Quiver GTM

Quiver GTM is an agentic developer marketing system built for technical founders, developer marketing teams and the agents working alongside them. Its purpose is to run developer marketing like an engineering system rather than a set of disconnected tactics, keeping product context, customer evidence, campaigns, content, tasks and results connected in one controlled system. Quiver gives developer marketing the architecture engineers expect: a source of truth, version history, explicit states, APIs, observability and feedback loops. Everything a team learns, creates, ships and measures stays connected, so each cycle improves the context and the decisions behind the next one without the system changing behind the user's back. Quiver is available as a hosted product or as a free, MIT-licensed self-hosted edition, and it runs on the user's own model account. The problem Quiver addresses is not a lack of ideas. According to Quiver, marketing is usually handed to technical founders as vibes and disconnected tactics: another chat has the positioning, a document has the plan, customer evidence is somewhere else, content loses its history when it ships, and performance gets reported and then disappears before the next decision. Quiver's answer is to give the whole operation state, structure and memory so that people and agents can work inside the same controlled system, because "just post more" is not an architecture. Importantly, Quiver does not train a mystery model on the company. It preserves the evidence, decisions, shipped work and results that should inform what happens next, and it keeps the human in control of what becomes part of the system. Quiver is not a metaphor painted over a chatbot; the site describes its primitives as the operating properties of the product. The first is a source of truth for product context: positioning, ICP, messaging, customer language, proof points and hypotheses live in one active context that every agent can use. The second is version control for decisions with history: every context and artifact change is versioned and restorable, so agents can propose updates while the human decides what becomes true. The third is a set of state machines that create a real production workflow. Work moves through Draft, Review, Approved, Live and Archived states instead of losing finished work inside chat history, which means generation and production are never collapsed into a single, uncontrolled step. The remaining primitives cover how the system connects outward and how it learns. Interfaces come in the form of a Content API plus MCP: approved content is published as structured JSON, and the agents a team already uses can operate Quiver through a real tool surface, with a ready-to-connect MCP endpoint secured through OAuth or scoped tokens. Observability keeps work tied to outcomes, so the plan, research, content, tasks and performance stay connected and a team can trace what shipped and what happened next. Feedback loops make the system improve: teams log the outcome, capture what worked, and review proposed context changes before that learning shapes the next cycle. Day to day, Quiver offers purpose-built Strategy, Create, Feedback, Analyze and Optimize sessions, or the option to connect an external agent through MCP; either way, the work lands in the system instead of disappearing with the conversation. Customer evidence feeds the system rather than sitting in a folder: calls, surveys, reviews and field notes are turned into themes, Voice of Customer quotes, product signals and evidence for or against active hypotheses, and that language is then made available to the agents doing the next piece of work. Content is treated as infrastructure rather than a text box, keeping its versions, publish state, SEO and social metadata, distribution history, repurposing lineage and metrics, supported by a content calendar. Campaigns link sessions, research, content and results, and the hosted edition adds built-in tasks, assignments and reminders. Quiver's runtime is built around keeping the work connected. A team starts by giving the system context, meaning the product, audience, positioning, customer language, proof and hypotheses, rather than opening a blank chat window. From there, research, sessions, artifacts, content and tasks stay connected to the initiative they are meant to move forward. Work ships through explicit states: agents create, humans review and approve what is true, and finished work is published without collapsing generation and production. Finally, results are fed back in: the outcome is measured, the learning is preserved, and the context and decisions behind the next cycle improve with human approval. Getting started follows the same logic: connect your own model provider account, then paste your website or describe the product so Quiver can draft a starting context for review. Because context and artifacts keep version history and explicit state, agent proposals never silently become truth; a person approves what enters the active context or moves from draft to live. The stated benefit is a marketing operation with a system of record instead of a context that has to be rebuilt every cycle. Teams no longer have to maintain a separate knowledge base by hand, because Quiver can propose updates from the research, creation and measurement activity the team is already doing. Every session and connected agent starts from the same approved, versioned source of truth, and the resulting work is linked to campaigns, publishing states and results, so the reasoning, approvals and learning that individual tools tend to leave disconnected are preserved. Quiver does not replace a CMS, CRM or analytics tools; it acts as the context and decision layer around them, while the Content API lets approved work be served as structured JSON so the company's own site keeps control of presentation. Concrete workflows described by Quiver include managing positioning and product context in one place, processing customer research into themes and Voice of Customer quotes, planning campaigns, creating and reviewing artifacts, coordinating tasks, publishing approved content and logging performance. A founder can paste a website or describe the product to bootstrap the context, connect an Anthropic, OpenAI, Google, OpenRouter or OpenAI-compatible account, and then open a session, connect an MCP client, or begin with research, with every action starting from the same approved context and writing back to the same system. When work goes live, Quiver creates the reminder to measure it, so the team logs quantitative results and qualitative notes, synthesizes what worked, and reviews proposed context updates before they affect future sessions. Quiver is explicitly aimed at technical founders, developer marketing teams and the agents working alongside them. It ships in two deployment shapes. Self-hosting gives the MIT-licensed foundation for free, forever, with unlimited seats on your own infrastructure: you host the app and database, maintain the deployment and bring your own model account, and you deploy and expose the MCP server code yourself. Hosted plans start at $49 per month for Founder (up to three seats, $490 billed annually) and $99 per month for Team (unlimited seats, $990 billed annually), with two months free on annual billing, a 14-day trial requiring a card, and cancellation any time. Hosted adds a ready-to-connect MCP endpoint using OAuth or scoped tokens, built-in tasks, assignments and reminders, a ready-to-invite shared workspace, and managed authentication, infrastructure and updates. Both editions support BYOK with per-job model selection. The takeaway Quiver repeats is simple: stop rebuilding the context. By giving developer marketing a system of record, with versioned product context, explicit production states, a Content API and MCP interface, observability into what shipped, and feedback loops with human approval, it lets a team and its agents operate from the same approved system. Quiver is not another AI writing tool; it is the structure around the models you already choose, so that the evidence, decisions, shipped work and results of one cycle become the context and better decisions of the next.