Make AI-powered delivery consistent across every team and project.
Stop relying on individual developer AI setups. Robin gives every developer the same proven prompts, the same workflow gates, and full audit visibility, at the org level.
Everyone can use AI for App Development. But how do you know that everyone delivers value that truly moves the needle, or pulls the cart in the right direction?
Robin is the governance layer that makes AI-powered development consistent, reviewable, and compounding.
Robin governs your AI-assisted development with proven prompts, workflow gates, and an audit trail, so developers stop losing time to setup, prompt wrangling, and rework on inconsistent output. See the time and money that returns to the team each year.
Set the three figures to match your team.
Time each developer loses today to project setup, prompt wrangling, and rework on inconsistent AI output, the work Robin standardizes away.
That is 4.0 developers of capacity handed back to shipping, from the same headcount.
Runs with your team's AI agentsThe hours figure is your estimate of what each developer loses to setup, prompts, and rework that Robin standardizes away. The 10x figure is an average from three client pilots on specific work, not a guarantee. Time is converted against a 48 week year and a 40 hour week. Figures are directional, meant to frame the conversation rather than replace a detailed assessment.
Your developers are already using AI. Some are using Claude Code. Some are using Cursor. Some are using Copilot. Each one configuring their own prompts, their own agents, their own quality bars. The result is inconsistent output, no institutional memory, and no governance trail.
When a senior developer leaves, their AI workflow goes with them. When a junior developer uses the wrong prompt, nobody catches it until review. Robin fixes both — by making the proven workflow the default for everyone, every project.
Whether you're starting from a brief, an existing codebase, or scoping quality assurance only, Robin has a workflow ready from day one.
Describe your project in plain language. Robin generates the tech spec, breaks it into prioritised features, creates implementation tasks, and produces test cases. Ready for your AI agent to execute.
Import your source code, documentation, document libraries, and DevOps or Jira boards. Robin maps the codebase, aligns it to its workflow structure, and picks up from where your team left off.
QA runs as its own project type, fully independent of development. IWConnect QA engineers use Robin this way today, on a client project where we do no development at all. Authoring, hooks, execution, evidence, and AI failure diagnosis, with zero changes to how your developers work.
Robin integrates multi-agent workflows for the full SDLC. Each step uses a pre-built prompt and the right agent automatically — you review and approve before anything moves forward.
Robin integrates multi-agent workflows for full SDLC
Primary design principle: 100% flexibility and independence from AI agents and tools. Robin owns the workflow and governance layer — you choose which AI agent executes each step. Switch agents without rebuilding your delivery process.
From brief, existing project, or QA scope
Refine BRs with stakeholder mapping and acceptance criteria
Break into prioritized features
Review and approve features
Implementation tasks with full code context
Approve tasks before execution
Robin, AI agents, and MCPs execute automatically
Plain language in, reviewed Gherkin out
Execute and monitor live test results
Repeats per task
Per-task governance: The approval gate operates task by task. Each implementation task is reviewed and approved by a human before Robin hands it to the AI agent for execution. Once a task is complete, Robin automatically queues the next one for approval, keeping every step traceable and every decision accountable.
The business requirement refinement engine surfaces open questions, stakeholder impacts, and acceptance criteria automatically, before a single line of code is written.
Describe the requirement. Robin generates the full spec (goal, users, scope, as-is flow, acceptance criteria) and flags the edge cases your team would have missed. Project documents live in the repo and stay reviewable before publishing.
Robin's task detail view shows which files to edit, which patterns to follow, and the exact implementation steps, with acceptance criteria and code notes attached. So AI agents produce quality output on the first pass.
Robin covers the complete software delivery lifecycle, from importing your project and refining requirements through to features, tasks, test cases, and test runs. Every step governed, traced, and visible in a single unified workspace.
Kick off from a business brief, onboard an existing codebase with its documentation and DevOps boards, or scope directly to a QA-only pipeline. All from the same starting point.
Every feature carries a full spec: overview, user stories, and GIVEN/WHEN/THEN acceptance criteria. Spec, design, tasks, and tests stay together per feature.
Every feature has a priority, status, and approval gate. Nothing proceeds without an explicit human sign-off, keeping delivery traceable from spec to code.
Each task carries the exact code context: which files, which patterns, which steps. AI agents and developers execute confidently without back-and-forth.
The QA engineer describes the test in plain language. Robin structures it into readable Gherkin, the engineer refines it, and Robin compiles it into a runnable Playwright script.
Each run returns pass or fail per step, the scenario, the technical script, and a video, with a screenshot at the exact failure point and traces.
Every tool claims AI writes your tests. The hard part is making those tests pass a real environment: cookie walls, password-gated storefronts, OTP, and bot detection that blocks a script for clicking faster than a human. Demo pages have no security layer. Your staging environment does. Robin is built for yours.
Automated tests open a fresh incognito session on every run, so every single test hits cookie consent, privacy acceptance, storefront password gates, and login before it even starts. A hook performs that setup once, captures and injects the session cookie, and every test calls it as a prerequisite. One captured session stays valid from hours to a month, depending on the cookie type.
Define your environments once - staging, production, storefront - and every flow runs across them. Credentials and other reusable values live as variables, so a stored username and password is referenced by every test instead of hardcoded into each one. Run secrets are injected as environment variables, stored encrypted, and never returned to the browser.
Modern sites run layered detection: OTP steps, Clerk authentication, services that flag a user for clicking too fast. Good for the end customer, the single biggest problem in test automation. Robin's Cookie Manager and Security Layer pass these checks and emulate human timing, so your test gets judged on what it validates, not blocked for being a script.
Where QA teams actually lose time is triaging whether a red test is a flaky selector or a real bug. Run AI Diagnose analyzes a failure from the error alone, with no hint from the engineer. Deep Diagnose scans a wider structure when the first pass isn't enough. Every failure arrives with the evidence attached: the exact locator, the wait time, a screenshot at the failure point, traces, and a video of the run.
Scenarios stay in Gherkin, so a BA or PM can read exactly what a run validated, step by step, without touching the code. Under the readable scenario, Robin's generated Playwright script carries the waits, locators, and human-behaviour emulation the run actually needs.
Robin's QA covers UI testing today. Backend, API, and integration testing are on the roadmap as necessary companions to the UI work. We'd rather tell you the current boundary than let a demo do it.
Each scenario has the prompts, configs, and templates already built in. You describe what you want. Robin runs the process.
Describe the project. Robin generates the tech spec, breaks it into features, creates tasks, and builds the skeleton. Ready for implementation in your AI agent of choice.
Import your source code, documentation, document libraries, and DevOps or Jira boards. Robin maps the codebase, aligns it to its scenario structure, and picks up from where your team left off.
Describe the feature. Robin handles spec, breakdown, and implementation using your existing project context. Without disrupting what's already working.
QA runs as its own project type, fully independent of development. IWConnect QA engineers use Robin this way today, on a client project where we do no development at all. Authoring, hooks, execution, evidence, and AI failure diagnosis, with zero changes to how your developers work.
Point Robin at the old code. It reverse-engineers the system, generates documentation and a migration plan, so your team knows exactly what they're moving and why.
No documentation? Robin reads the codebase and generates structured docs, dependency maps, and system understanding, so your team can finally see what they inherited.
Robin owns the workflow, the governance, and the prompt catalog. The AI agent is a configurable plug-in. Switch it out without changing your delivery process.
Key governance principle: AI that generates without governance is just risk at scale. Robin gives every developer the same proven prompts, the same workflow gates, the same audit trail, regardless of which AI agent they prefer.
Robin is for organizations where software development is a core competency and competitive advantage. And where AI adoption needs to scale without sacrificing quality or control.
Stop relying on individual developer AI setups. Robin gives every developer the same proven prompts, the same workflow gates, and full audit visibility, at the org level.
The prompt determines the quality of the code. When every developer uses the same proven prompts, experience level matters less. And code quality becomes consistent.
No time lost figuring out prompts, configuring agents, or setting up projects from scratch. Robin has it all pre-built, so you can focus on shipping, not setup.
Fill this in and we’ll respond within 1 business day to schedule a 30-minute live walkthrough — no sales pitch, just the product working on a real requirement.
Practical answers about Robin, AI-powered software delivery, SDLC governance, AI coding agents, requirements refinement, task generation, test cases, and human approval gates.
Robin is IWConnect’s AI software development orchestration platform. It governs how teams use AI coding agents across the software delivery lifecycle, from business requirements and features to tasks, implementation workflows, test cases, and test runs.
Claude Code, Cursor, GitHub Copilot, and similar tools help generate or edit code. Robin sits above those tools as a governance and orchestration layer. It standardizes requirements, prompts, context, approval gates, task structure, testing flow, and auditability across multiple AI coding agents.
Robin is designed to orchestrate AI-assisted development rather than remove human control. It can help prepare specifications, features, tasks, prompts, implementation workflows, test cases, and test runs, while keeping review and approval points in the delivery process.
Robin governs AI-powered development by keeping requirements, project context, task definitions, agent instructions, human approval gates, test cases, outputs, and delivery history in a structured workflow. This makes AI-assisted development more consistent, reviewable, and reusable across teams.
Robin supports the software delivery lifecycle from business specs, requirements, and feature definition through task generation, implementation workflows, test-case creation, test execution, and delivery review. It helps teams move from idea to working software with clearer governance and handoffs.
Yes. Robin can support existing projects by helping teams understand code context, organize project knowledge, define new requirements, generate structured tasks, prepare AI-agent instructions, and support modernization, extension, QA, or reverse-engineering workflows.
Yes. Robin can start from a business spec or requirement and help refine it into clearer requirements, features, acceptance criteria, implementation tasks, test cases, and test runs. This helps reduce the gap between business intent and technical execution.
Human approval gates are review points placed between important SDLC stages. Teams can review requirements before task generation, approve features before implementation, validate test cases before execution, and keep humans responsible for decisions that affect scope, quality, architecture, or production readiness.
Robin is designed to support multiple AI coding agents and developer tools instead of locking teams into one provider. It can work as an orchestration layer for agent-assisted development workflows using tools such as Claude Code, Cursor, GitHub Copilot, and other AI development assistants.
Robin is built for software engineering teams, product teams, technology leaders, QA teams, modernization teams, platform teams, and organizations adopting AI coding agents but needing more consistency, governance, traceability, and reusable delivery patterns.
Teams can start with Robin by choosing one delivery path: a new project from a business spec, onboarding an existing codebase, or starting with QA-only coverage. From there, IWConnect can configure the SDLC workflow, approval gates, agent setup, requirements flow, task structure, and testing process.
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