An agent at every stage of software delivery.

Automate the path from requirement to production — planning, coding, review, security, testing, and release — while your engineers keep approval at merge and deploy.

Start a Project See the approach
sdlc / pr-2481 · invoice searchrunning
6 steps · 1 human gate0 / 6
01 / Problem → solution

Where teams get stuck, and how we engineer past it.

THE PROBLEM
  • 01Manual handoffs between ticketing, review, testing, and release slow every change.
  • 02Test coverage and security checks depend on who happens to pick up the work.
  • 03Production signals rarely flow back into planning in a structured way.
OUR APPROACH
  • 01Connect agents to your tracker, repositories, and CI so each stage produces a reviewable artifact.
  • 02Keep humans in control at merge and release, with every agent action visible in the pull request.
  • 03Feed observability data back into the next plan to close the loop.
02 / What we build

What a ai-powered sdlc engagement delivers.

01

Planning agents

Turn requirements into scoped tasks, acceptance criteria, and change designs.

02

Coding & review

Feature-branch implementations and review suggestions against your conventions.

03

Test generation

Unit and integration tests generated and executed in CI.

04

Security gates

Dependency, secret, and policy scanning before merge.

05

Release automation

Canary rollouts, release notes, and rollback triggers.

06

Feedback loop

Production health and usage signals routed back to planning.

TECHNOLOGY
  • GitHub
  • GitHub Actions
  • GitLab
  • Jira
  • Argo CD
  • OpenTelemetry
03 / Representative solutions
REPRESENTATIVE SOLUTION

AI SDLC automation

01 / 06
PROBLEM
Delivery slowed by manual handoffs between ticketing, code review, testing, and release.
ARCHITECTURE
An orchestrator coordinating planning, coding, review, and testing agents over GitHub and CI, with human approval at merge and release.
WHAT WE BUILT
An agent pipeline from ticket to pull request, automated test generation, policy checks, and generated release notes.
BUSINESS OUTCOME
Shorter path from requirement to production, with every agent action reviewable by the team.
TECHNOLOGYLangGraphGitHub ActionsOpenTelemetryPostgreSQL
REPRESENTATIVE SOLUTION

AI observability platform

06 / 06
PROBLEM
LLM features in production lacked visibility into quality, latency, and cost.
ARCHITECTURE
Tracing for every model call, evaluation pipelines, and cost attribution by feature.
WHAT WE BUILT
Dashboards, regression evaluations in CI, and alerting on quality drift.
BUSINESS OUTCOME
AI behavior that can be measured, compared, and governed.
TECHNOLOGYOpenTelemetryMLflowPrometheusGrafana
04 / More solutions

Explore other solutions.

What would you automate if your engineering systems could think?

Tell us the workflow, platform, or operational challenge. Softmatic can help design and build the intelligent system behind it.

Start a Conversation Talk to an Engineer