Every idea, in production, in hours.
IdeaFlow brings AI coding into your company — with the tools you already use: project tracking (Jira, etc.), knowledge bases (Confluence, etc.), Claude Code and more, in one structured workflow.
AI can write code. It can't own it.
The hard part of AI-assisted development isn't speed — it's keeping quality high, making ownership clear, and giving every change a responsible reviewer. That comes from process, not a prompt.
One-shot AI tools
- Code appears — but nobody owns it
- Quality is assumed, never verified
- No clear responsibility for what ships
- Nothing on record when something breaks
IdeaFlow
- Every change has an owner and a responsible reviewer
- Quality gates at each step — dev, QA, security, stakeholder
- Clear responsibilities across the whole lifecycle (ADLC)
- Full accountability trail: who approved what, and why
From "I have an idea!" to production
IdeaFlow runs a full Agent Development Lifecycle (ADLC) — a structured, controlled loop built for AI that writes code: an inner loop that ships, and an outer loop that keeps improving.
"I have an idea!"
Natural-language input. Automatic ticket creation in your project tracker (Jira, etc.), or a standalone flow. Success criteria & expected behavior are captured up front.
Context Engine
The AI reads your project tracker, knowledge base (Jira, Confluence, etc.), meeting transcripts (Fireflies, etc.), customer feedback and your codebase. Through harness engineering, a purpose-built agent harness — context, tools & guardrails — is shaped around the task before any code is written.
AI builds + preview
Automatic branches across all repos. A live preview link for stakeholders & customers. Your project tracker is updated.
Expert + Customer review
Dev, Tester, Security, Stakeholder, Customer — in parallel. Feedback → context for the next iteration.
Merge + docs
Approve, merge, a doc is auto-generated in your knowledge base. Your project tracker closes the ticket. Changelog ready.
The outer loop · Monitor & Improve
Steps 1–5 are the inner loop that ships. The outer loop keeps it healthy: post-deploy monitoring, errors and customer feedback flow back as context, and the success criteria set up front become the baseline the work is measured against — feeding the next iteration. Inner loop plus outer loop is what makes this a complete ADLC, not a one-shot generator.
Each role sees exactly what it needs
One unified platform, a tailored experience per role.
Idea → Result
Sees only the live preview. Communicates in natural language. Never sees code.
- Natural-language input, like Lovable.dev
- Real-time preview of changes
- Iterate: "Make it blue", "Add step"
- Request expert review with one click
- Final approval before merge
Code + Tools
Sees the full diff and branch context. Uses the tools they already know — and can take the feature wherever they want.
- Full diff + branch view
- Claude Code, Cursor, VS Code
- Custom MCP plugins
- Continue the feature yourself — code anything, any way
- Push changes back to the branch
- Technical quality sign-off
Quality Gates
Auto-generated tests, coverage gaps, edge cases — all in one view.
- AI-generated test suggestions
- Coverage analysis
- One-click test runs
- Edge case review
- QA approval gate
Preview & Approval
Sees the live preview link, project-tracker status (Jira, etc.), and knowledge-base output (Confluence, etc.). No code, no technical knowledge required.
- Live preview link — opens in the browser
- Project-tracker ticket status in real time
- Comment & approve from the preview
- Auto-generated knowledge-base page
- No code visibility needed
Everyone sees the same changes at the same time — Dev, Tester, Security, Stakeholder and Customer. Customer feedback closes the loop automatically for the next iteration.
Plugs into the tools you already have
Every plugin connects via MCP. IdeaFlow doesn't replace your tools — it becomes the orchestration layer between them.
Jira
Auto-create & track tickets. The AI updates status as it works. A stakeholder preview link on every ticket.
Linear
Modern issue tracking. Auto-assign, labels, cycle sync with AI progress.
GitHub Issues / Asana
Native PR linking, auto-close on merge. Timeline tracking for business roles.
Confluence
Reads PRDs & specs before coding. Post-merge: auto-generated feature docs, changelog, API notes.
Notion / Google Docs
Import specs & PRDs. Output meeting notes, feature summaries, decision logs.
Sharepoint
Enterprise doc ingestion. Compliance-ready output for regulated industries.
Fireflies.ai / Otter.ai
Auto-transcribes meetings. Extracts action items, decisions and feature requests — automatic context for the AI.
Zoom / Teams AI
Native meeting summaries. Decisions from standups & planning sessions are integrated automatically.
Intercom / Zendesk / Surveys
Customer feedback is collected, categorized and fed in as context for new features.
Claude Code
Full agentic coding via MCP. Deep codebase understanding, autonomous multi-file changes.
Cursor / Windsurf
The dev expert makes inline edits on the same branch using the IDE they already know.
GitHub Actions / Sentry
CI/CD triggers on every AI commit. Post-deploy monitoring — errors feed back to the AI.
Every plugin integrates via MCP (Model Context Protocol) — the emerging standard for AI tool integration. If your tool isn't on the list, we can build a custom connector as part of the Integration Service.
Bringing IdeaFlow into your company
A hands-on service, not a download. We wire IdeaFlow into your stack, tailor the workflow to how you work, and get your team productive — setup, custom services and training, end to end.
Setup & integration
We connect IdeaFlow to the tools you already run.
- Repos: GitHub, GitLab, Bitbucket & Azure DevOps
- CI/CD, project tracker & meeting-bot integration
- Multi-repo orchestration & MCP connectors
- Fast, guided setup — typically days, not months
Custom workflow & services
We tailor the process and build what's missing.
- Custom workflow & expert-routing design
- Custom MCP plugin development
- Compliance & audit-trail configuration
- Architecture guidance (harness engineering, ADLC)
Training & enablement
We get your whole team confident and productive.
- Onboarding for business & dev roles
- Hands-on training sessions
- Best-practice playbooks & docs
- Guidance for both business and engineering
Plus dedicated support with an SLA, and ongoing optimization as you scale.
Built for real engineering teams
No black box. MCP-based orchestration, sandboxed environments and full traceability — wired into the tools you already run.
Model Context Protocol
Every tool, repo and data source connects over MCP — the open standard behind native integration with Claude Code, Cursor and your own servers. No bespoke glue per tool.
Agent harness
Harness engineering assembles context, selects tools and applies guardrails per task — the agent works inside boundaries you define, not a free-for-all.
Git-native multi-repo
Coordinated branches across GitHub, GitLab, Bitbucket & Azure DevOps. Frontend + backend + infra move together in one feature, as PRs your team already reviews.
Sandboxed preview
Ephemeral, isolated environments spun up per branch. Stakeholders and customers open the same preview link — real running software, not screenshots.
Anthropic Claude
Claude as the reasoning backbone: code, test generation, PR descriptions and review. Model routing lets you match model to task and cost target.
Guardrails & policy
Permissions, data boundaries and write access are validated before rollout. Sensitive actions wait for human approval — safety by design, not bolted on.
Observability & audit
Every decision, change and approval is traced and logged. Post-deploy signals and errors feed the outer loop. Compliance-ready audit trail.
Plugin bridge
An abstraction layer over every external tool via MCP connectors. Swap or add tools without breaking the workflow — no conflicts, full flexibility.