AI Solution: the best recommendations

Best Recommendations To Implement AI Software Solutions

Last updated: September 15, 2026

The days of writing endless lines of code are gone. Good or bad, AI is turning software into pre-packaged, copy-paste chunks of logic that engineers just have to put together. So, the big question isn't how to build an AI Solution, it's how to use it to your advantage.

This blog is based on insights from our AI software consulting team. We have Statisticians and Senior AI Engineers with 10+ years of experience building AI models for the financial, banking, and IT industries. Our team shares key insights on how any business can successfully implement and adopt AI software. Buckle up!

AI Models Don't Work for Every Business Case

If you think AI is just the new buzzword, it's actually not. Gartner forecasts that worldwide AI spending will reach $2.59 trillion in 2026, a 47% jump over the previous year. That money is flowing somewhere. So, will you catch the gold rush? Hopefully, but not so fast!

Many companies try to force generic AI models, like ChatGPT, into every possible business scenario. This usually leads to inefficiencies and hallucinations (AI making up stuff). Recently, our team had a consulting call with a large U.S.-based healthcare organization that tried using AI to retrieve patient records, only to find that the model was fabricating data. A few simple SQL queries would have been a much more effective solution. This kind of failed AI implementation can actually discourage organizations from adopting AI at all.

Recommendation: Before jumping into AI, analyze the use case first. Is AI really the best solution? If not, don't force it.

Start with a Proof of Concept (PoC)

AI Solution is often pitched to executives as the ultimate solution for everything. But in reality, AI is not perfect, at least, not yet. AI-generated voices still sound robotic, and some use cases are fairly limited. To avoid massive investments in a system that may not work as expected, companies should start small. Developing a Proof of Concept (PoC) or software MVP can help you assess whether AI is actually a viable solution before going all in. What does an AI Proof of Concept (PoC) look like?

Here's a use case: a customer support team wants to use AI to automate responses to common inquiries. Instead of immediately replacing human agents with a full-fledged AI chatbot, a company could start with a PoC chatbot that handles just a handful of repetitive questions, like order tracking, refund policies, and account resets.

Here's how our AI chatbot development company would approach the PoC:

1. Define the Scope: The chatbot only answers the top five most common customer questions instead of trying to replace an entire support team.
2. Use Off-the-Shelf AI Tools: Rather than building a complex AI model from scratch, you could use existing tools like OpenAI's GPT or Google's Dialogflow to generate responses.
3. Test with a Small Audience: The chatbot is deployed only on a small section of the website or tested internally before rolling it out to real customers.
4. Measure Success: The team tracks key metrics like response accuracy, user satisfaction, and how often the chatbot needs human intervention.
5. Iterate or Delete: If the chatbot performs well, it can be expanded. If not, the company has saved time and money by testing before investing in a full-scale system.

For instance, our AI consulting team based in Latin America built a custom chatbot for a Pension Fund in Latin America. The Client needed an AI chatbot integrated with MS Teams so users and internal employees could ask questions about a new pension reform affecting state-wide pension funds in Colombia. So, rather than buying ChatGPT licenses for every employee, the company needed a custom solution that could:

• Process and update multiple regulatory documents.
• Maintain zero errors (because compliance mistakes aren't an option).
• Deliver consistent answers across all users.

Here are the main features we designed for this AI solution:

• Extracting document content using Python & Markdown.
• Enhancing text with image descriptions using LLMs.
• Converting everything into vector embeddings stored in AWS S3 (using FAISS by Meta).
• Integrating with MS Teams, WhatsApp, and Slack.

The end result was a very successful implementation with almost zero errors, faster document processing, and consistent responses for every user. Now, the next step was just to keep it up to date with new ongoing regulations, which leads us to the next recommendation.

Build a Modular AI Prototype You Can Update Regularly

One of the biggest challenges with AI is that models evolve constantly. According to McKinsey’s The state of AI in 2026 report, only about a third of participants (37%) think AI has a real impact on the company’s financials. Adoption is moving fast, but value depends on how well solutions are built and maintained. In our experience, the best way to future-proof your AI system is to keep it modular and avoid relying on a single data source. If your AI infrastructure is flexible, you can swap components in and out as technology advances without having to rebuild everything from scratch.

With all this change, the future of software development and the skills that matter are shifting, too. Learning one specific programming language like React or Angular is not as crucial as it used to be. AI tools can now generate and translate code across languages in seconds. What really counts is understanding how to use AI to solve real problems and create something valuable.

Expectation Management: Define Accuracy vs. Efficiency Trade-offs

An AI Solution won't always be 100% accurate, and that's okay, if you plan for it. The key is understanding the trade-offs. For example, a manual process may be slow but have 99.9% accuracy. AI might only be 87% accurate but it can get the job done in seconds. The goal is to determine what level of accuracy is acceptable for your use case. AI should be a complement to process optimization, not an outright replacement.

Here's our suggestion: keep audio or video analysis as simple as possible to avoid high infrastructure costs. The more complex the processing, the more expensive and time-consuming it becomes. The goal is to find the right balance between speed and accuracy. Working with AI optimization experts can help fine-tune models until they meet your target KPIs.

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 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 Data

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.

Make Sure You Invest in People

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 Specializes in Implementing Secure AI Solutions

Ewents integrates generative artificial intelligence technologies, building custom tools that improve efficiency and reduce operating costs. But most importantly, our security team embeds AppSec and compliance into any project from the start. 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, security concerns, 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 + AI security considerations
• 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. We also run AI evals, which are test cases that other AI agents can run to validate against human defined testing requirements. 

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:

• Core Tech: Python backend, React/Angular/Vue frontend, and Markitdown for document processing.
• Vectorization: OpenAI embeddings, FAISS on AWS S3, and LangChain/Pinecone/Supabase for vector storage.
• Integrations: CRM tools like Salesforce AI and WhatsApp.
• Audio Providers: Genesys, Avaya, Twilio, Justcall, Zendesk
• Deployment: Docker and AWS Lambda for scalability and optimization.
• AI Agents: Vocode, Pipecat
• Video Analysis: OpenCV, MediaPipe, AWS Transcribe, Google Video AI
• Acceleration: Groq, Cerebras AI

Call & Video Analysis

Our AI consultants enable precise interpretation of interactions, analyze emotional tone, and identify relevant patterns for insightful business intelligence. Our AI solutions enhance customer service by analyzing both audio and video data through:

• Real-time call scoring and analysis
• Sentiment detection and customer interaction tracking
• Speech-to-text transcription with advanced NLP processing
• Large-scale call monitoring and performance evaluation

Conversational Agents

Our AI automation experts also develop advanced AI voice assistants for seamless client interaction management. Our models create realistic, expressive synthetic voices, ensuring a human-like experience in applications such as:

• Appointment Scheduling
• Sales & Customer Support Automation
• Debt Collection & Financial Interactions

Start by Hiring Custom AI Development Engineers

Every AI development project is supported by a specialized team. Thanks to our nearshore model, you can easily hire top AI developers working in a team augmentation model. Our team structure is flexible and can be tailored based on your business needs and governance model, including specialized roles such as:

• • Forward Deployed Engineer
  • AI Security Specialist
  • AI Cloud Solutions Architect

• AI Testing Engineer

• Project Manager (Optional)

Working with a specialized team is the best way to prove ROI quickly before wasting your yearly budget on a AI solution that might not work in practice. Contact Ewents to schedule your AI discovery call now.

How long does it take to implement an AI solution?

It truly depends on the scope of the solution. Realistically, our team is building rough prototypes in just 2-3 business days to automate one clear process and show quick value. That said, some implementations require cleaning and ingesting data from multiple sources, or applying structural changes to the existing infrastructure. For those cases, with a focused scope, a working proof of concept usually takes about two to three months. At Ewents we follow a 1:2:3 model: one day to define the use case, two weeks of design and planning, and three months to build and test the PoC with real users. Moving from PoC to full production depends on integrations, data readiness, as well as security and compliance requirements, so we usually plan a future roadmap after the initial solution is deployed and working.

Do I need to hire a Forward Deployed Engineer to build a PoC?

Sometimes, it is not required. A non-technical person might be able to deploy a fairly complex AI solution vibe-coding. However, a Forward Deployed Engineer (FDE) can add significant value when the organization does not know exactly how to approach an automation, or how to break down the business rules and requirements into technical requirements that can be implemented wth AI. To learn more, you can read our latest blog about the role of a Forward Deployed Engineer. 

What are the top security risks I should consider when deploying AI?

Based on our experience, the greatest risk from using AI is letting it run wild without setting proper data guardrails and clear access controls. Most of the security issues come from:

• Feeding too much sensitive data to the models. 
• Giving both users and AI agents too much access. Often times, an AI agent can reach systems, files, or APIs it does not really need.
• Third-party risk, because your company’s security also relies on which vendors, tools, and integrations you use. 

Realistically, it is always best to lean on the stricter side and limit access to only a few users, protect sensitive data with proper guardails, establish processes to validate outputs, and make sure a human always approves for higher-risk actions.

How can I make sure my AI build is secure and aligned with compliance standards?

Our team strongly suggests to always think about security and compliance from the start. When we work with clients, we usually engage security engineers and GRC analysts to ask questions such as:

• What data the AI system will access?
• Where that data is stored?
• Which models and vendors are involved?
• Who is responsible in case AI causes a security incident?

These thoughtful questions help us define controls around access, data privacy, logging, human oversight, and how you handle risks such as hallucinations, bias, or unintended data exposure. In one of our most recent blogs, we also discuss why every team needs a GRC analyst, which can be helpful to map system requirements against industry frameworks such as SOC 2, ISO 27001, GDPR, or the NIST AI Risk Management Framework.

What is a proof of concept (PoC) in AI and why start with one?

A PoC is a rough, preliminary version that you can build and deploy online quickly and for a low cost to validate against users if the solution provides real value. Since building complex AI integrations sometimes requires structural changes and a lot of change management within the organization, it is always a best practice to do small, incremental iterations of the code and see if the solution will be effective at automating or improving a critical process. 

How do I keep my AI solution up to date as models change?

We strongly suggest to design it to be modular, keeping the model, the data pipeline, the vector store and the integrations as separate components so you can replace one without rebuilding everything. Additionally, you should schedule regular reviews of accuracy, cost and new model options, and version the source documents the system relies on. Even better, our team can build autonomous agents and dashboards that control which model to use and how usage is changing over time to avoid drastic spikes in cost, and notify you in case there’s any.