Infactory: The virtual AI programmer you want to hire
Infactory is building a virtual AI programmer, providing an expert “team member” to every business with an AI app or Gen AI differentiation goal. The tech industry is just beginning to discover the potential of AI to boost productivity and cover insights in our data. But there’s a critical gap: demand for AI expertise far outstrips the tiny fraction of AI specialists. That talent pool is even smaller when seeking hands-on expertise building consumer-facing AI applications, as opposed to foundational model research or basic LLM integration.
This expertise gap is especially pronounced in legacy application and data companies with a high demand for accurate AI applications but little in-house AI experience. And the need is even greater for companies that provide apps and services based on trust and accuracy, like media, health tech, and fintech. This expertise gap is particularly pronounced in legacy application and data companies with a. high demand for accurate AI applications but little inherent AI expertise. And the need is even greater for companies that provide apps and services based on trust and accuracy, like media, health tech, and fin tech. These companies risk their reputations by attempting to DIY reliable Gen AI offerings using primarily non-specialized full-stack engineers and a patchwork of OSS frameworks, RAG-as-a-Service tools, web scraping APIs, and other costly, complex strategies.
Infactory’s Virtual AI Engineer is the solution. It embodies our team’s lived experience and brain trust–we’re one of the few teams with hands-on expertise in this specific domain. At Humane, Google and within specific industries, we built these exact types of applications, experimenting with cutting-edge techniques to enhance AI accuracy and answer quality for partners across media, health tech, fintech and other services. It’s a massive undertaking to produce factual, high-quality answers that meet expectations for real-world use cases. Our technology provides the virtual AI engineer that many of these companies desperately want to hire.
The AI revolution has made a great start, but unlocking the full potential of AI will require better tools and expertise for companies without robust AI expertise. .
With today's AI programs, we're at the point the personal computer was in the mid 1970s, before the Apple ][, and well before the Macintosh or the iPhone. Early computers in this era, like the MITS Altair, had blinking lights on their front consoles that changed as it ran its programs. People back then thought that felt magical.
We think that today's LLM/RAG applications represent a similar point in the evolution of AI programs. The "prompt an LLM to find facts extracted from a collection of search results" can feel magical. And it is. But that kind of app is only scratching the surface.
AI could do so much more—and it will. AI programs must deliver reliable access to facts, show their work, and present results we can trust. We can achieve this and more with Infactory’s AI Engineer, which encapsulates the knowledge needed to build production-ready AI applications for the most accuracy-dependent use case, and produces the right tools for the job. .
Tools for presenting data to LLMs so the AI can understand and extract the structure and meaning in that data, and tools to produce and manipulate common formats for its findings.
Tools for reading these formats and building up models and schemas ideally suited for AI processing, much the way that relational and NoSQL databases define models and schemas to optimize for the way they handle data.
Tools for searching efficiently through data that conforms to these models and schemas.
Tools for generating and executing queries that run against these data models and schemas that can obviate the need for direct LLM involvement on a per-request basis, can execute more quickly and cheaply, and can do chained and follow-on computation at each step.
Tools for programmers to create these new AI-targeted programming resources, tools for building programs out of these resources, and tools for managing and deploying these programs locally and in the cloud.
Ultimately, our virtual AI Engineer uses these tools to build the foundation for trustworthy and fact-based AI, preparing developers and users not just for the kinds of desktop, mobile and web experiences we’ve come to expect, but looking ahead to the programs that these new technologies will enable in the future.
That's what Infactory is building: a virtual AI engineer, one that unlocks the full potential of the technology to build better programs and usher in a new age of computing for all teams