Best Recommendations To
Implement AI Software Solutions

Our AI dev team also advises against using fixed parameters and instead recommends defining ranges. For example, when dealing with AI customer service reps, rather than setting a strict rule that pauses in responses must be exactly 25 seconds, it’s better to analyze pauses within a range—say, between 5 and 60 seconds. This flexibility allows the AI to generate probabilistic outputs, making the analysis more adaptable and accurate.

Another consideration is that in industries like healthcare, even small errors can be costly. In contrast, customer service AI can afford to make minor mistakes as long as it improves efficiency overall. Our team would likely look at your industry, legal and compliance regulations, and financial risk. Based on the assessment, we can determine if it’s a good idea or not to implement AI. We’ve learned our mistakes from actual use cases, so here’s the next suggestion:

Learn from Real AI Solution Use Cases

Our team is always learning from real-world cases we experience working with our clients. In one specific project, we developed an AI-powered call analysis system for a large call center to automate customer service analysis and track ticket resolutions. Rather than relying on a simple chatbot, our AI implementation processed and analyzed 10,000+ customer calls within hours (something that would typically take months).

Here’s what we learned when dealing with voice-based AI agents: accents and languages matter. A British English AI model might struggle with American English vocabulary. Similarly, an AI trained in general Spanish won’t necessarily work well in Spain or Latin America. The second challenge is also industry-specific language. AI trained for general conversations usually doesn’t perform well in legal, medical, or financial settings that require custom language training.

Clean Your AI Solution Inputs

AI models are only as good as their data. This is because garbage in = garbage out. A question in Gartner’s peer community asks, “What lessons have you learned from failed or less successful AI pilot projects that have informed your full-scale deployment approach?” A Sr. Director of Global Technology & Security in Healthcare and Biotech responded:

“The foremost lesson is the critical importance of data quality. The adage “garbage in, garbage out” holds particularly true in the context of AI. If the input data is of poor quality, the results will inevitably suffer.” 

Before processing calls or video inputs, it’s critical to:

• Reduce ambient noise.
• Apply filters to clean up audio.
• Highlight important voices.
• Use transcription models like Whisper or Deepgram for accuracy.

Get Your Team Ready for AI

Interestingly enough, the same peer member of Gartner’s community also highlights:

“Another vital aspect is the allocation of dedicated resources. AI projects cannot be sidelined or treated as an afterthought without compromising their potential and overloading the involved staff.”

Since finding the right team and talent is critical, your next step is finding a partner to support you with AI integration services. That’s where we come in:

Ewents is a Top Nearshore AI Solution Development Company

Ewents integrates generative artificial intelligence technologies, building custom tools that improve efficiency and reduce operating costs. We offer our clients a 1:2:3 Delivery Model—going from concept to a working AI solution in just three months. Here’s how:

1 Day: Use Case Definition

We kick things off with an intensive one-day session to define your AI use case. Our team works closely with you to identify the key challenges, objectives, and expected ROI, ensuring the AI project aligns with your business goals.

2 Weeks: Design & Planning

Once the use case is defined, we move into a structured two-week design phase, breaking down the project into real-world scenarios and defining measurable KPIs. This phase includes:

• Workshop #1: Business objectives & ROI mapping
• Workshop #2: Exploring and selecting use-case options
• Workshop #3: Designing conversation flow and AI interactions
• Workshop #4: Defining technical requirements

During this stage, we also validate technical feasibility evaluating platforms, architecture, and integrations to start implementing without issues. 

3 Months: Proof of Concept (PoC) Development

Once we nail a well-defined design, our team builds and tests a functional AI PoC within three months. This includes:

• Building the PoC: Implementing selected scenarios of the AI use case in a test-ready state
• User Testing: A preselected User Acceptance Testing (UAT) group validates the AI’s performance, providing feedback to refine and optimize training

Once the PoC is validated, we help businesses scale AI solutions into fully integrated automation systems. We specialize in three key AI software development services:

Intelligent Agents

We develop smart agents that process natural language, learn from interactions, and adapt to specific contexts. Our AI-driven solutions leverage techniques like Retrieval-Augmented Generation (RAG) and integration with search engines, databases, and APIs to deliver highly accurate and context-aware responses for use cases like:

• 24/7 Personalized Chatbots
• Product Recommendation Systems
• AI Education Tutors
• Fraud Detection Systems

Additionally, we use the latest and greatest AI technologies, mostly focusing on open source code, so we can drastically lower implementation costs. Our Tech Stack for Intelligent Agents Includes: