ComputationalSoftware, applied AI, and computational engineering for problems that reach beyond the browser.
Computational Training Labs connects algorithms, hardware, and data infrastructure. We help turn technical uncertainty into systems your team can understand, operate, and extend.
Discuss a projectEmbedded systems / Applied AI / Data infrastructure / Scientific computing
The algorithm is one part of the work. Interfaces, deployment, and the conditions in which a system runs matter just as much.
Design the boundaries, interfaces, and operating model before complexity becomes expensive. Assess existing systems, resolve integration problems, and turn research code into maintainable software.
Architecture reviews · API and system design · Performance analysis
Build model-driven systems around a defined task and a measurable baseline. Develop Model Context Protocol (MCP) servers and integrations that connect agents to tools, data, and existing software, with explicit permissions, evaluation criteria, and human review where decisions require it.
Agent workflows · MCP development · Tool integration · System evaluation
Bring computation closer to the equipment and signals it serves. Develop software around real limits in memory, power, connectivity, and latency, from sensor interfaces to on-device inference.
C/C++ and Rust · RTOS · Sensor fusion · Hardware-in-the-loop testing
Make data usable from ingestion through analysis and deployment. Engineer batch and streaming pipelines with validation, reproducible transformations, and visibility into failures and operating costs.
Data pipelines · Cloud architecture · Observability · MLOps
Translate a technical hypothesis into an experiment that can be reproduced and assessed. Develop computational methods, instrument prototypes, and investigate the gap between a promising result and a usable system.
Feasibility studies · Numerical methods · Benchmarks · Prototyping
Develop systems that extract useful information from images, video, and sensor data. Build and evaluate deep learning models for detection, segmentation, tracking, and visual inspection, accounting for lighting, scene variation, and the hardware that will run them.
Detection and segmentation · Visual inspection · Multimodal learning · Edge inference
Develop training pipelines and model architectures around the data, compute budget, and intended use. Scope foundation model pretraining, adapt existing models through fine-tuning and post-training, and evaluate quality and failure modes before deployment.
Dataset curation · Pretraining and fine-tuning · Distributed training · Model evaluation
Each stage should answer a question and leave behind something useful. The depth of the work follows the problem.
Establish the operating conditions, constraints, and acceptance criteria.
Scope, risks, and a testable definition of success.
Test the uncertain parts with focused experiments and representative data.
A working prototype, measurements, and a decision record.
Build the interfaces, tests, and instrumentation needed for real operation.
Versioned software, repeatable builds, and deployment procedures.
Document the system and work through operation with the team maintaining it.
Runbooks, technical documentation, and a practical handoff.
Good engineering makes a system easier to reason about—not just easier to demonstrate.
Computational Training Labs brings scientific software, hardware R&D, and applied AI into one engineering practice. We work across embedded devices, cloud infrastructure, model training, and agent workflows, with attention to the interfaces that connect them.
An engagement can start with a feasibility study, an architecture review, or a prototype that needs to become dependable software. We connect experiments to implementation through reproducible benchmarks, explicit design decisions, and tests under actual operating conditions.
For AI systems, that includes evaluating model behavior, defining tool permissions, and deciding where human review belongs. For edge and cloud systems, it means accounting for compute limits, data quality, and recovery from failure. In both cases, the goal is software your team can inspect, maintain, and extend.
An architecture review, an uncertain prototype, or a system that needs to work outside the lab. Start with what you know and what still needs to be resolved.
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