Lightspeed AI

An AI-driven SDLC applies AI at every stage of software delivery, not only at coding: requirements and design, code and review, test generation, test execution, release, and field support. Unlike a coding assistant, it treats the lifecycle as one connected system in which each stage passes context to the next and a human approves the work before it moves downstream. LTTS goes one step further by closing the loop, so field failures are diagnosed and converted into tests that feed the next release.

A coding copilot helps one developer inside one editor, and its scope ends at the commit. An AI-driven SDLC platform covers the stages a copilot never touches: requirements, architecture and detailed design upstream, then test generation, execution, release and field diagnosis downstream. It is adopted by an engineering organization rather than an individual, and it carries versioned artifacts and human approval gates so the work is auditable. The LTTS platform hands the actual coding to whichever agent your teams already use, including Claude Code, Codex, Cursor and Copilot.

Yes, provided the output is measured rather than assumed. LTTS scores every AI-generated test suite against human-written baselines across five dimensions, including concept coverage and structure match, and reports that score to the client. Average accuracy is currently above 60%, with 80 to 90% targeted once the agents are tuned to the customer's domain. Generated suites also cut test-generation time by 40 to 50%, and when run through distributed execution they shorten regression cycles by a further 40 to 50%, because broken automation is healed instead of rewritten.

Manual root cause analysis on a field defect typically runs three to five hours, which is why most defects are never fully diagnosed. AI automates the slow parts: parsing and normalizing logs, correlating events across timestamps and protocol layers, classifying the failure, and proposing a root cause with the supporting evidence attached. LTTS delivers 10x faster detection and reporting and 50 to 60% faster root cause analysis. For a North American telco this produced 25% fewer escaped connectivity issues and 35% faster remediation.

AI removes the manual effort concentrated at the two ends of the lifecycle. Design and specification work that took weeks is generated in hours, test cases are written from existing requirements instead of by hand, and field logs are diagnosed automatically rather than read line by line. Across LTTS engagements this delivers 30 to 40% less time from specification to code, 40 to 50% less time on test generation, 50 to 60% faster root cause analysis, and 35 to 45% faster time to market.

AI reduces defects in two ways. It generates test cases and executable scripts directly from requirements, design documents and APIs, so coverage tracks the specification rather than a tester's memory. It also analyzes field and lab logs to find failure patterns and converts them into pre-emptive tests before the next release. For one smartphone OEM, LTTS generated more than 40 test cases and matching scripts from roughly 20 field defects, cutting defect leakage by 20% and effort by 40%. Automated code review contributes around 35% fewer code issues.

Industries where software runs on a physical product and a defect costs far more than a patch. LTTS applies Lightspeed AI across automotive, telecom and consumer devices, industrial, medtech and aerospace, with field-diagnosis work concentrated in automotive infotainment and connectivity and in telecom modem and Wi-Fi log analysis. The platform is not OEM-specific: the pipeline stays the same and only the domain tuning changes, which is why the same approach covers a global power-management major's legacy modernization at 45% faster and a construction-equipment OEM's development program at a 45 to 50% productivity gain.

By adding AI around the existing toolchain instead of replacing it. The LTTS platform is roughly 90% pre-built, with about 10% configured per client for authentication, integrations and domain-specific agents. It connects to the Jira, Git and CI/CD systems already in place and hands coding to the agent your developers already use. It runs on AWS, Azure or on premises with open-source, in-house or commercial models, and the knowledge base is kept separate from the model so models can be swapped later without losing anything. Most clients start with a six-week pilot on one live program: a working proof of concept in month one, team adoption in month two.