# Quack Stack - Full Context > Continuous product intelligence for teams that build. ## What Quack Stack is Quack Stack is a platform that gives product teams a shared, always-current picture of what to build and why. It listens to your market, customers, competitors, and team conversations, then synthesises that intelligence into a shared picture everyone can query - so the full context that a four-person startup has naturally scales to any size team. Signals flow in, prioritised work flows out, evidence flows back. The system covers the full product lifecycle: discovery, validation, experiment design, development coordination, and growth. Think of it the way CI/CD works for code. Before CI, engineers manually checked whether their changes broke anything. After CI, the system handled it continuously. Quack Stack does the same thing for product intelligence - continuous access to the shared picture of what to build and why. ## The problem we solve Small teams have a superpower: everyone knows everything. The customer calls, the competitor moves, the half-formed idea from yesterday's standup - it's all shared context. Decisions are fast because the full picture is in the room. Then you grow, and that superpower disappears. Context fragments across people, tools, and channels. AI has made this worse, not better. Building has got a lot faster, but every AI session starts cold. Engineers have always made product calls every commit - now they're doing it with less context than ever. PMs and designers create PRs directly but lose the customer signal that should have shaped the prompt. The wall between "deciding" and "building" has collapsed, but the context didn't follow. Product tools still assume someone writes a spec and hands it off. Engineering tools still assume someone gives you a task and you build it. Neither fits a world where everyone does both. Quack Stack sits in between. One context layer. Every surface. Every role. ## Who it's for Product teams that have outgrown "everyone knows everything" but don't want a heavyweight process to replace it. PMs, engineers, designers, and founders who need shared context across people, tools, and AI agents. ## How it works 1. **Collect** - Customer interviews, support tickets, competitor moves, and team notes flow in continuously from your existing tools. 2. **Understand** - Signals become ranked opportunities traced to real evidence. Expert agents pressure-test every assessment from four domain lenses. 3. **Validate** - Experiments with hypotheses and kill criteria. Structured interviews. Decisions grounded in evidence before engineering begins. 4. **Deliver** - Engineers query signals from the CLI or their IDE. PMs get Slack briefs. AI agents pull full context via MCP. 5. **Measure** - Shipped features are tracked automatically. Outcomes sharpen priorities. The loop closes. --- ## Features ### Discovery **Competitor Tracking** - Discover and track who else is solving this problem. AI surfaces competitor suggestions from your research findings. Accepted competitors feed into strategy documents and experiment design. Includes both direct competitors and substitute threats. **Substitute Threat Detection** - Find what customers use instead, not just who competes directly. Customers often solve problems with tools, workarounds, and services that look nothing like your product - spreadsheets instead of project management software, WhatsApp groups instead of community platforms. These substitutes are often the real competition. **Research Synthesis** - AI-generated analysis that turns raw findings into a clear verdict. Takes your entire findings library and produces a structured analysis - themes, patterns, confidence levels, and a graduated verdict on whether the problem you're investigating is real. Generated by Claude Opus. **Interview Library** - All your customer conversations in one place, with quotes that link to opportunities. Every interview lives in one place with its full transcript, extracted quotes, engagement scores, and ICP fit assessment. Quotes are automatically matched to relevant opportunities and experiments using semantic similarity. **Product Feature Extraction** - Automatically catalogue your product's features from your docs and blog. The AI investigator uses your feature catalogue to avoid suggesting opportunities for things you already have. ### Strategy **Strategy Wizard and Quick Start** - From one sentence to a complete validation strategy. Quick Start takes a single sentence and generates a full project in minutes. Wizard walks through a guided questionnaire for more control. Both produce: Ideal Customer Profile, positioning documents, competitor analysis, SWOT, message house, and a sequenced experiment plan with kill criteria. **Product Vision** - A North Star and principles that every decision can reference. Sits above all other strategy docs. As experiments run and evidence accumulates, the system matches outcomes to your principles automatically. Principles that keep getting validated grow stronger. Principles that contradict the evidence get flagged. **ICP Pivot** - Change your target segment and everything follows. Promote a new ICP segment and the cascade handles the rest: ICP, positioning, and messaging docs regenerate overnight. Discovery queries shift to target the new segment. The investigator re-evaluates opportunities through the new lens. **Strategy Document Generation** - AI-generated strategy docs from your research and answers. Documents include ICP, positioning, competitor analysis, message house, and red list. Updated conservatively by the nightly cycle - not overwritten. ### Experiments **Experiment Board** - Your command centre for all validation work. Experiments grouped by validation stage (Problem, Demand, Solution, Product). Kanban and list views with filtering by state, stage, or type. **Landing Page Builder** - Create the artefacts your experiments need. Pages built from configurable sections - hero, value propositions, social proof, signup forms, pricing tables, feature lists. No design skills required, no external tools. **Landing Page Hosting** - One-click publish to a live URL. Pages go live at validate-idea.com, ready to receive traffic. Form submissions captured automatically and routed back to the experiment's results. **Interview Guides** - Structured preparation for qualitative experiments. Auto-generated from experiment hypotheses. **Experiment Execution Modes** - Every experiment classified as in-app (built in Quack Stack), manual (run outside Quack Stack), or code (needs code changes). Routing and notifications adapt accordingly. **Learning Loop** - Each experiment makes the next one smarter. Sequential experiment learning where outcomes feed forward. ### Platform **Overview** - Your living market assessment. Reads everything the system knows - research, competitors, experiments, strategy, team knowledge - and distils it into a single, opinionated briefing. Regenerates every night. Verdict uses three decisions: pursue, pursue with caution, or kill. **AI Agent** - An always-on agent that monitors your experiments and takes action. Watches experiments continuously, flags kill criteria, analyses conversions, sends morning digests. Operates through Slack with expanding autonomy. **Nightly Intelligence Cycle** - Wake up to fresh research, updated strategy, and a morning brief. Runs your entire research and analysis pipeline automatically overnight: extracting findings, updating strategy docs, sweeping competitor activity, re-evaluating opportunities, and generating a fresh assessment. **Opportunities** - Capture, evaluate, and promote opportunities before they become experiments. The AI investigator agent assesses each one with a confidence score, recommendation, supporting evidence, and channel suggestions. Grouped by readiness: ready to test, trending, new, or stale. **Expert Panel** - Four domain specialists that sharpen every AI assessment. Lenses: customer discovery, growth strategy, positioning, and pricing. Embedded in prompts automatically - they inform the investigator, the overview, and the synthesis pipeline. **Home Dashboard** - Everything you need to know about your validation, in one view. At-a-glance summary with sequence state, status cards, active experiments, and upcoming tasks. Designed to orient you in under thirty seconds. **Measuring Phase** - Ship it, measure it, decide what's next. Every shipped opportunity enters measurement automatically. Four structured outcomes: positive (ship), improving (iterate), negative (archive or iterate), guardrail breach (iterate immediately). **Decision Audit Trail** - See who changed what, when, and why on every opportunity. Full lifecycle history with actor identity and surface. **Semantic Search** - Find anything across your research by meaning, not just keywords. Vector search via pgvector and Voyage AI across opportunities, experiments, findings, features, team knowledge, growth log, and interview quotes. **Workspaces** - Isolated environments for teams, clients, or separate ventures. ### Integrations **Connect Your AI Tools (MCP Server)** - Use Quack Stack directly from Claude, ChatGPT, Cursor, or any MCP-compatible tool. Your AI tools get full product context automatically. Ask about customer signals, opportunity evidence, or why a feature exists - right where you're already working. Authentication uses OAuth 2.1, secure and per-user. **Command-Line Interface (@quack-stack/cli)** - One binary, install with `npm install -g @quack-stack/cli` and `quack login`. Works in Codex, Claude Code, Cursor, Aider, or any plain shell. Same intelligence as the MCP server, scriptable from anywhere. Device-flow OAuth, token written to `~/.config/quack/config` with 0600 permissions. **Slack Product Guide** - A product guide that lives in your channels. Morning briefs, proactive signals, and answers to questions. The guide has full context of your research, strategy, and experiments. **Customer Support Ingestion** - Turn support conversations into product intelligence automatically. Connects to Zendesk, Intercom, or HelpScout. Each conversation is triaged for product relevance, then broken down into structured evidence: customer quotes, pain points, feature gaps, and competitor references. **Meeting Ingestion** - Automatically pull customer conversations from your meeting tools. Currently supports Granola. Transcripts processed into structured quotes with topic tagging, speaker attribution, and automatic linking to opportunities and experiments. **Autonomous Agent Dispatch** - Let AI agents claim and ship opportunities from your backlog. Pull mode: any MCP-connected agent can ask for the next task, claim it, and submit a PR. Push mode: Quack Stack sends tasks to Cursor's background agents. Both modes provide full context (opportunity spec, evidence, experiments, product vision). **Meta Ads** - Run Facebook and Instagram ads without leaving your dashboard. Connect your ad account with OAuth, build campaigns from experiment specs, generate creative assets with AI, search Meta's interest taxonomy, and watch results flow back automatically. **GitHub** - Automatic PR state sync and shipped feature detection. When a PR merges, shipped features enter measurement automatically. ### Analytics **Conversion Analysis** - Automated pattern detection across experiments. **Reports** - Share validation progress with stakeholders. --- ## Pricing Quack Stack is pre-launch and onboarding design partners. Pricing is set per partnership while we calibrate. **Starter - one product** - 1 project, unlimited team members - Weekly market research and competitor intelligence - Experiment tracking and measurement - Strategy docs - MCP connectors (Claude, ChatGPT, Cursor, IDEs) - CLI for Codex, Claude Code, Aider, any shell - Slack guide - Support ingestion - Interview ingestion **Pro - up to five products** - Up to 5 projects, unlimited team members - Daily market research and competitor intelligence - Everything in Starter **Enterprise - unlimited** - Unlimited projects - SSO and SAML - Custom integrations - SLA guarantees - Dedicated support - Everything in Pro All plans include unlimited seats - you pay for what the system does, not who sees the results. Usage-based AI consumption with a real-time meter in your dashboard. Design partners get a discount on list price for the first three months. Talk to us via quack-stack.com/contact. ### What does the system do for you? - **Market research** - Discovery cycles that scan your market, find signals, and extract opportunities - **Competitor intelligence** - Track competitors and substitutes, spot gaps, and surface threats - **Customer voice** - Processing support conversations, meeting transcripts, and interviews into structured insights - **Strategy docs** - Generating and updating your ICP, positioning, messaging, and product vision - **Experiment design** - Hypotheses, kill criteria, interview guides, and landing pages for every bet you run - **Measurement and learning** - Track outcomes, record decisions, and build a growth log so every experiment makes the next one smarter ### FAQ **How does usage-based pricing work?** Every plan includes enough AI usage for its intended use. Your dashboard shows a real-time meter. If you reach your limit, the system pauses and lets you know. Top up or upgrade to keep things running. No surprise bills. **What counts as usage?** The AI work Quack Stack does for you: scanning markets, processing support conversations and interview transcripts, generating strategy docs, designing experiments, and conversations with your product guide. **Why unlimited seats?** The intelligence Quack Stack produces costs the same whether 3 people see it or 30. You pay for what the system does, not who sees the results. --- ## Integrations ### Where your team works - **Slack** - Product guide with morning briefs, proactive signals, and conversational access to all intelligence - **Claude, ChatGPT, Cursor, VS Code** - Full product context via Model Context Protocol (MCP). Ask about customer signals, opportunities, competitors, or experiments right in your editor or chat ### Customer voice - **Zendesk** - Support tickets flow in continuously, extracted and structured automatically - **Intercom** - Chat conversations become product signals with recurring themes and patterns - **HelpScout** - Mailbox conversations join the evidence base ### Team knowledge - **Notion** - Meeting notes, decision logs, and research docs feed into the shared picture - **Granola** - Meeting transcripts processed automatically with decisions, trade-offs, and reasoning captured - **GitHub** - PR merges trigger shipped feature tracking and automatic measurement --- ## Company Quack Stack is built by Patricia Klimek (CEO) and Steven Henty (CPTO). Patricia brings deep strategy and go-to-market expertise from BCG and private equity across the UK and DACH regions. Steven brings over a decade of product and engineering leadership with distributed teams globally. Based in Europe (Vienna and Spain). Currently in early access. The mascot is a rubber duck with glasses - playing on the "rubber ducking" concept from programming. It's deliberately warm and non-threatening. The product is a smart friend who did the research, not a consultant handing down judgments. --- ## Links - Website: https://quack-stack.com - Features: https://quack-stack.com/features - Pricing: https://quack-stack.com/pricing - Integrations: https://quack-stack.com/integrations - About: https://quack-stack.com/about - Contact: https://quack-stack.com/contact - Blog: https://quack-stack.com/blog - Book a demo: https://quack-stack.com/contact --- ## Blog Post Summaries ### Your team's AI stack is about to fragment, and you won't see it happen URL: https://quack-stack.com/blog/ai-stack-fragmentation Published: 2026-04-23 Author: Steven Henty AI-stack fragmentation is a new category of team misalignment: every person on a product team in 2026 has built their own AI setup - their own prompts, context, and agents - and each person defends their version because the AI walked them through the reasoning. The old fragmentation was "we use different tools"; the new one is "we use the same tools but each of us has earned a different conviction from them." Four shifts in the last 12-18 months made this newly urgent. Autonomous agents crossed from demo to production. MCP became a real standard for context flow. AI coding tools made strategic misalignment visibly expensive (shipping the wrong thing now takes two days, not six weeks). LLM economics (batch APIs, prompt caching, Haiku-tier models) made continuous team-wide synthesis affordable. The argued solution is a full product intelligence loop, machine-readable end to end: ingest signals, synthesise into evidence-linked artifacts (ICP, positioning, decisions), generate experiments with kill criteria as data, generate assets, wire measurement, track results, update the synthesis. Every stage reads from and writes to the same store, so agents can read the substrate rather than carry their own context. The key property: coherent-but-wrong beats incoherent-but-individually-correct. A shared synthesis, even a wrong one, is something a team can argue about. Twenty private syntheses that each seem right are incoherent in ways nobody can see. Notable framings used: individual AI is extraordinary; collective AI memory across a team is roughly nowhere. Agents need a shared brain, not individual MCP configs. Agents respect evidence more than authority. ### Why we built Quack Stack URL: https://quack-stack.com/blog/welcome Published: 2026-04-04 Author: Steven Henty Continuous product intelligence for teams that build. The post explains why the wall between "people who decide" and "people who build" has collapsed - engineers now talk to customers and make product calls; PMs write code; AI agents ship without context about customer pain or hypotheses. The old handoff is not slow, it is gone. Traditional product and engineering tools still assume the handoff exists. That leaves context fragmented across Slack, wikis, and tickets, going stale while work ships. Quack Stack is framed as the context layer in between: it listens to market, customers, and team reasoning, then makes that intelligence available in Slack, your IDE, the command line, and every AI agent via MCP. The analogy used: CI/CD for product intelligence. Before CI, engineers manually checked whether changes broke anything. After CI, the system handled it continuously. Quack Stack aims to do the same for product intelligence - continuous access to the shared picture of what to build and why. Four operating principles are stated: signals flow in from market research, customer support, competitor monitoring, and team notes. Intelligence compounds as the system synthesises signals into ranked opportunities with evidence. Context shows up where you work - IDE for engineers, Slack for PMs, MCP for agents. Results feed back in - shipped features are measured automatically and outcomes sharpen future priorities. Target audience: teams that have outgrown "everyone knows everything" but don't want a heavyweight process to replace it.