Automatically complete AI solutions for the most complex challenges
Keplore's Super Research Engineer System (SRES) delivers complete, validated AI solutions for AI/ML engineering, robotics, edge, and other complex AI teams—turning experiment-heavy challenges into deployment-ready systems across cloud, edge, and heterogeneous environments.
What SRES Actually Does
The Super Research Engineer System (SRES) is an automated research-engineering system designed to deliver complete, validated AI solutions for complex, experiment-heavy problems, especially when there's no clear solution path by automating the complete research-engineering lifecycle end to end.
Capabilities
Understand your problem & environment
You set the goal and context; SRES ingests your assets to map options.
Design and run experiment pipelines
It proposes hypotheses and runs structured experiments automatically.
Orchestrate infrastructure, drivers, and tools
SRES sets up runtimes and executes across your compute stack.
Validate and package system-level solutions
A validation layer checks performance and regressions, iterating until it returns a deployment-ready system for integration.
The output is a complete, production-ready software system, not partial code, tooling, or recommendations, delivered faster and more reliably than manual R&D can achieve with acceptance proof and regression tests built in.
SRES handles large projects in robotics and edge/embedded AI, delivering high-quality, repeatable, validated solutions that would be impractical for human teams to complete reliably at scale.
You define the problem and success criteria. SRES executes the complex research and engineering work required to deliver a finished solution.
Who It's For
Built for AI/ML engineering teams that need a standard research-engineering system to repeatedly find, validate, and deliver complex AI solutions across environments.
AI/ML Engineering Teams
Automatically find research, run experiments, train and tune models, and optimize performance, completing the full validation loop based on pre-defined goals and constraints, so teams can quickly get proven AI/ML solutions.
Edge/Embedded AI Teams
Get models running on edge hardware—NPUs, GPUs, microcontrollers, and AI accelerators—in hours instead of weeks, dramatically speeding up experimental development and hardware integration.
AI Startups/Product Teams
Turn ideas into production-ready solutions at a fraction of the usual time. Spend more time training custom models with faster repetition, and leave environment setup, dependency management, and data versioning to SRES without hiring specialized research engineers.
Advanced Research & Development Teams
Explore new, innovative AI solutions while SRES handles reproducing past results, setting up experiment environments, debugging, and validation. Ensures key knowledge is instantly reproducible and transferable, accelerating the transition from a proof of concept to a scalable product.
Problems We've Helped Solve
Here are examples of teams using SRES to deliver AI solutions that would have been impractical to develop through human-only R&D workflows.
Lighthouse Robotics - A robotics innovation hub located in China
Lighthouse faced limited research/engineering capacity, fragmented testing environments, and slow pipelines. SRES delivered an end-to-end reproducible workflow that expanded capacity, accelerated iteration, and strengthened experiment + validation.
Senior AI Systems Engineer - Vision Model Deployment & Optimization with Qualcomm NPU
Traditional Qualcomm NPU optimization can take weeks due to fragmented runtimes and manual performance tuning. SRES analyzes NPU-relevant research, targets bottlenecks, benchmarks end to end, and delivers a deployable, hardware-optimized solution.
Additional Target Use Cases
We're pursuing projects where teams need faster iteration and proof-driven validation across real environments, including:
Production ML systems with repeatability + regression requirements
Hardware-constrained deployments (latency / memory / runtime limits)
Multi-environment workflows where "works on my machine" remains a blocker
WHY SRES
SRES –  | Inference Infrastructure (Modal, Fireworks) | MLOps (Weight & Bias, Data Brick) | Vibe Coding (Cursor, Claude) | Foundation Models (GPT, Gemini) | |
|---|---|---|---|---|---|
| Foundation Model Consumption / Access | |||||
| Vibe Coding / AI- Assisted Programming | |||||
| Experiment & Asset Management (MLOps) | |||||
| Execution & Inference Infrastructure (via integration) | |||||
| System-Level Solution |
Ways To Work With Us
Keplore for Teams and Enterprises
Let's work together to help your team find validated solutions to your business's most complex AI/ML engineering problems.
Design partner environments
for your business's needs
High-impact projects where experimentation and exploration are bottlenecks
Deliver complete, validated AI systems ready for deployment
Keplore for Experts and Researchers
Use our agentic environment to find and validate solutions through sophisticated AI/ML experiments.
Compare and select the best LLM/VLM for
your use case (quality/cost/latency)
Build experiment environments fast and efficiently
Explore different iterations through reproducible testing grounds
Connect your data and grow with our system
Explore and run your first experiment today
GET STARTED