Give CodeLynq a few lines — a meeting note, a written request, a production alert — and it specifies the work against your codebase, builds it, verifies it, reviews and security-scans its own diff, then opens a pull request. You approve the spec. You merge the code. Every step is logged.
Not sure it will work on your codebase? Send us one task from your backlog and we run it against your repository and send you the pull request. No account needed.
14-day money-back guarantee · Cancel anytime
Delivery pipeline
# run 2481 · repo: acme/mobile-app
✓ Analyzed repository structure
✓ Implemented TSD §2 — push token registration
✓ Added migration 0013_expo_push_tokens
✓ 14 tests passing
→ Opening pull request…
feat: push notification service
+412 −18 · 9 files · ready for review
4 checks before review
verify, self-review, security scan, docs
You approve, you merge
CodeLynq never merges or pushes to protected branches
SSO, SCIM, audit trail
and your code can stay on your own machines
Ways in
A few lines is enough. CodeLynq pulls the actual task out of whatever comes in, then runs the same reviewed pipeline regardless of where it started.
Paste raw meeting notes, or connect Fireflies or Plaud and let them arrive on their own.
Two lines in a text box. No template, no ticket hygiene, no grooming ceremony first.
Your monitoring posts an incident. With auto-run switched on and inside the cap you set, it can open a fix PR by itself.
A red build or a changes-requested review sends the run back to the same branch to fix its own work.
AI grooming turns the items nobody has scoped into specified, estimated, ready work.
Claude or any MCP client can create tasks, approve specs, start runs and trigger deploys through scoped, permissioned tools.
How it works
Most tools stop at the task list. CodeLynq carries the work all the way to a pull request.
A meeting note, a short written request, a production alert, a groomed backlog item, or a call from another AI tool over MCP. CodeLynq pulls the actual work out of whatever arrives.
The validation chat asks what was left unsaid: edge cases, scope, acceptance criteria. Ambiguity is resolved while it is still a conversation rather than a diff.
CodeLynq drafts a Technical Solution Document against your actual codebase, informed by what it has learned from your previous runs. You review, comment and approve. The architecture stays your call.
The agent implements the approved spec, then runs your verify command, reviews its own diff, scans it for security issues and updates the docs — fixing what it finds before a human sees it.
The PR lands in your normal review flow. When CI fails or a reviewer asks for changes, CodeLynq iterates on the same branch until it is green or it hits the limit you set.
You merge — CodeLynq never does. That merge can then trigger your own deploy pipeline and record the release. When something breaks later, live debugging works against your real environment through limits you set.
Product
CodeLynq is not another board to maintain. It does the work that usually falls between meetings and code.
Meeting notes and transcripts, a two-line written request, a production alert, a groomed backlog item, or another AI tool calling in over MCP. However work arrives, it lands in the same reviewed pipeline.
The validation chat questions every task before anyone builds it. Unknowns, edge cases and missing requirements surface while they are still cheap to fix.
Technical Solution Documents drafted against your actual repository, with full revision history. Engineers review the plan, not a surprise diff.
Before a pull request opens, the run executes your verify command, reviews its own diff, scans it for security issues and updates the docs — and fixes what it finds.
Failing CI or a changes-requested review sends the run back to the same branch until it is green or hits your cap. A merge can trigger your deploy pipeline and record the release.
Every run and every review adds durable facts about how your project actually works — conventions, layout, pitfalls — and later runs start from that instead of from nothing.
SAML 2.0 and OIDC single sign-on, SCIM user provisioning, enforced MFA, IP allowlists, admin/member roles, configurable retention, and an audit log you can export or stream to an HTTPS endpoint of your own.
A self-hosted runner clones your repositories locally and they never reach our servers. Bring your own model key and the spend and retention terms are yours too.
Loved by teams
“Our sprint planning meetings used to produce a wall of notes nobody read again. Now they produce reviewed pull requests by the end of the week.”
Sanne V.
Engineering Lead, B2B SaaS
“The validation chat catches the questions we would normally discover mid-implementation. Rework on small features has basically disappeared.”
Mark D.
CTO, logistics platform
“I approve the TSD, and the run does the rest. It feels like adding a junior developer to the team who never misreads the ticket.”
Julia K.
Product Engineer, fintech
Implementation
Adopting an AI developer works best with a plan. We work alongside your team to get CodeLynq into how you actually ship, from the first repo to an org-wide rollout.
We review your repositories, stack, and delivery workflow with your team and map where CodeLynq drives the most value first.
We connect your repos, codify your conventions, testing strategy, and verification commands, and configure SSO, roles, and run policies.
We run a hands-on pilot on real backlog tasks with your engineers, tuning specs and review flow until the pull requests land cleanly.
We train your engineers and leads on writing effective tasks and TSDs, reviewing AI pull requests, and getting the most from each run.
We help you roll CodeLynq out across teams with usage reporting, cost controls, and ongoing support as adoption grows.
Tell us about your stack and workflow and we'll put together a tailored implementation and enablement plan, pilot included.
Talk to our implementation teamPricing
Every plan is backed by a 14-day money-back guarantee. Upgrade, downgrade or cancel anytime from your billing settings.
For small teams shipping their first AI-assisted features
For product teams making AI delivery part of every sprint
For organizations running AI delivery across the whole portfolio
We build custom plans for larger engineering organizations. Tell us what your delivery pipeline looks like.
FAQ
An AI coding agent is software that takes a development task and carries it out on a codebase by itself: it reads the code, makes the change, runs commands and tests, and hands back the result. CodeLynq is an AI coding agent for teams: it works from a spec your team approved, checks its own work against your tests, a self-review, a security scan and your architecture rules, and ends in a pull request a person reviews and merges.
Connect a repository and give CodeLynq one of those requests. You will get back a specified, verified, security-scanned pull request to review.
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