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When code is cheap to generate, checking it becomes the bottleneck.
Requirements shift in meetings while tickets and tests stay the same.
Generated code can pass basic checks and still miss the business need.
The backlog, repository and CI each show part of the status.
Test coverage is pieced together at release or audit.
We pair AI assistance with clear acceptance criteria, quality targets, review gates and a live record of evidence.
Turn requests into behaviors, edge cases, acceptance criteria and quality targets before coding starts.
Use specialized agents for code, tests and documentation while engineers review design and dependencies.
Link unit, integration, security and AI test results to each requirement, and flag gaps automatically.
Place our engineers inside your team to build the first releases with you, then hand over the code, the agents and the playbook.
Track lead time, deployment frequency, failure rate and recovery time, and each agent's effect on them.
We review one product stream and find where requirements, code and tests drift apart.
Our engineers join the team and set up criteria, evidence links, agents and review rules in its tools.
We refine the process with production feedback, then transfer it to your engineers.
Builders and reviewers share one definition of done.
Missing code or tests show up before release day.
Our engineers ship real features with your staff.
Your engineers own the process when we leave.
Plans, code, tests and deployments connect.
Changes to code, prompts or models are retested.
Reports show what is built and tested.
DORA metrics show whether AI is helping.
Client engagement, anonymized
An insurance and healthcare technology organization wanted AI across software delivery without losing control of quality or security. We assessed its delivery maturity, wrote a lifecycle playbook and specified twenty delivery agents, each with a purpose, triggers, governance rules and an expected effect on DORA metrics. Leaders also received a KPI prototype and a roadmap from requirements to release evidence.
A software delivery process in which AI assists at every stage while outputs stay traceable and reviewed. It adds quality targets, agent governance and automatic traceability to the standard SDLC.
Set acceptance criteria before coding, require human review of generated code and dependencies, run automated security and quality checks, and link every change to its requirement and tests.
AI assistants for specific delivery tasks, such as writing user stories, generating tests, reviewing code or drafting release notes. Each should have a narrow job and a human reviewer.
Track the four DORA metrics: lead time, deployment frequency, change failure rate and time to restore service. Compare them against a baseline.
An engineer who works inside the client's team, on its systems and real problems, instead of building from a distance. Ours build the first releases with your staff and leave the code, agents and process with them.
We will trace it from requirement to release, show you the gaps and put an engineer on the first fix.
Review your development lifecycleOr email us at admin@pointonezero.com.