Governed AI delivery for software teams
Inspect context before it reaches a model, keep team knowledge versioned in Git, control providers, and make AI coding usage visible across one shared workspace.
See Context Lens in action
The control gap
Individual adoption becomes shadow AI when the workflow is useful but invisible: no shared view of what is sent, which provider receives it, what it costs, or which private behavior has become team process.
Developers see the answer, but often cannot inspect the complete payload, memory, files, and instructions that shaped it before the request leaves their machine.
Plans, conventions, and reliable prompts disappear into personal chat history, so the team repeatedly teaches the same system to different models.
Multiple tools, accounts, models, retries, and oversized payloads create a workflow Engineering can use but cannot clearly operate as a system.
The three control surfaces
Signature surface
Inspect the exact payload, token budget, and memory blocks before sending. Clean, reorder, or trim context before it burns tokens or inflates cost.
See what the model actually receives, not just the response.
Trim noisy context before it weakens output or raises cost.
Result: less accidental context sharing, lower payload noise, and reviewable human oversight.
View technical documentation
Signature surface
Store reusable workflows in the repo, review them in Git, and let teams share operating knowledge as versioned artifacts instead of scattered prompts.
Turn prompts, plans, and conventions into reviewable assets.
Stop re-explaining architecture and team rules every session.
Result: team knowledge compounds in Git instead of disappearing into individual sessions.
View technical documentationSignature surface
Track tokens, cost, latency, tools, and files touched per call, per agent, and per skill so leads, finance, and security can optimize spend instead of just observing it.
Make spend and throughput visible where real decisions get made.
See which workflows are expensive, slow, or over-contextualized.
Result: Engineering can connect model usage to cost, latency, tools, and actual delivery workflows.
View technical documentation
From private usage to a team system
Developers use AI inside real delivery work.
The payload can be inspected before send.
Useful instructions become versioned repository artifacts.
Usage, providers, models, and cost become operable team signals.
Developer path
Team path
Evaluate current tools, provider risk, shared knowledge, and cost visibility, then scope a focused team pilot.
Platform capabilities
Keep the editor foundations, extensions, shortcuts, and source-control workflow developers already know while changing how the AI layer is governed.
Project memory and shared knowledge can live inside the repository, local providers are supported, and teams are never forced into a proprietary backend.
Run Knotic, OpenRouter, GitHub, Anthropic, OpenAI, or local endpoints, then route different AI roles to different models without changing the workflow.
Knotic inference is designed for throughput without arbitrary hour or day cooldowns in the middle of delivery.
Turn complex requests into step-by-step plans, execute them in sequence, keep state between steps, and avoid the mega-prompt mega-patch cycle.
Download and cache GGUF models, run local completions, and support air-gapped teams that need a serious path to private inference.
Collaborate on live AI sessions with permission controls for view or interactive access, turning AI work into a team surface instead of a solo chat log.
An honest category comparison
These products are valid choices for different workflows. Knotic is designed for teams that also need inspectable context, repository-owned knowledge, provider flexibility, and operational telemetry.
An AI-first editor built around fast individual coding workflows.
Read comparison →An agentic editor experience focused on developer flow and execution.
Read comparison →AI assistance integrated across the GitHub and editor ecosystem.
Product site ↗A capable terminal-based coding agent centered on Claude models.
Read comparison →Start free with Knotic, chat, and Context Lens. Paid plans add more credits for advanced workflows, stronger support, and team-oriented governance as usage grows.
Use the Knotic workspace, in-app chat, and Context Lens for free. If you bring your own provider or local endpoint, Knotic stays usable without a paid plan while runtime costs follow the provider you choose.
No subscription
Bring your own provider or local runtime, then add Knotic credits only when you need them.
Download Knotic freeYou want to use your trusted provider but still want to use our tools and workflow.
100 Prompts
Ideal if you want to test the basic features with no commitment.
300 Prompts
For power users who want the full Knotic workflow on a single seat.
650 Prompts
Designed for growing companies that need full control and flexibility.
1500 Prompts
06 - Common questions
Knotic is a VS Code-based AI workspace, not a single assistant panel. Planning, execution, context inspection, provider routing, shared knowledge, and telemetry live in one environment.
Most AI coding tools focus first on individual velocity. Knotic adds a team control layer: context inspection before send, repo-versioned knowledge, provider choice, shared sessions, and per-call telemetry.
Yes. Knotic supports BYOK and multiple providers, including OpenRouter, GitHub, Anthropic, OpenAI, and local runtimes. Teams can route different roles to different models without rewriting the workflow.
Yes. Skills as Code, project memory, specs, and reusable instructions can be stored as repository artifacts and reviewed through Git instead of remaining in private chat history.
Yes. Knotic supports local endpoints and a local model path for teams that need tighter control over inference and data movement.
Context Lens exposes payload size before send, while HQ Monitoring shows tokens, provider, model, latency, tools, and cost per call. Teams can find repeated prompting, oversized context, and expensive routing decisions.
Start with an AI governance assessment to map current tools, providers, data flows, and visibility gaps. A focused 30-day pilot can then establish a practical baseline with 5–10 developers.
Two ways to begin
Download the desktop workspace and complete the guided setup—or map your existing AI coding workflow before a focused team pilot.
