Quiver GTM

Quiver is an agentic developer marketing system built for technical founders, developer marketing teams and the agents working alongside them. It gives developer marketing the architecture engineers already expect: a source of truth, version history, explicit states, APIs, observability and feedback loops. The system connects product context, customer research, campaigns, content, tasks and performance in one controlled place, so everything a team learns, creates, ships and measures stays connected rather than scattering across chats, documents and separate tools. Quiver is offered in two forms: a managed hosted product with built-in tasks and managed team access, and a free, MIT-licensed self-hosted edition that teams run on their own infrastructure. Quiver starts from a blunt observation about how marketing usually arrives at a technical founder: as vibes and disconnected tactics. One chat holds the positioning, a document holds the plan, customer evidence lives somewhere else, content loses its history the moment it ships, and performance gets reported and then disappears before the next decision is made. The website frames the gap directly, noting that just posting more is not an architecture. The practical consequence is that each marketing cycle tends to restart from scratch rather than compound on what came before, because nothing preserves the evidence, the decisions and the results in one place. Quiver responds by giving the whole marketing operation state, structure and memory, so people and agents can work inside the same controlled system instead of rebuilding context every time. The foundation of Quiver is a set of primitives presented as the actual operating properties of the product rather than a metaphor painted over a chatbot. The first is a source of truth: positioning, ICP, messaging, customer language, proof points and hypotheses all live in one active product context that every agent can draw on. The second is version control for decisions: every change to context and artifacts is versioned and restorable, agents can propose updates, and a person decides what becomes true. The third is a state machine for production workflow: work moves through Draft, Review, Approved, Live and Archived, so finished work is not lost inside chat history. Together these primitives mean the same context and the same production discipline that engineers expect from their own systems are applied to marketing work. Two further primitives connect Quiver to the rest of a team's stack and to real outcomes. Interfaces: Quiver publishes approved content as structured JSON through a public Content API, and an MCP interface lets the agents a team already uses operate Quiver through a real tool surface. That combination allows Quiver to remain the source of truth while a company's own website keeps ownership of presentation. Observability: the plan, research, content, tasks and performance stay connected, so a team can trace what shipped and what happened next instead of treating reporting as a dead end. Feedback loops then close the cycle: outcomes are logged, what worked is captured, and proposed context changes are reviewed before that learning shapes the next cycle, with humans approving what enters the system. The runtime is organized around the idea that the system gets better because the work stays connected. Quiver does not train a mystery model on a company. Instead it preserves the evidence, decisions, shipped work and results that should inform what happens next, and keeps the team in control of what becomes part of the system. The work runs in four steps. First, give the system context: start with the product, audience, positioning, customer language, proof and hypotheses rather than a blank chat window. Second, operate the work: research, sessions, artifacts, content and tasks stay connected to the initiative they are meant to move forward. Third, ship through explicit states: review what agents create, approve what is true, and publish finished work without collapsing generation and production into one step. Fourth, feed results back in: measure the outcome, preserve the learning and improve the context and decisions behind the next cycle, with human approval at the point where proposals become truth. Viewed by function, Quiver describes itself in four ways. For your agents, it provides operators rather than chat tabs: agents get durable context, operational state and tools, and teams can use purpose-built Strategy, Create, Feedback, Analyze and Optimize sessions or connect an external agent through MCP, with the work landing in the system instead of disappearing with the conversation. For your content, it is infrastructure rather than a text box: content keeps its versions, publish state, SEO and social metadata, distribution history, repurposing lineage and metrics, and the Content API serves approved work as structured JSON. For your customer evidence, it is research that changes the system: calls, surveys, reviews and field notes become 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. For your results, it is a loop that actually closes: when work goes live, Quiver creates the reminder to measure it, teams log quantitative results and qualitative notes, synthesize what worked, and review proposed context updates before they affect future sessions. Getting started follows three bootstrap steps. First, initialize the context: paste your website or describe the product, and Quiver drafts the starting context, which you review and make your own by checking the assumptions. Second, connect your model: bring an Anthropic, OpenAI, Google, OpenRouter or any OpenAI-compatible account and choose the model that fits each job, with your provider account determining model cost and the policy governing model usage. Third, start operating: open a session, connect an MCP client or begin with research, knowing that every action starts from the same approved context and writes back to the same system. Several boundaries are stated explicitly. Quiver is not a replacement for a CMS, CRM or analytics tool; it describes itself as the context and decision layer around those systems, using its Content API, MCP interface and publishing workflows to preserve the reasoning, approvals and learning that individual tools often leave disconnected. It also differs from using a standalone model chat directly, because a standalone chat starts with only the context supplied to that conversation, whereas Quiver gives every session and connected agent the same approved, versioned source of truth and then connects the resulting work to campaigns, publishing states and results. It also distinguishes approved knowledge from assumptions: context and artifacts keep their version history and explicit state, and agent proposals do not silently become truth, because a person reviews and approves what enters the active context or moves from draft to live. On deployment, Quiver offers two paths. The Open Source edition is the self-hosted foundation for technical founders and startups comfortable owning the stack, priced at $0 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. The hosted editions are run for you. Founder costs $49 per month for up to three seats, or $490 per year billed annually with two months free, and is described as a ready-to-use shared system for a technical founder and up to two teammates. Team costs $99 per month with unlimited seats, or $990 per year billed annually with two months free, and is the expanding hosted team product with room for the whole company. Founder and Team differ only by seats. Hosted plans add built-in tasks, assignments and reminders, a ready-to-invite shared workspace, managed authentication, infrastructure and updates, and a ready-to-connect MCP endpoint with OAuth or scoped tokens. All hosted plans include a 14-day trial, card required, cancel any time. Across editions the same capability list applies: versioned product context with restore, Strategy, Create, Feedback, Analyze and Optimize modes, versioned artifacts with approval and publish states, campaigns linking sessions, research, content and results, customer research synthesis and a Voice of Customer library, hypothesis evidence tracking, a content calendar with SEO/social metadata and repurposing, distribution records and content metric history, a public Content API for approved content, performance synthesis and reviewed context proposals, and BYOK with per-job model selection. The takeaway Quiver reinforces throughout is simple: stop rebuilding the context. By keeping context, work, decisions and learning connected in one approved system, a developer marketing operation gains a system of record that both the human team and its agents can operate from, so each cycle improves the context and the decisions behind the next instead of starting over.