Future AGI - Built for End-to-End AI Development 💡

CV Deep Dive

Today, we’re talking with Nikhil Pareek, Co-Founder and CEO of Future AGI.

FutureAGI is tackling one of the biggest bottlenecks in AI development: the slow, unpredictable, and often opaque process of iterating on AI models. While AI has advanced rapidly, most teams still rely on slow, manual workflows to test, evaluate, and deploy their models—leading to long development cycles, unreliable performance, and costly failures when models don't behave as expected in production.

FutureAGI provides an end-to-end developer platform that integrates directly with popular AI frameworks like LangChain and LlamaIndex, allowing teams to quickly prototype, evaluate, and monitor AI systems. The platform’s core innovation is its automated evaluation layer, which enables teams to test models dynamically and refine them in real-time, without waiting weeks for human feedback. By reducing iteration time from days to minutes and making AI development more systematic, FutureAGI is helping companies move from experimentation to scalable deployment faster than ever before.

In this conversation, Nikhil discusses why AI evaluation is the missing piece in enterprise AI adoption, how FutureAGI is building tools that enable smaller teams to compete with tech giants, and why AI-driven software development is the future.

Let’s dive in ⚡️

Read time: 8 mins

Our Chat with Nikhil 💬

Nikhil, welcome to Cerebral Valley! First off, introduce yourself and give us a bit of background on you.

Hey there! I’m Nikhil, one of the founders at Future AGI. I’ve been working in AI for most of my life. My first job involved developing autonomous agents for drones—drones that could land, navigate, communicate with each other, and scan entire areas for inspection. From the start, AI fascinated me because it was more than just coding. Drones, in particular, combined hardware and software in a way that felt like the perfect balance.

After that, I knew I wanted to build something of my own—to create a company and push the boundaries of what AI could do. I needed to develop a diverse set of skills beyond just the technical side, so after that, I moved into consulting. I worked with a firm that consulted for Roche, BMS, and Pfizer, helping them make sense of patient-level data. This involved a range of data science tasks—forecasting, building knowledge graphs—back when we didn’t have all the advanced ML models or open-source tools we do today.

In 2020, I started my first startup in the computer vision space. The idea was simple: we have CCTV cameras everywhere—how can we detect crime in real time? Scaling that was a challenge, but we built a solid product and were fortunate enough to sell the entire software and IP. After that, I became CIO at another startup where we developed highly efficient OCR models. These models achieved state-of-the-art accuracy, outperforming AWS and Google’s offerings in our specific tasks. Just to give perspective, our models ran at about $1K per month, whereas using Google's services for the same tasks would have cost $50K—all while delivering higher accuracy.

Coming from those experiences, what motivated you to co-found Future AGI?

Eventually, I wanted to move beyond enterprise solutions, which led me to co-found Future AGI. One of the biggest challenges in AI is that it’s never a one-and-done job. It’s an iterative process. In my first startup, I spent six months just collecting diverse data variants—recordings of crimes happening in real time, annotating them, refining the dataset. And even after all that, the first deployment still failed. That experience shaped how I think about building AI today.

The world around us is incredibly dynamic, and when you train these algorithms, you can never be sure they'll work on diverse datasets or edge cases you hadn't considered. Data alone isn't enough—you need continuous monitoring with a human in the loop to ensure the AI is predicting accurately. Someone has to oversee what the model is producing, validate its outputs, and keep refining it.

While working on my startup, I realized that if AI is going to scale—whether to a five-person team or to become the next wave of software alongside LLMs and agents—it needs a much faster iterative loop. Just like in traditional software, you should be able to test cases, monitor performance in production, and refine models rapidly. The current approach, where you send data to an external annotation vendor and wait for updates, is too slow. In AI, 80% of the effort goes into data and quantitative analysis, so making this process more efficient is key.

That’s where Future AGI started. We set out to solve this problem—helping teams build AI faster and more reliably. But we didn’t jump straight into building. We spent one to two months just talking to experts in the field, asking how they tackled these challenges. The most common answer? "We have humans to check it." That was the starting point for what we wanted to improve.

How would you describe Future AGI to the uninitiated developer or AI team?

We are an end-to-end developer platform designed for building with LLMs, agents, and prompt-based applications. Our platform provides SDKs that allow developers to quickly get started with any of the popular frameworks like LangChain, LlamaIndex, and others. You can easily download our SDKs, start prototyping rapidly, and iteratively refine your models.

One of our key innovations is in evaluation. We’ve built a system that allows for highly customizable evaluations based on specific use cases. For example, if you're working on a summarization model, you can simply ask, "How good is my summarization?" and our system will evaluate the output accordingly. This enables developers to continuously test and improve their models in a structured way.

Beyond prototyping and evaluation, we offer standardized observability through OpenTelemetry. Once your application is built, you can deploy it while maintaining full visibility into its performance. What sets us apart is that our evaluations are both cost-effective and highly accurate, leveraging our own in-house trained models. Traditional LLM-based evaluation methods typically add around 40% to your application’s costs, whereas our approach keeps overhead at just 2-3%. This makes our platform highly scalable and accessible for teams looking to develop and refine AI applications efficiently.

Who are your customers today? Who is finding the most value in what you're building at Future AGI? 

Our primary customers are Cross functional teams who want to work together in developing and shipping a highly accurate enterprise grade AI application. The key distinction we use internally is that AI should have a direct dollar value attached to it—it shouldn't be a gimmick or an internal experiment.

The other key factor is scale. If an AI system only runs one or two inferences per day, it’s not a critical problem for the company. But when AI operates at a scale of, say, 500 million inferences per day, it becomes essential. That’s where we focus—on AI that is both revenue-impacting and deployed at scale.

Walk us through Future AGI’s product. What use-case should people experiment with first, and how easy is it for them to get started? 

The journey on the platform begins based on the current stage of the ML team. Let’s say you’re a team just starting to build your AI application. You’d begin by defining your dataset—this can be synthetic or your own data. Then, you can start prototyping your agentic application using our UI or SDKs. You can define evaluations to measure agent performance and quickly iterate to continuously improve your agents.

Once the application reaches a reasonable level of accuracy, teams typically add a final layer of human review, which is also supported directly within the platform. After deployment, they can use our “Observe & Protect” feature to monitor performance in production and ensure the outputs remain high-quality and safe.

So, all in all, getting started is incredibly simple—just three lines of code. You can quickly register and integrate with popular frameworks like LangChain and Llama Index, embedding our platform seamlessly into your workflow. We’ve seen how setting evals can be a complex process, but on our platform, we’ve made it quite straightforward and it can be done in minutes

You simply attach evaluations to your nodes, describing your use case in natural language. If you're working with a RAG system, for example, you just select the relevant RAG metrics, attach them, and you're ready to go.

Which existing use-case for Future AGI has surprised or delighted you the most? How are you measuring the impact or results that you’re creating for your customers? 

Many of our customers come to us after facing the same recurring issue: their AI models fail during a demo or when the client tries to use them in a real-world setting. The other major challenge they encounter is measuring performance and optimizing development time.

One of the key success metrics we track is how quickly teams can develop on our platform compared to traditional workflows. In many cases, we've helped teams achieve up to a 10x speed improvement. For instance, in a recent proof of concept with a marketing team, their typical process involved a subject matter expert writing a prompt, developers building the agents, and then exporting results into an Excel sheet for review. This back-and-forth cycle would take days before they could iterate. With our platform, they were able to test and refine their models in real time, removing the inefficiencies of slow human feedback loops.

Beyond development speed, we also measure deployment satisfaction and how frequently teams adjust their models post-launch. A strong indicator that our platform is working is when teams can rapidly iterate after deployment, monitoring performance for a short period before refining their approach. Ultimately, the biggest ROI for our customers comes from saving time and improving accuracy, allowing them to build AI solutions that are both reliable and efficient.

Given the excitement around new trends in AI such as Agents and Multimodal AI, how does this factor into your product vision for Future AGI? 

We believe AI will become the new software, fundamentally changing enterprise workflows at scale. Large enterprises—companies valued at $10 billion or more—are already rethinking their existing processes, moving towards agentic pipelines that offer more efficiency and flexibility.

This shift won’t just be about automation—it will transform interfaces, operations, and workflows entirely. AI won’t replace software as we know it overnight, but its trajectory is clear. Enterprises, however, require deterministic and reliable AI systems. They can’t afford purely probabilistic models without guarantees on performance.

To bridge this gap, we're developing the necessary tooling—evaluation frameworks, experimentation tools, and monitoring systems—to ensure AI systems operate with the accuracy and reliability that enterprises demand. At the same time, we're making it possible for small, agile teams of four to five developers to build sophisticated agent-based systems with maximum efficiency. Robust tooling is key to making AI a scalable and trusted replacement for traditional software.

What has been the hardest technical challenge around building Future AGI into the platform it is today? 

The first thing we created was a very strong evaluation layer because we saw it as a horizontal tool that would be applicable across all products. Whether you’re prototyping or deploying, you need evaluation at every stage. We spent a fair amount of time researching this and even secured a couple of patents in multimodal evaluation.

We started with two key principles. First, we wanted it to be future-proof. The field is evolving so fast that any architecture we commit to today could quickly become outdated. Whether it’s new training techniques or emerging methodologies, we needed something that could adapt. Second, it had to be customizable for all use cases. We saw other companies working on evaluation struggles because they went too deep into just one technology or one specific use case. We made sure our foundational layer could evolve as the industry progresses.

You might have heard about something called distillation—where you take a bigger model, extract the best parts, and use them to refine a smaller model. We worked on that early on, along with runtime testing. When we started, there was no widely accepted idea of using LLMs as judges. We had a hard time convincing customers that this worked. The concept of AI evaluating AI was scary for many—how could they trust an AI system to judge the quality of another AI? But over time, this approach proved itself, and our technical bets paid off.

One of the core ideas we focused on was AI judging AI, using larger models to distill smaller models, and ensuring scalable architectures. Another key decision was prioritizing open-source models over closed-source ones. We anticipated that pricing would be a major concern for customers, so we wanted to ensure our approach was both cost-effective and scalable.

Initially, the product wasn’t immediately understood. The idea that users could simply type their use case and have the system evaluate it seemed too abstract. Customers wanted more control, not just an automated judgment system. We took that feedback seriously and kept iterating. At one point, we deployed five different variations of the product across five different companies, gathering feedback to refine the approach. That rapid iteration cycle helped us shape the product into something that truly resonated with users.

We built on two core technology layers: leveraging open-source models and distilled models while integrating best practices like chain of thought reasoning and self-reflection. This allowed the system to continuously improve as new data came in, making it highly specialized and effective.

As we developed the platform, customers started asking if we could handle images as well—particularly those working on ad campaigns who needed validation for AI-generated images. Thanks to our modular architecture, we were able to support image evaluation in just two weeks. The flexibility of our design allowed us to expand quickly while maintaining accuracy and reliability.

How do you see Future AGI evolving over the next 6-12 months? Any specific developments that your users/customers should be excited about? 

Our main priority right now is getting the word out about what we’ve built. We only recently started commercializing—just about a month and a half ago—so the focus is on bringing in as many customers as possible, gathering feedback, and iterating quickly. Over the next three to four months, that’s the primary goal: refining the product through real-world use.

By the end of the year, based on the traction we’ve seen with our current POCs, we’re confident we’ll hit at least $1 million ARR. Beyond that, we’re preparing for a fundraising round in about four months, ensuring we have strong momentum and a compelling case for investors. Those are the two main priorities right now—growth and setting up for the next phase of scaling.

How would you describe the culture at Future AGI? Are you hiring, and what do you look for in prospective team members joining the Future AGI? 

We place a strong emphasis on culture. From day one, we’ve built a team where everyone takes full responsibility for what they’re building. A strong sense of ownership is core to how we operate.

Talent is another major focus. Every member of our team has demonstrated something exceptional in the past. Many have worked in top research labs at companies like Microsoft and Google, and we have team members who have published in CVPR. The goal is to bring together people who not only understand cutting-edge research but also know how to create things that haven’t been built before.

Right now, we’re growing fast and have a team of 27 people, including a number of highly talented interns alongside our full-time staff.

Conclusion

Stay up to date on the latest with Future AGI, learn more about them here.

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If you would like us to ‘Deep Dive’ a founder, team or product launch, please reply to this email ([email protected]) or DM us on Twitter or LinkedIn.

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