Our Mission

Bridging Earth Science and Artificial Intelligence

IPS CeresAI was founded to solve a critical issue: modern farms generate vast streams of parameter data, but lack the computational context to translate them into actionable, crop-saving decisions.

Origin Story

How IPS CeresAI Began

Launched in late 2025 by a team of agronomists, remote sensing researchers, and software engineers, IPS CeresAI set out to build model pipelines tailored specifically to agricultural field parameters.

Having operated self-funded for the last 6 months, we've deployed telemetry probes and collected drone multispectral sets across 12 partnership cooperatives, proving the technical viability of leaf disease early-warnings.

Seed Capital Round

Investor Pitch Deck Overview

We are raising a $1.5M Seed Stage round to scale H100 deep learning training servers, expand regional drone operations, and secure 4 additional agronomy researchers.

Request Pitch Deck Access

Key Milestones

NOVEMBER 2025

Company Incorporation

IPS CeresAI officially registers operations. First seed code written for drone multispectral stitching engines.

JANUARY 2026

Beta Test Site Launch

Deploy IoT soil probes and launch automated drone flights across 3 premium wheat crop trials.

APRIL 2026

IPS CeresAI SaaS v1.0

Release initial web dashboards. Leaf rust early detection models achieve 94% validation accuracy.

CURRENT STATE

NVIDIA Roadmap Scaling

Self-funded expansion phase. Integrating TensorRT inference models to optimize API request latencies.

Our Guiding Principles

Sustainability Commitments

Our technologies align precision mapping with long-term ecological stewardship.

Targeted Input Reduction

By mapping spatial health variance down to sub-meter resolution, IPS CeresAI saves cooperatives an average of 22% in fertilizer and nitrogen applications.

Smart irrigation water conservation monitoring

Irrigation Conservation

Combining soil probe moisture depth tracking with evapotranspiration models reduces water extraction requirements across large crop zones.

Climate Resiliency

Our algorithms track heat index thresholds, allowing farmers to select seed varieties that survive local weather stress curves.

Scientific Leadership

The Core Team

A collaborative mix of agricultural experts, machine learning engineers, and data operators.

Dr. Amit Verma

CO-FOUNDER & CEO

12+ years in AgriTech systems and predictive modeling. Former researcher at national institute of agronomy.

Dr. Sarah Chen

CHIEF MACHINE LEARNING ENGINEER

Ph.D. in Remote Sensing. Specialized in computer vision pipelines and multispectral segmentation.

Rajesh Patel

HEAD OF AGRONOMY OPERATIONS

Managed drone-based spatial surveys across 50,000 hectares of diverse crop variants.

Priya Sharma

LEAD GEOSPATIAL DATA SCIENTIST

Expert in Sentinel-2 spectral indices calibration and satellite temporal gap interpolation.

Vikram Singh

SENIOR IoT SYSTEMS ARCHITECT

Designed low-power cellular soil telemetry probes and sensor mesh ingestion gates.

Neha Gupta

PRINCIPAL SOFTWARE ENGINEER

Optimized real-time PyTorch inference serving on AWS Cloud Run containers.

Arjun Rao

CHIEF FLIGHT COMMANDER

DGCA certified drone operator with 800+ hours surveying multi-spectral crop trials.