The AI landscape has rapidly evolved over the past year, making it easier and cheaper than ever to build AI-powered features. In 2024, we talked about how to integrate AI into products, but 2025 has changed the game. Let's dive into the new reality of building AI features today.

What's Different in 2025?

Unlimited Context Windows

A major breakthrough in 2025 is the expansion of near-unlimited context windows. This means models can process and retain significantly more information at once, reducing the need for chunking or splitting data. As a result, AI-powered applications can now analyze entire documents, codebases, or chat histories in a single query, leading to more coherent and intelligent responses.

Lower Costs & New Competitors

The cost of running AI models has plummeted. OpenAI is no longer the only major player; new competitors like DeepSeek, Mistral, and open-weight models are delivering comparable results with lower costs and greater flexibility. Organizations can now run highly capable models on their own infrastructure, making AI more accessible than ever.

No Moat for OpenAI

In 2024, OpenAI still had a significant lead in AI capabilities. However, by 2025, the gap has closed. Open-weight models can deliver good-enough performance for most applications, and smaller models fine-tuned on specific tasks often outperform their larger counterparts. This has led to a shift where companies prioritize efficiency and fine-tuning over simply using the biggest models available.

The Rise of Text Extraction

Text extraction has emerged as one of the highest-value AI applications. Businesses are leveraging AI to pull structured data from unstructured documents, emails, and reports, improving workflows across industries such as finance, real estate, and legal.

Revisiting Previous Case Studies: How Would We Do It Today?

In 2024, we worked on multiple AI-powered solutions. Let’s revisit those case studies and explore how we could improve them using the latest advancements in AI.

Case Study 1: AI-Powered Customer Support Chatbot

2024 Solution: We built a customer support chatbot using GPT-4 with a fine-tuned model for handling FAQs and ticket resolution. The chatbot relied on a relatively short context window and required extensive pre-processing to chunk and retrieve relevant information.

How We Would Improve It in 2025:

  • Unlimited Context Windows would allow the chatbot to analyze entire customer histories without needing to split data into smaller parts.
  • Cheaper, Fine-Tuned Smaller Models would reduce the cost while maintaining high accuracy.
  • On-Device Inference would enhance privacy and speed, eliminating the reliance on external API calls for sensitive data.

Case Study 2: Automated Content Generation for Marketing Teams

2024 Solution: We used an AI-driven content generation tool to help marketing teams create blog posts, social media copy, and email campaigns. The system required post-processing to ensure tone and coherence, and fact-checking was manual.

How We Would Improve It in 2025:

  • Improved Fact-Checking with External Data Fetching: Integrating AI with real-time web retrieval to ensure accurate information.
  • Longer Context Windows: AI could generate entire reports with contextual accuracy without breaking content into smaller pieces.
  • Cheaper and Task-Specific Models: Running smaller fine-tuned models for each type of content (blog, tweet, email) would improve efficiency.

Case Study 3: AI-Assisted Code Review

2024 Solution: AI-assisted code review was built using an LLM that flagged common issues and provided suggestions. However, it struggled with larger codebases and lacked the ability to remember past suggestions effectively.

How We Would Improve It in 2025:

  • Near-Unlimited Context Windows: AI can now analyze entire repositories in a single pass.
  • Local Model Deployment: Running models on local infrastructure for privacy and speed.
  • Real-Time AI Pair Programming: Seamless integration with IDEs for real-time, intelligent coding assistance.

New Case Study: Real Estate Trend Analysis with LLMs

One of the most exciting AI implementations we worked on in 2025 was for a real estate customer who needed a solution to extract and analyze market trends in real time. Their problem? They had a massive database of property listings, historical sales data, and customer interactions, but no easy way for users to generate up-to-date reports.

The Challenge

The customer wanted an AI-powered tool that:

  • Extracted data from various sources, including SQL databases and CRM logs.
  • Processed the data in real time to provide insights on market trends.
  • Allowed users to query the system in natural language to get specific reports.

The Solution

We built a lightweight LLM-based system that leveraged the following:

  • Vector Search & RAG: Since the database was large, we implemented a Retrieval-Augmented Generation (RAG) system to fetch relevant data efficiently.
  • Fine-Tuned Smaller Model: Instead of using GPT-4 or an expensive closed-source model, we fine-tuned a smaller open-weight model to understand real estate terminology and common queries.
  • Streaming & Caching: To ensure real-time responses, we used a combination of Redis caching and streaming APIs, making the experience feel instant for users.

The Outcome

With this solution, the customer was able to:

  • Generate detailed market reports in seconds.
  • Provide personalized insights to clients based on live data.
  • Reduce manual data extraction work by 80%.

This case study highlights how smaller models, efficient retrieval techniques, and real-time AI processing are shaping the future of AI applications in 2025.

Final Thoughts

AI in 2025 is more accessible, cost-effective, and powerful than ever. With near-unlimited context, cheaper models, and a strong focus on task-specific fine-tuning, businesses can integrate AI features seamlessly into their products. If you’re looking to build AI-powered features, now is the best time to get started.

Want to discuss AI implementation for your project? Reach out—we'd love to help.