As the digital landscape continues to evolve rapidly, staying ahead requires more than just delivering great software; it demands innovation, adaptability, and intelligence at every layer. At EvinceDev, we’re pushing the boundaries of what’s possible with AI by embedding it deeply into our internal workflows, client solutions, and R&D efforts. April 2025 marked significant strides in AI-driven development practices that are shaping how we build, test, and scale technology.
Custom Estimation & Timeline AI
Estimating project timelines and budgets has always involved a mix of logic, experience, and a bit of guesswork. We wanted to replace the guesswork with intelligence. Our internally deployed Estimation & Timeline AI uses historical project data, team velocity metrics, and contextual inputs (like tech stack complexity and integration requirements) to deliver reliable projections in minutes.
Challenges
- Inconsistent historical data: Historical estimates often lacked standardized formats or had gaps.
- Varying team dynamics across projects: Velocity depends on team composition, which varies widely.
- Client inputs often lacked clarity: Estimations suffered when requirement inputs were vague or evolving.
Solutions
- Built a data-normalization engine: We cleansed and structured years of project logs, mapping them into a uniform dataset.
- Integrated team-performance modeling: AI adapts predictions based on real-time team velocity, availability, and historical task efficiency.
- Contextual NLP pre-processors for inputs: The system parses client briefs and feature sets to extract key effort indicators—even from non-technical language.
Outcome: Our teams now generate accurate estimations 3x faster—with built-in confidence scores and delivery risk flags.
Revolutionize Decision-Making with AI Behavioral Insights
We’ve engineered an AI system that observe user behavior the way a product manager would—but on autopilot and at scale. It monitors app or website usage in real time, identifying patterns, friction points, and drop-off zones. Whether it’s a broken functionality, a user unable to proceed, or an important button that nobody’s clicking—our AI catches what traditional analytics often miss.
But it doesn’t stop there.
The system contextualizes each interaction based on device type, user segment, and engagement history—then generates actionable UX and content suggestions. Instead of vague reports, you get clear answers like:
- “This button isn’t working on mobile.”
- “Users are stuck on this step and abandoning the flow.”
- “You’re losing conversions because a key CTA is being ignored.”
Challenges
- Differentiating signal from noise: Raw behavioral data (scrolls, clicks, hovers) is noisy without proper context.
- Privacy and compliance concerns: Monitoring behavior must stay GDPR-compliant and ethically sound.
- Translating analytics into actionable insights: Data overload doesn’t help unless it turns into decisions.
Solutions
- Intent modeling + anomaly detection: We use AI to classify interactions into signals of interest vs. noise, making insights sharper and more relevant.
- Edge-based anonymized tracking: Behavior is captured locally and anonymized before analysis—ensuring privacy without losing value.
Outcome: Clients now gain laser-sharp visibility into why users leave, where they struggle, and how to improve conversion—with almost zero manual analysis.
Unlock Seamless UI Testing with AI Visual Recognition
We initiated the development of an AI-driven UI testing tool that simulates human-like interactions using computer vision and automated workflows. The system captures full-page, stitched screenshots to provide comprehensive visual coverage, highlighting UI regressions that are often missed by standard test scripts. This approach significantly reduces QA effort, accelerates test cycles, and improves quality assurance for responsive web and mobile applications.
Key Highlights
- Full-page screenshot stitching: Captures complete views of the webpage across multiple devices to ensure end-to-end visual consistency.
- Visual difference detection: Automatically identifies UI discrepancies using pixel-level image comparison, with bounding boxes highlighting differences.
- Device-aware testing: Adapts to different screen sizes, operating systems, and browser versions for accurate cross-device validation.
- Baseline comparison: Uses a predefined baseline device for consistent visual testing across multiple devices.
Challenges
- Dynamic UI variations: Managed by capturing multiple viewport sections and stitching them together to create a comprehensive page view.
- False positives in visual anomaly detection: Partially addressed through pixel-level thresholding, though further improvement is needed for context-aware filtering.
- Efficient handling of cookie pop-ups: Implemented automated consent handling to streamline initial page interactions.
Solutions
- Stitching and full-page capture: Developed a custom scrolling and stitching algorithm to handle large, complex pages without losing context.
- Contextual difference detection: Bounding box detection for visual discrepancies to highlight impacted regions, providing clearer insights for QA teams.
- Device-aware testing framework: Supports multiple devices with dynamic viewport handling, ensuring compatibility across screen sizes and resolutions.
Outcome
We’re redefining the future of UI testing—one that captures the complete picture, adapts to changing layouts, and significantly reduces QA time and costs.
Fine-Tuning LLMs for Industry-Specific Chatbots
Our AI research team is fine-tuning open-source LLMs (like Mistral, LLaMA, and Falcon) using anonymized client support logs and knowledge bases. The goal is to build chatbots that deliver deeply contextual, human-like conversations tailored to specific industries like finance, healthcare, and SaaS. By combining RAG (Retrieval Augmented Generation) with intent modeling, these bots can handle complex queries, understand jargon, and even align with company tone and policies.
Key Highlights:
- Custom LLMs trained on domain-specific datasets
- More accurate, brand-aligned responses in support and sales chats
- Uses Retrieval Augmented Generation for knowledge-based querying
- Lays the groundwork for white-labeled AI chatbot deployment
SOMMOS: AI-Powered Family Storytelling Platform
In an era where digital connections shape human experiences, EvinceDev proudly designed and developed Sommos, a Baltimore-based social innovation platform that brings families closer across generations. Built from the ground up by our team, Sommos is a memory-preserving storytelling application that leverages cutting-edge AI technologies like GPT-4 and Whisper to transform fragmented family content; such as voice notes, old photos, and video snippets—into emotionally rich, structured digital narratives.
Platform serves as a secure digital archive, enabling families to relive cherished moments and strengthen emotional bonds through AI-enhanced storytelling. From intelligent interview scheduling to dynamic story generation and interactive timelines, EvinceDev delivered a seamless, intuitive solution tailored for all generations to connect, remember, and engage.
What Makes Sommos Stand Out:
- AI-generated Interview Questions: Personalized based on family member profiles to spark authentic conversations.
- Interactive Family Room: A dedicated space to browse stories, read transcripts, watch interview videos, and engage through comments.
- Smart Assistant for Onboarding & Scheduling: AI-curated interview timelines with notifications and reminders for smooth participation.
- Advanced Speech Recognition: Integrated with OpenAI’s Whisper model to transcribe audio accurately—even across diverse accents, dialects, and overlapping dialogue.
Key Impacts & Results
- 95% accuracy in AI transcription, improving accessibility and record-keeping
- 92% growth in content sharing, encouraging deeper family interactions
- 86% increase in user engagement, showcasing the platform’s intuitive design and emotional value
Sommos isn’t just a product, it’s a movement toward preserving legacies through technology. Powered by EvinceDev’s full-cycle development and AI expertise, this platform is shaping how future generations connect with their past.
Coboda Dashboard: Real-Time Property Management Insights
The Coboda Dashboard is a comprehensive admin tool designed to monitor bookings across Airbnb and other rental platforms for a portfolio of 266 properties. It provides a centralized view of key metrics such as reservations, pricing, reviews, and occupancy trends. The dashboard includes modules like Property List, Reservation List, and visual graphs to track booking performance. Admins can access detailed overviews, daily performance reports, and pacing insights to analyze booking trends. It also helps optimize pricing strategies and manage property-level data efficiently. With real-time updates, the dashboard supports informed decision-making and streamlined operations.
Challenges Faced
- Complex Data Transformations: Each module (Reservations, Overview, Pricing) required intricate data calculations, aggregations, and normalizations.
- Performance Bottlenecks: Processing large volumes of data across 266 properties in real-time without lag was a significant technical challenge.
- Dynamic Metric Derivation: Metrics like occupancy, ADR, pacing trends, and revenue needed to be computed accurately from fragmented data sources.
- Maintaining Consistency Across Tabs: Ensuring that all tabs reflected consistent data despite relying on multiple asynchronous API calls and real-time updates was difficult.
Solutions Implemented
- Custom Data Parsing Engine: Built a robust data parsing layer to extract, normalize, and flatten nested Hostway API responses into usable formats.
- Modular Data Transformation Pipelines: Structured each dashboard tab with dedicated transformation logic, making the system more maintainable and scalable.
- Optimized Querying and Caching: Implemented strategic caching and optimized database queries to ensure smooth performance during data-intensive operations.
- Unified Data Mapping Layer: Created a centralized mapping schema to standardize key metrics and ensure consistency across all dashboard views.
- Real-time Sync with Error Handling: Set up asynchronous sync processes with retry mechanisms and logging to keep data live, accurate, and resilient to API disruptions.
These initiatives represent our commitment to infusing artificial intelligence into the very fabric of modern digital solutions; making systems smarter, faster, and more reliable. From internal automation to advanced client-facing applications, our AI-first mindset continues to drive measurable innovation.
Curious how these breakthroughs could elevate your next product or streamline your operations? Let’s talk about applying AI to your business. Reach out to us to explore what’s next.