Let’s cut to the chase, your VP just walked into your staff meeting and challenged all departments to figure out how they’re going to use generative AI to drive wins for the business. What do you do? Where do you start? Does your team know which questions to ask first? If you’re a team with engineers, you have them use ChatGPT or set up a dedicated LLaMa2 instance. If you’re a non-technical team, you’re scrambling to find available engineering resources to get set up. Everyone is fighting for data analysts to help them prepare data. But for what? Which datasets? What use cases? How will you measure success?
That same effort is being repeated across the organization as each team is left to its own devices, multiplying time and expense. This will impact other roadmap projects as resources will have to be reallocated to get genAI experimentation up and running. The first step you can take to minimize costs and accelerate your time to market is to centralize those resources to build shared infrastructure to support genAI and the Large Language Models (LLM) that power it. Unifying LLMOps across the organization can reduce duplicative work and maintenance. How much will that cost? Use the calculator below to estimate your build and maintenance costs.
Now, if you’re like many organizations, your VP’s challenge has each team running full-speed to find those wins. Can someone in your organization champion the importance of consolidating efforts? Who will have the authority to approve that effort? Will that timeline align with expectations as each team is given team-specific, quarterly objectives to deliver on genAI initiatives?
With Engenai, you can dramatically accelerate your time to market while spending just a fraction of what it would take to build and run your own LLMOps infrastructure. Your Engenai instance can be configured, managed, and accessed with low or no code. Get your teams up and running with genAI in a couple of weeks instead of months.
