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AI is everywhere—featured in demos, sales decks, and investor calls. Founders feel the pressure to “add AI” as if it’s just another feature to check off. But here’s the reality: most AI features either don’t work reliably in production or fail to deliver real value.
At TLVTech, we’ve partnered with dozens of startups to integrate AI—sometimes with great success, other times with hard lessons learned. Before you invest your team’s time, money, and energy into building an AI product, here’s what every founder should know.
Don’t chase the latest model just because it’s trendy. Focus on the real user pain you’re solving. You don’t need GPT-4 to deliver value; you need clarity on what your users actually need.
The best AI is invisible. If users notice the AI, it’s either broken or unnecessary. Ask yourself: “Would anyone use this feature if it didn’t have AI under the hood?” If the answer is no, rethink your approach.
It sounds counterintuitive, but your first version shouldn’t be “smart.” Before investing in models or APIs, validate your assumptions manually or with simple rules. If a human can handle the task in 30 seconds, do it manually behind the scenes. If users find value, then—and only then—should you automate.
For example, instead of building a custom AI support agent, route key queries to your team and tag them. If you see strong user engagement, you’ll know it’s worth productizing.
The AI feature that impresses in a demo isn’t always what keeps users coming back. Users don’t care about novelty—they care about reliability and usefulness.
Test for failure early. What happens when your model gets it wrong? What’s your fallback? Users will forgive limited features, but not unpredictable or nonsensical results.
Plugging into OpenAI or another LLM provider doesn’t solve everything. You’re still responsible for:
You need guardrails, error handling, and a user experience that anticipates mistakes. Otherwise, you’re shipping a prototype—not a product.
Unless you’re building deep tech, off-the-shelf models will get you 90% of the way. Focus on user experience, feedback loops, and smart prompting.
Only train custom models if:
You’ve pushed off-the-shelf solutions to their limits
You have unique, proprietary data
The potential impact justifies the investment
The best AI products we’ve seen didn’t start with fancy models. They started with tight user feedback loops, clear success metrics, and relentless focus on utility.
AI is a tool—a powerful one. But it won’t rescue a weak product or a fuzzy value proposition.
If you’re building something real and want AI to be part of it, let’s talk. We help teams launch AI features that actually work.
- AWS Redshift is a data warehousing service from Amazon Web Services, designed for real-time analysis of large data volumes. - It works by storing data across different compute nodes, creating a high-speed, low-latency network for efficient data exploration. - Data is stored in clusters (groups of databases). Redshift's core functionalities include ETL and integration with most BI tools. - Benefits include scalability, speedy complex queries, and cost-saving. It is valuable for industries like media and healthcare. - Redshift's pay-as-you-go pricing model has two components: node hours and data transfer with costs related to Dense Compute and Dense Storage nodes. - Compared to other platforms, Redshift is superior in scale and performance operations. Redshift is better for complex high-volume analytics, while Athena is suited for simplicity. - To start with Redshift, sign up for an account, select Redshift, follow the setup guide to launch a cluster, load your data, query it, tune when necessary, and manage costs. - Redshift Spectrum is an AWS feature that allows big data manipulation directly from an S3 bucket. It enables data access without loading it into Redshift.
Coding standards boost readability, collaboration, and scalability, reducing errors and ensuring reliable, maintainable, and team-friendly code.
- Web application security is crucial for longevity and user safety; without it, your application is susceptible to data breaches and cyber threats. - The Open Web Application Security Project (OWASP) is a key tool in web application security, assisting businesses in understanding and addressing vulnerabilities. - Consequences of inadequate security include loss of revenue, reputation, customer trust, and potential legal penalties. - Tools commonly used to improve web application security include firewalls and antivirus solutions, alongside platforms like TryHackMe for cybersecurity skill development. - A reliable web app security plan should include regular security audits, strong passwords, up-to-date software, and data encryption. - Implementing OWASP guidelines for web app protection starts with understanding OWASP principles, targeting app vulnerability points, and regular updates on OWASP standards. - A web application firewall, analogous to a castle gate, forms a barrier against harmful data and should be regularly updated to match evolving cyber threats.
