Embed AI at every stage of your software delivery lifecycle — from requirements analysis and code generation to automated testing, intelligent code review, and deployment monitoring. Kansoft delivers AI-assisted engineering that ships faster, breaks less, and costs less to maintain.
AI tools in developers' hands aren't enough — the bottleneck is integration, workflow, and measuring actual impact.
Pull requests wait days for human review. Code-review bots flag issues instantly and let humans focus on architecture.
AI-generated test suites and mutation testing catch edge cases that manual test writing misses.
Legacy systems with no documentation slow onboarding. AI documentation generators explain code from source.
Teams hit productivity ceilings not from lack of people, but from toil: boilerplate, repetitive reviews, manual testing.
Code quality varies across developers and teams. AI linting and review enforces consistent standards automatically.
Without AI-assisted refactoring, teams can't keep up with technical debt while shipping new features.
Measured outcomes from production AI-assisted engineering teams.
AI-assisted user story refinement, acceptance criteria generation, and dependency mapping from natural language requirements.
GitHub Copilot / cursor integration, custom code generation templates, in-IDE AI assistant configuration for your stack.
AI code reviewers integrated into GitHub/GitLab workflows that flag security issues, style violations, and logic errors before human review.
Unit, integration, and E2E test generation from source code, with mutation testing to verify test quality.
Auto-generated code documentation, API docs, architecture diagrams from code, and onboarding guides for legacy systems.
Anomaly detection in deployment pipelines, AI-assisted incident triage, and root-cause analysis from logs and traces.
We embed alongside your engineering team rather than replacing your workflow — augmenting from within.
Map your current pipeline, identify bottlenecks, measure baseline metrics (PR cycle time, defect rate, deployment frequency, test coverage).
Select and configure AI tools for your stack (language, framework, CI platform). Establish developer access, security policies, and usage guidelines.
Integrate AI into highest-impact points first — typically code review and test generation. Measure against baseline metrics. Gather developer feedback.
Tune AI tool configurations based on pilot results, expand to additional SDLC stages, build custom prompt templates for your codebase.
Full team rollout, pair programming sessions, runbook documentation, and measurement dashboards handed to engineering leadership.
A SaaS platform integrated CodeRabbit and a custom LangChain review pipeline. AI handles first-pass review — security issues, dead code, and test coverage gaps — leaving humans to review logic and architecture only.
A FinTech company used Diffblue Cover to generate unit tests for a 200,000-line Java codebase with 12% test coverage. Coverage reached 68% in 3 weeks; regression bugs in the next quarter dropped by 45%.
A health technology company had a 150,000-line legacy Python codebase with zero documentation. AI documentation pipelines generated module-level docs, function docstrings, and onboarding guides. New developer ramp-up dropped from 6 weeks to 2.5.
Our consultants are practising engineers who've shipped production software. We don't advise on tools we haven't used in anger.
We baseline your SDLC metrics before starting and measure against them continuously. No vanity metrics — just PR cycle time, defect rate, and deployment frequency.
AI tooling configured for data residency, code secrecy, and audit requirements. GDPR, SOC 2, and regulated-industry configurations pre-built.
We train your engineers on effective AI prompting, review and correction workflows, and when not to use AI — not just tool installation.
We work across Python, Java, Go, TypeScript, .NET, and mobile stacks, and integrate with GitHub, GitLab, Azure DevOps, Bitbucket, and Jenkins.
Teams across India, UAE, USA, Europe, and Australia — same-day responses and workday overlap regardless of your timezone.
We configure AI tools with security-focused rules (OWASP, CWE top 25, SAST integration) and add a custom AI security review step to your pipeline. All AI-generated code is reviewed by our engineers before it reaches your team. We also integrate static analysis (SonarQube, Semgrep) to catch patterns AI tools miss.
We configure tools to use self-hosted or enterprise-tier options (GitHub Copilot Business, Continue.dev with local models) that don't train on your codebase. For regulated industries, we deploy local code-generation models entirely within your infrastructure boundary.
We run a structured change programme alongside the technical integration: developer workshops, pair sessions with our engineers, weekly retrospectives, and a gradual rollout strategy that starts with volunteer early adopters before scaling. The key is measuring and communicating wins early.
Yes. We integrate with GitHub Actions, GitLab CI, Azure Pipelines, Jenkins, and CircleCI. AI review and test steps are added as pipeline stages without requiring changes to your existing workflow structure.
We establish a baseline on four DORA metrics (deployment frequency, lead time, change failure rate, MTTR) plus PR cycle time and test coverage at the start. We report against these weekly and monthly, so you have objective evidence of impact.
