At a time when many companies are racing to slap “AI-powered” labels on generic chatbots, Konstantin Bukin took a different approach. As Director of AI at Saritasa, a custom software development firm with over 15 years of experience, Bukin spent months building and testing AI solutions internally before offering them to clients. That deliberate, hands-on process of developing an AI Website Assistant and an internal team assistant with agentic capabilities gave Saritasa something increasingly rare in the AI space: genuine expertise backed by real-world implementation experience.
The result is a chatbot development approach that prioritizes deep integration over quick deployment, ownership over subscription models, and practical business impact over hype. Saritasa’s custom AI solutions connect directly to client databases and CRM systems, moving beyond simple question-and-answer functionality to perform tasks, automate workflows, and proactively assist users.
In this conversation, Bukin discusses why the hardest part of AI implementation is rarely the AI itself, how agentic capabilities transform chatbots from passive tools to active partners, and why businesses need to audit their data before they even think about calling a developer. For any organization considering AI implementation in 2025, this interview offers a grounded perspective on what actually works and what’s required to make it work.
For our readers who might not be familiar with Saritasa, can you tell us about the company and how your role as Director of AI fits into the broader mission?
Saritasa is a custom software development company that’s been building tailored technology solutions for over 15 years. We work with businesses across industries, from manufacturing to healthcare to education, helping them solve complex problems with technology.
As Director of AI, I lead our efforts to apply artificial intelligence to real business needs. That means not only building AI solutions for clients but also helping them understand how AI fits into their existing systems, processes, and goals. At Saritasa, our mission is to empower businesses with technology, and AI is a natural extension of that mission.
You mentioned that rather than rushing to market with AI chatbots, Saritasa focused on learning and developing internal expertise first. Can you walk us through that decision and what you learned during that development process?
When AI exploded in late 2022, many companies rushed to put their name on “AI solutions.” We took a different route by building and testing AI internally first.
For example, we developed our own AI Website Assistant and an internal team assistant with agentic capabilities before ever offering chatbot services to clients. This gave us two big advantages:
- Deep expertise: We worked through the integration challenges ourselves, so we know what clients will face.
- Practical validation: We saw firsthand which AI use cases drive real efficiency and which are just hype.
That hands-on experience made us far better positioned to deliver AI solutions that actually work in real-world business environments.
Saritasa recently announced custom AI chatbot development services. What makes your approach different from the off-the-shelf solutions that many companies are rushing to implement?
Most off-the-shelf chatbots are “one-size-fits-all.” They’re cheap and fast to launch, but they don’t connect deeply with a business’s systems or processes.
Our approach differs in that we build custom AI chatbots that integrate directly into a company’s databases, CRM systems, and workflows. Instead of a generic assistant, clients receive a solution that understands their data, processes, and customers.
We also push beyond simple Q&A into agentic capabilities. That means the chatbot can do more than answer questions; it can take action, perform tasks, and proactively assist users. This turns it from a passive tool into an active partner that drives real business impact.
One key selling point is that clients own their chatbot solutions with no additional fees or licenses. In an era of subscription-based AI services, why did Saritasa choose this model?
We believe AI should be a long-term asset, not a rental. With subscription services, companies risk dependency, hidden costs, or even losing access if the provider changes terms.
By giving clients ownership, they get full control over their data, their workflows, and their budgets. No surprise licensing fees, no vendor lock-in. It’s an investment that keeps paying dividends.
What are the biggest technical challenges you’ve encountered when building AI solutions that integrate with existing business systems, particularly with database integration and CRM systems?
The hardest part is almost never the AI itself—it’s the integration. Businesses often have complex, sometimes messy systems. Connecting AI to those systems means tackling:
- Data quality issues (inconsistent, unstructured, or outdated information)
- Security and compliance (permissions, role separation, authentication)
- Real-time syncing (ensuring the chatbot always has the latest info)
Overcoming these challenges requires strong engineering and a deep understanding of the client’s business processes.
You’ve developed both internal and external chatbots for Saritasa. Can you explain the difference between these two types and why organizations might need both?
Internal chatbots are designed for employees. They streamline processes like project updates, reporting, or accessing company knowledge. In some cases, they also perform repetitive tasks, essentially acting as productivity boosters with agentic capabilities that go beyond answering questions.
External chatbots are customer-facing. They handle support, sales inquiries, and onboarding – improving customer experience while reducing workload on human teams.
Most organizations benefit from both. Internal bots improve efficiency, while external bots enhance customer interactions. Together, they save time, reduce costs, and improve overall business performance.
What are the biggest misconceptions you encounter when talking to potential clients about AI chatbot implementation?
Two stand out:
- “AI is plug-and-play.” Many assume you can just switch it on and it will know everything. In reality, it takes customization, integration, and training to create value.
- “AI will replace people.” In practice, AI handles repetitive tasks, but humans are still critical for complex decisions and empathy-driven interactions.
Many companies are concerned about AI replacing human workers. How do you position AI chatbots as augmenting rather than replacing human customer service teams?
We frame AI as a force multiplier. Instead of replacing people, it empowers them:
- Chatbots handle repetitive questions and routine tasks.
- Human teams focus on higher-value conversations, relationship building, and problem-solving.
The best AI implementations make people more effective and impactful.
For businesses looking to implement AI solutions in 2025, what advice would you give them about planning and preparation? What should they do before they even start talking to developers?
My advice is: get your foundation in order first. Before building AI, businesses should:
- Audit their data: Make sure it’s clean, accurate, and accessible.
- Map out workflows: Document them clearly and identify inefficiencies or bottlenecks.
- Define success: Know what outcomes you want from AI before you start.
The stronger the foundation, the smoother and more successful the AI project will be.
Where do you see AI chatbot technology heading in the next two to three years, particularly for small and medium-sized businesses?
I see four major trends:
- Deeper integration: Chatbots will be tightly embedded into CRMs, ERPs, and custom systems.
- Greater personalization: Bots will tailor responses based on data, user roles, behaviors, and history.
- Agentic capabilities: AI will move beyond answering questions to proactively performing tasks, automating workflows, and taking action on behalf of users.
- Lower barriers to entry: Tools will continue to get cheaper and easier, giving SMBs access to enterprise-level AI without enterprise-level budgets.
For small and medium-sized businesses, this means AI chatbots will become practical, affordable, and transformative, helping them compete at a higher level without dramatically increasing costs.
