online
When I can help
Problems I solve best — because I've solved them before, at scale.
You want to use AI in your company without breaking what already works. I'll help you integrate it so it saves team time, improves customer service, and doesn't slow operations down.
You don't want an expensive demo toy. You want something that genuinely helps your business. We'll define clear outcomes and measure whether AI is actually delivering value.
You need systems that stay fast without becoming too expensive. I design solutions that scale while keeping operations predictable.
You're not sure where AI makes sense in your business. I'll help you pick 1–2 areas where it can deliver quick, practical impact.
You need someone who understands both business and technology. I'll help you make faster decisions and avoid expensive dead ends.
When AI works with video or cameras, it must be fast and reliable. I have hands-on experience with real-time systems running at scale.
Selected Results
- I helped build a system that automatically evaluates video from 1000+ cameras. Customers cut manual checking and saved a significant share of manual work.
- I design AI solutions that keep working after launch, not just during demos.
- I help teams set up quality control so it's clear whether AI is performing well, not just "feels fine".
- I can significantly reduce operating costs without sacrificing service speed or quality.
- I led the growth of engineering teams from first hires to stable delivery that is faster and more predictable.
What I offer
2–3 weeks
Where AI makes sense in your business and how to introduce it
Output:
- where AI can bring the biggest value
- what to tackle first
- how to do it safely and practically
- how you'll know it's working
4–8 weeks
First working AI solution for real operations
Output:
- first usable version in real workflows
- quality and error control
- cost monitoring
- clear boundaries for what AI should and should not do
Ongoing (monthly)
Ongoing guidance for companies that want to grow without expensive mistakes
Output:
- clearer technical decisions
- lower risk of bad investments
- support with hiring choices
- stronger development workflow
Transparency: I'm a co-founder/ex-CTO of GoodVision, so I'll always disclose any potential conflict and suggest alternatives when appropriate. If an existing product is a better fit than custom development, I'll say so openly.
In the first call, we'll identify where AI can truly help your business — and where it would just waste money.
Where you are today and how far I can take you
Not every company needs a fully autonomous agent. I'll help you take the right next step — and recognize when it's time to stop.
0
Where companies are today
Manual processing
Humans do everything by hand — from looking up information through drafting replies to making the final call.
Human
AI
0% AI
1
I can help here
AI assistant
The model drafts replies and looks up information. A human reads, checks, and approves everything — AI is a fast helper, not a decision-maker.
Human
AI
25% AI
2
I can help here
Constrained workflow
The model strictly follows clear steps defined for your process. A human always approves at the end — no surprises.
Human
AI
50% AI
3
I can help here
Agent with tools
The agent picks where to look for information and which tools to use. It hands a finished package to a human for review and final approval.
Human
AI
75% AI
4
I can help here
Partially autonomous agent
The agent has authority to make smaller decisions on its own. Larger decisions and unclear situations get escalated to a human — the rules define exactly where the line is.
Human
AI
90% AI
Every company is different — and so is the right level of AI.
Some get the most value from an AI assistant, others need an agent with tools or a partially autonomous system. I'll help you identify the right level for you and design a solution at the scope that fits.
AI assistants in practice
Anonymized examples from real projects.
AI assistant that pre-qualifies new legal inquiries and frees up senior team's time
Context: Law firm receiving new inquiries from multiple channels — often incomplete, senior staff spending time on initial evaluation.
Problem: Poorly framed questions or insensitive communication drive clients away. Key information is missing for initial assessment.
What we delivered: The assistant asks clients for key details, prepares a short summary, and passes lawyers only what is worth handling.
Result: Less time spent on first-pass sorting, less repetitive rewriting, and faster responses to new prospects.
Safety: Sensitive data minimization, audit log, role-based access, escalation to a human when uncertain
AI-assisted process via SMS and WhatsApp that speeds up customer and supplier communication
Context: B2B2C model in commodities — many suppliers, many customers, many steps. Users don't want to onboard to another tool.
Problem: Coordination chaos, inconsistent supplier data, communication delays.
What we delivered: The system guides people step by step in familiar channels, collects key information, and reduces manual coordination.
Result: Significantly faster turnaround from inquiry to supplier selection. Less manual coordination, higher conversion through familiar channels.
Safety: Input validation, clear rules for customer vs. supplier communication, auditable process
AI helper that explains complex options in plain language
Context: Benefits comparison tool — users have many preferences but can't translate them into decisions. A data table isn't enough.
Problem: Users see the data but don't know what it means for them. A personalized perspective is missing.
What we delivered: AI helps users understand what matters for them and translates complex data into everyday language.
Result: Higher clarity and user confidence. Better engagement — more completed comparisons and higher conversion.
Quality: Continuous tuning on real data and feedback, transparent recommendation explanations
Case Studies
Real-time Computer Vision for CCTV (>100 countries)
Context: GoodVision needed real-time video analysis across multiple locations, serving customers in >100 countries.
Problem: Processing 1000+ camera streams with sub-second latency, scaling to handle peak loads, maintaining 99.9% uptime.
What I did: Designed edge processing architecture running on NVIDIA Jetson devices, built model serving infrastructure achieving 99%+ detection accuracy, implemented MLOps pipeline for continuous deployment, optimized for GPU economics and edge deployment.
Business result: Reliable production operation under high load, sub-second latency in selected scenarios, and meaningful reduction in operating costs.
Stack: AWS, AWS IoT, Docker, NVIDIA Jetson, Jetpack, PyTorch, TensorRT
Cost & performance architecture for GPU workloads
Context: ML workload requiring significant GPU compute with cost and latency constraints.
Problem: Balancing GPU costs, latency requirements, and scalability for variable workloads.
What I did: Architected hybrid cloud solution (on-demand + spot instances), implemented auto-scaling, optimized model inference, established cost monitoring and alerting.
Business result: Significant cost reduction while maintaining required latency and stable operations during substantial traffic spikes.
Stack: AWS EC2, ECS, CloudWatch, custom cost optimization
Engineering team scaling (0 → 20)
Context: Needed to scale engineering from founding team to support growth across >100 countries.
Problem: Hiring quality engineers, establishing technical culture, building processes for distributed team, maintaining delivery speed.
What I did: Built hiring process and technical interviews, established architecture principles, implemented CI/CD and code review practices, created onboarding system, set up cross-functional collaboration.
Business result: More stable delivery across time zones, faster release throughput, and systematic technical debt control.
Stack: Hiring processes, technical culture, architecture governance, CI/CD, cross-functional processes
About me
AI in day-to-day operations
I help identify where AI makes business sense and follow through all the way to live operations.
Software Architecture & Scaling
I design systems to stay reliable, handle growth, and avoid unnecessary operating costs.
Computer Vision & Real-time
I have experience with demanding AI systems that must be fast and reliable in real-world conditions.
Technology leader with hands-on customer and operational experience
Over the last several years, more than 50% of my work has been customer-facing and operational. Alongside leading engineering, product, and AI development, I represented the company in international business negotiations, supervised real-world deployments, organized trade fair participation and executive presentations, hired technical and marketing talent, and managed operational topics such as office selection, planning, and day-to-day company operations.
This combination allows me to connect board-level business priorities with practical technical delivery — from first customer conversation to production rollout.
- Led in-person international business negotiations with customers, including markets in the US and Asia.
- Supervised on-site product deployment and customer delivery, including projects in Europe, the US, and Asia.
- Represented the company at industry fairs, presentations, and customer-facing events.
- Prepared and delivered technical and product presentations for business stakeholders.
- Participated in hiring for technical and marketing roles.
- Managed operational responsibilities including office selection, planning, setup, and day-to-day operations.
- Bridged business, product, and engineering priorities from sales discussions through production deployment.
Where I bring the most value
- Turning complex AI and software systems into business value for enterprise customers.
- Leading technical discovery, solution design, and deployment supervision.
- Building trust with business and technical stakeholders across cultures.
- Connecting customer needs with product and engineering priorities.
- Hiring and leading technical teams in fast-moving environments.
- Taking responsibility not only for architecture, but also for delivery and operations.
When to look elsewhere
- I'm not the right fit for low-cost bulk coding. My biggest value is helping choose the right direction and avoid expensive mistakes.
- If the work is a large implementation, it makes sense to involve your team or an agency. I help decide what to build, how to design it, and how to keep quality under control.
- I don't sell one favorite technology. I help choose the approach that makes sense for your specific business.
I deliver the most value where business, product, and architecture need to be aligned. Still not sure? Book a free 30-min call — no commitment, we'll figure out if it's a fit.
Let's find out how AI can help you.
Currently accepting select engagements. Let's discuss your architecture and a practical plan for the next steps.