[{"data":1,"prerenderedAt":171},["ShallowReactive",2],{"blog-index:en":3},[4],{"id":5,"title":6,"authors":7,"body":14,"category":158,"date":159,"description":160,"extension":161,"locale":162,"meta":163,"navigation":164,"path":165,"published":164,"readingMinutes":166,"seo":167,"slug":168,"stem":169,"translationKey":168,"updated":159,"__hash__":170},"blog\u002Fblog\u002Fintroducing-signaldig\u002Fen.md","What SignalDig is building: evidence before growth decisions",[8],{"name":9,"to":10,"avatar":11},"SignalDig Team","https:\u002F\u002Fgithub.com\u002Fdaily-growth-signals",{"src":12,"alt":13},"\u002Fbrand\u002Fsignaldig-avatar.svg","SignalDig team avatar",{"type":15,"value":16,"toc":142},"minimark",[17,21,24,27,32,35,54,57,60,64,67,70,73,77,80,85,88,92,95,99,102,106,109,113,116,119,122,126,129,132,136,139],[18,19,20],"p",{},"Growth research often begins with a straightforward question: Is this keyword worth targeting? What changed in the market? Which user problem appears often enough to investigate?",[18,22,23],{},"The work that follows is rarely straightforward. Search demand lives in one tool, result-page observations in another, trends somewhere else, and user language across public conversations. Before anyone can make a decision, those observations need to be collected, compared, dated and interpreted.",[18,25,26],{},"SignalDig is being built to make that research step more coherent. It brings multiple dimensions of current market data into one request and returns growth signals that retain their evidence, context and limits.",[28,29,31],"h2",{"id":30},"the-problem-is-not-simply-access-to-more-data","The problem is not simply access to more data",[18,33,34],{},"More fields do not automatically create a better decision. A response can be technically complete while leaving the important questions unanswered:",[36,37,38,42,45,48,51],"ul",{},[39,40,41],"li",{},"Which market and language does the observation describe?",[39,43,44],{},"When was it observed, and how quickly could it become stale?",[39,46,47],{},"Are two fields measuring the same concept?",[39,49,50],{},"What evidence supports the direction, and what evidence weakens it?",[39,52,53],{},"What should be validated next in official analytics or business records?",[18,55,56],{},"When this context is missing, a person or AI agent has to reconstruct it. That increases research time and makes confident-sounding conclusions easier to produce than well-supported ones.",[18,58,59],{},"SignalDig treats context as part of the product, not as cleanup work left to the user.",[28,61,63],{"id":62},"what-we-mean-by-a-growth-signal","What we mean by a growth signal",[18,65,66],{},"A growth signal is a structured interpretation of one or more market observations. It is not a promise that an action will produce growth.",[18,68,69],{},"For example, an increase in search interest is an observation. It becomes more useful when it is connected to the relevant market, language, time window, result-page pattern and the words people use in public discussions. The resulting signal might suggest a content gap worth testing. It should also disclose conflicting evidence, uncertainty and the conditions that would make the idea no longer worth pursuing.",[18,71,72],{},"That distinction matters. External market data can reveal a direction. Only the product's own analytics, customer conversations and business records can validate the outcome.",[28,74,76],{"id":75},"how-signaldig-approaches-the-work","How SignalDig approaches the work",[18,78,79],{},"The current product direction follows a simple sequence.",[81,82,84],"h3",{"id":83},"_1-start-with-a-growth-question","1. Start with a growth question",[18,86,87],{},"The request defines the keyword, domain, market, language and data scope. A clear question gives every observation a job and prevents unrelated metrics from becoming decoration.",[81,89,91],{"id":90},"_2-compose-relevant-data-dimensions","2. Compose relevant data dimensions",[18,93,94],{},"SignalDig can combine search demand, result-page structure, trend context and public conversations around that question. The composition is expandable; a fixed list of upstream providers is not the product boundary.",[81,96,98],{"id":97},"_3-normalize-and-mark-context","3. Normalize and mark context",[18,100,101],{},"Different responses are translated into a stable contract. Market, language, observation time, freshness and field meaning remain attached so people and agents can interpret them consistently.",[81,103,105],{"id":104},"_4-return-evidence-with-the-direction","4. Return evidence with the direction",[18,107,108],{},"The output connects supporting evidence, counter-evidence, confidence, limitations and a practical next validation test. The intention is to make review possible, not to hide uncertainty behind a score.",[28,110,112],{"id":111},"built-for-people-and-ai-agents","Built for people and AI agents",[18,114,115],{},"SignalDig has two complementary layers.",[18,117,118],{},"Agent Skills describe repeatable research and decision workflows. They help an agent ask better questions, compare evidence and propose a validation step. SignalDig MCP provides the current market data capability those workflows can call.",[18,120,121],{},"Keeping these layers separate is deliberate. A workflow can remain open and inspectable, while access to live capabilities can be authenticated, governed and improved independently.",[28,123,125],{"id":124},"what-signaldig-does-not-claim","What SignalDig does not claim",[18,127,128],{},"SignalDig is not a replacement for first-party analytics, revenue records or direct customer research. It does not claim continuous monitoring of the entire web, guaranteed completeness, or guaranteed growth outcomes.",[18,130,131],{},"The product is currently in early development. Examples on the site are illustrative and do not represent production metrics or customer results. As the service evolves, we will keep documenting the evidence boundary as carefully as the features themselves.",[28,133,135],{"id":134},"the-principle-behind-the-product","The principle behind the product",[18,137,138],{},"Growth work involves uncertainty. The useful response is not to remove every caveat or add more dashboards. It is to make the evidence easier to inspect and the next test easier to define.",[18,140,141],{},"That is the direction SignalDig is pursuing: connect multidimensional market data, surface real growth signals, and keep every conclusion close to the evidence that supports it.",{"title":143,"searchDepth":144,"depth":144,"links":145},"",2,[146,147,148,155,156,157],{"id":30,"depth":144,"text":31},{"id":62,"depth":144,"text":63},{"id":75,"depth":144,"text":76,"children":149},[150,152,153,154],{"id":83,"depth":151,"text":84},3,{"id":90,"depth":151,"text":91},{"id":97,"depth":151,"text":98},{"id":104,"depth":151,"text":105},{"id":111,"depth":144,"text":112},{"id":124,"depth":144,"text":125},{"id":134,"depth":144,"text":135},"Product","2026-08-12","An introduction to SignalDig, the problem it addresses, how a growth signal is formed, and where the product deliberately stops.","md","en",{},true,"\u002Fblog\u002Fintroducing-signaldig\u002Fen",6,{"title":6,"description":160},"introducing-signaldig","blog\u002Fintroducing-signaldig\u002Fen","9S7HXWTZxb_7599Gn1Lrwq680QPyiLetKeUx94fdFV4",1786965745864]