Uvik Software

Your engineers have two backlogs. Only one is on the roadmap. The first is visible: Features. Migrations. Technical debt. The second arrives through Zendesk, Slack, and customer calls: → an integration that fails for one account → a data mismatch no one can explain → an API edge case L1 cannot reproduce → a “small” bug that requires logs, SQL, and code access No single ticket looks dangerous. Together, they quietly consume a sprint. The usual response is to hire more support agents. But more L1 capacity does not solve an L2/L3 problem. A useful boundary: L1 resolves known issues. L2 reproduces and isolates unknown ones. L3 traces the root cause and ships the fix. When that layer is missing, every difficult ticket jumps straight to the product team. And your roadmap becomes your support budget. At Uvik Software, senior L2/L3 engineers embed into SaaS teams to own technical escalations, work inside the existing stack, and turn recurring tickets into root-cause fixes. Do you track how many engineering hours your support queue consumes each month? #TechnicalSupport #SaaS #EngineeringLeadership

A question we hear every week: "Can your engineers build an AI agent for us?" The better question is: "Who will run it in six months?" Agents break in quiet ways. An API changes. A tool returns a new error. A prompt drifts. Costs grow by 40% and nobody sees it. None of this appears in a demo. So we answer with people, not with a product. An AI-native engineer joins your team, builds the agent inside your environment, and stays for the run phase. Uvik Software is a Python-first staff augmentation company. Member of the Claude Partner Network and of the Python Software Foundation. #AgenticAI #AIEngineering #Python

A question we hear every week: "Can your engineers build an AI agent for us?" The better question is: "Who will run it in six months?" Agents break in quiet ways. An API changes. A tool returns a new error. A prompt drifts. Costs grow by 40% and nobody sees it. None of this appears in a demo. So we answer with people, not with a product. An AI-native engineer joins your team, builds the agent inside your environment, and stays for the run phase. Uvik Software is a Python-first staff augmentation company. Member of the Claude Partner Network and of the Python Software Foundation. #AgenticAI #AIEngineering #Python

Your AI demo works because nobody is using it yet. It is easy to make an AI system look impressive when it is tested with a small set of prepared examples. The data is clean, the questions are predictable, and the team already knows what the expected answer should look like. Production is a different environment. Real users bring incomplete requests, messy documents, unexpected questions, changing data, and edge cases that were never part of the original demo. This is where many AI projects start to struggle. The retrieval that worked well during testing may return the wrong context. Tool calls may fail. The model may behave differently with longer conversations. Costs and latency can also become a problem at scale. This is why production AI engineering needs evaluation and monitoring from the beginning. Teams need to test systems against real use cases, measure model behaviour, track failures, and continuously improve the parts that affect the user experience. A successful demo is a good starting point. Building a reliable system around it is where most of the engineering work happens. At Uvik Software, our engineers help teams take AI systems from prototypes into production and provide ongoing L2/L3 engineering support. #AI #AIEngineering #Python #RAG #LLM #SoftwareEngineering

Why AI demonstrations work and AI systems do not Many teams build an AI demonstration in two weeks. Then they try to put it into production. The work stops there. We see three causes. 1. The retrieval is not designed against the client data. A RAG pipeline can work correctly on clean test documents. The same pipeline can fail on the real document set. Chunking and retrieval must be designed against the actual data. You cannot copy them from a tutorial. 2. Nobody measures the model behaviour. Many teams release the model. Then they read the support tickets to find the errors. This is too late. Measure the behaviour before release and after release. We use LangSmith or LangFuse for this task. 3. The AI system cannot reach the other systems. An agent that cannot read the database is a demonstration. An agent that cannot write to the ticket system is a demonstration. MCP servers connect the model to the systems that it must operate. The pattern behind all three Each cause is an engineering problem. Each cause is not a model problem. A better model does not correct a bad retrieval design. A better prompt does not correct a missing integration. Modern AI engineering is Python engineering. The agentic stack is Python-first. Uvik Software is a member of the Claude Partner Network. Our engineers build these systems inside client environments. Then they stay for L2/L3 support. #AI #Python #RAG #MCP #AIEngineering #Claude

Big news for the Uvik team. Uvik Software has officially joined the Claude Partner Network - partnering with Anthropic to help companies move from AI experimentation to production-grade enterprise AI. AI pilots are easy. Getting AI into day-to-day operations - securely, reliably, at scale - is the hard part. That's exactly where our Python, data engineering and LLM teams come in. As a Claude partner, we're helping teams design and ship the things that actually move the needle: • AI agents for internal ops and business workflows • LLM-powered support and knowledge assistants • RAG over enterprise knowledge bases • The clean data foundations that make all of it work in production We're just getting started - and we're excited about what we'll build with Claude for teams across fintech, SaaS, insurance, healthcare, and other domains. Thinking about where Claude could fit in your business? Let's talk → uvik.net #AI #Claude #Anthropic #EnterpriseAI #LLM #Python #SoftwareEngineering

Big news for the Uvik team. Uvik Software has officially joined the Claude Partner Network - partnering with Anthropic to help companies move from AI experimentation to production-grade enterprise AI. AI pilots are easy. Getting AI into day-to-day operations - securely, reliably, at scale - is the hard part. That's exactly where our Python, data engineering and LLM teams come in. As a Claude partner, we're helping teams design and ship the things that actually move the needle: • AI agents for internal ops and business workflows • LLM-powered support and knowledge assistants • RAG over enterprise knowledge bases • The clean data foundations that make all of it work in production We're just getting started - and we're excited about what we'll build with Claude for teams across fintech, SaaS, insurance, healthcare, and other domains. Thinking about where Claude could fit in your business? Let's talk → uvik.net #AI #Claude #Anthropic #EnterpriseAI #LLM #Python #SoftwareEngineering

The offshore hiring conversation changed again in 2026. The question is no longer whether companies can save money by hiring globally. The question is where the best balance of cost, seniority, AI expertise, and time-zone overlap actually exists. The numbers are striking: • Senior offshore engineers cost 40–70% less than US equivalents • AI/ML talent can be up to 75% cheaper in established offshore hubs • AI skills now carry a 56% wage premium globally • Demand for AI talent exceeds supply by more than 3:1 At the same time, not all offshore markets are equal. Eastern Europe continues to stand out for senior engineering depth, AI/ML expertise, and European working-hour overlap, while Latin America remains the preferred nearshore option for US-based teams. We analyzed 2026 salary and hourly-rate data across 14 countries and 7 high-demand engineering disciplines, including AI/ML, DevOps, Data Engineering, Python, Go, and React Native. Full breakdown here: https://lnkd.in/dcudv_mA

The most expensive developer in your company is often not the one with the highest salary. It's the one you haven't hired yet. When engineering leaders discuss hiring, the conversation usually revolves around compensation, recruitment costs and budgets. These metrics are easy to calculate, which is why they receive so much attention. What is much harder to quantify is the cost of an unfilled position. A critical engineering role left open for months affects far more than the team's headcount. Product releases are delayed, senior engineers spend more time covering gaps instead of focusing on strategic work, technical debt accumulates and roadmap commitments become increasingly difficult to meet. The impact rarely appears in a single report. Instead, it spreads across the organization in small but costly ways. Teams move slower. Priorities shift. Opportunities are postponed. Existing employees become overloaded. Many companies view hiring as an expense. In reality, for key positions, the greater expense is often waiting too long to make a hiring decision. The question should not always be whether a company can afford to hire another engineer. Sometimes the more important question is whether it can afford not to. The cost of a salary is visible. The cost of a vacant seat usually isn't. That doesn't make it any less real.

AI demand is growing, but the real bottleneck is not hiring “AI people”. It is having senior engineers who can turn AI into production systems. According to Indeed Hiring Lab, job postings mentioning AI reached a record 4.2% at the end of 2025, even while the broader tech hiring market remained cautious. CompTIA also reported that more than 275,000 active U.S. job postings referenced AI skills in January 2026. But for CTOs and engineering leaders, the key takeaway is not simply that “AI hiring is growing”. The real takeaway is that AI is becoming part of the engineering stack. It now affects backend development, data infrastructure, product workflows, code review, testing, security and deployment. That means companies do not just need AI tools. They need senior software engineers, data engineers, architects and tech leads who can understand LLM limitations, integrate AI into existing products, validate AI-generated code, manage security risks and move prototypes into production. The bottleneck is no longer access to AI technology. The bottleneck is senior engineering capacity. AI adoption does not remove the need for strong engineers. It raises the bar for them. At UVIK, we see this shift clearly: AI is no longer a side experiment. It is becoming part of product engineering, data infrastructure and backend development. And that means companies need senior engineers who can help them move from AI ideas to reliable production outcomes.