This project tackles the difficulty of ensuring next generation AI hardware and Machine Learning (ML) frameworks meet strict performance and stability requirements before production rollout.

CIeNET supported the development of a next‑generation ML and High‑Performance Computing (HPC) platform by conducting comprehensive performance qualification and stability verification for advanced AI hardware (TPU and GPU). Our work included building automated benchmarking pipelines for leading ML frameworks such as JAX, PyTorch, and MaxText, as well as designing validation workflows and metric‑driven gating mechanisms to ensure new hardware and software releases consistently met production‑grade reliability and performance requirements.

By introducing automated log‑analysis tools, key‑metric extraction, and visual performance dashboards, CIeNET reduced the validation cycle from roughly one week to about two days. These improvements accelerated the rollout of new AI compute capabilities, enhanced process consistency, and significantly lowered manual engineering effort.

Two solutions that addressed the challenge of slow code reviews, inconsistent AI-generated code quality and inefficient SQL query generation.

CIeNET built Code Assist for faster, higher‑quality code generation by benchmarking different assistants such as Claude.ai, ChatGPT, Copilot and Gemini, therefore helping the customer understand where the areas of improvement are. It involved comparing different AI foundation models, code generation quality checks, verifying AI engine proficiency, triage of issues, and bug-fix/feature development.

CIeNET also created an NL2SQL solution capable of generating accurate SQL queries from natural‑language prompts. This work included building comprehensive test cases to evaluate SQL generation quality and efficiency, performing cross‑model performance analysis against other LLMs, and authoring high‑quality NL‑to‑SQL datasets for model training and refinement, covering multiple data warehouse and database environments such as BigQuery, Redshift, Databricks, Snowflake, MySQL, and PostgreSQL.

These solutions were supported by CIeNET’s GAINS (Generative AI beNchmark System) framework to evaluate LLM accuracy and reliability.

Fleet operators struggle with unsafe driving behaviors. It demands high cost, manual effort and long testing time. CIeNET delivered an AI‑powered driver‑safety system with real‑time alerts, distraction detection, and fleet behavior analytics.

By lowering testing time, automating manual tasks, and improving overall fleet safety, we were able to create a more stable and mature solution, which ultimately resulted in reducing high‑risk driving events.

Most telecom base station inspections are manual, slow, and prone to errors, especially across large national or global networks.

CIeNET developed an AI powered inspection platform that automates the detection of weather proofing failures, corrosion (including rust), and broader structural defects across base station sites. To ensure global coverage and local compliance, the platform is deployed on AWS in overseas regions and on InSuite within China.

This solution enabled faster and more consistent inspections, reduced manual workload and errors, and lowered maintenance costs while improving both infrastructure reliability and personnel safety.

Supported a commercial product and services AI company in developing a suite of intelligent IoT solutions. These solutions addressed challenges such as inconsistent and long manual inspections in hardly accessible places such as pipelines, electrical poles, and the bottom of swimming pools, and limited real‑time visibility into physical assets like insects and operational inefficiencies in community laundry rooms where faults, abnormal behavior, and machine downtime often go unnoticed.

By applying computer vision, embedded AI, and continuous sensing, it resulted in higher mapping and detection accuracy (>90% for Pool Mapping and Insect detection), reduced inspection time by four therefore lowering labor costs (Drone Inspection, Pipeline Repair) and improved equipment monitoring and automated fault detection for Intelligent Laundry systems.

The RC‑AI platform is an end‑to‑end, agent driven recruitment solution designed to streamline the entire hiring lifecycle. From resume intake to technical interviews, it standardizes diverse CV formats into a unified structure, enables JD‑based candidate sourcing using internal knowledge bases, and automatically matches resumes to job descriptions through fit‑score evaluation. The platform also includes an AI interviewer agent capable of generating role‑specific interview questions, conducting interviews, transcribing candidate responses, and assessing technical competency.

Built on a layered architecture combining LLMs, structured databases, and domain‑specific agent modules, RC‑AI reduces manual screening effort, shortens time‑to‑hire, and improves evaluation consistency across sourcing, assessment, and decision‑making.

These solutions address core challenges faced by engineering and validation teams across automotive and other industries that rely on complex, software-driven systems: manual testing, debugging, and content verification are slow, inconsistent, and difficult to scale across complex software systems. CIeNET’s AI & Agent-driven automation suite enhances software quality and engineering productivity across the entire validation lifecycle.

C-GTS – Automated Test Case Generation
Software testing teams frequently spend significant time interpreting long requirement documents. C-GTS adopts agent‑driven automation test-case generation from structured and unstructured documents. It reduces test-case creation time by 30-50%, ensures consistent requirement coverage, and minimizes human interpretation errors.

AFA – Automated Failure Analyzer

Traditional debugging requires engineers to manually inspect logs and traces, slowing down root cause identification. AFA automatically parses logs, applies similarity search, and retrieves prior solutions. This shortens debugging cycles by 30-50%, reduces repeated investigation of known issues, and produces consistent failure reports for certification processes.

SCV – Smart Content Verification

Verifying UI/HMI/UX digital content is repetitive and prone to missed defects. SCV uses AI-based pattern detection to automate UI and content verification. Teams experience 40–60% reduction in manual verification workload, improved detection of visual inconsistencies, and higher regression reliability.

Soak BOT – Agentic Soak & Endurance Testing

Long duration tests require continuous monitoring and repetitive execution. Soak Bot automates natural language task parsing, execution scheduling, verification, and report generation through agent‑driven workflows. It reduces manual soak testing effort by 40-60%, improves reproducibility for long-run scenarios, and ensures real-time monitoring and structured reporting for auditability.

Together, these solutions enable engineering teams to detect defects earlier, shorten release cycles, and improve overall product reliability — leading to faster validation, reduced labor cost, and significantly higher test coverage across complex software systems.