Decipher TextMessage VS TextTimeline

TextTimeline's answer:

Most tools in this category are printers - they get messages onto paper. TextTimeline is an evidence tool: it parses the actual message export file (not screenshots), preserves every timestamp, sender, and source reference, makes years of messages searchable in plain language, and produces court-formatted deliverables where every finding cites the exact source messages. It is also the only tool in the space built for both iPhone and Android exports, entirely in the browser with nothing to install.

TextTimeline's answer:

Three reasons. First, integrity of the evidence: TextTimeline works from the real export data, so the output carries original timestamps and senders - screenshot-OCR apps produce a transcription with no underlying data to authenticate. Second, scale: when a case involves thousands of messages across years, printing isn't the problem - finding is, and TextTimeline is the only option with real search. Third, pricing: $19 or $99 one-time with a free no-signup demo, while alternatives charge weekly or monthly subscriptions or require a desktop app purchase.

TextTimeline's answer:

People in the middle of a custody, divorce, or harassment matter who need their text messages in court-ready form - often self-represented, often on a phone, often facing a hearing date. The secondary audience is the professionals who serve them: family-law attorneys and paralegals who need a client's message history organized, searchable, and cited without hours of manual work.

TextTimeline's answer:

TextTimeline was built by a solo founder who went through his own family-law case and hit the problem directly: years of text messages that mattered, and no reasonable way to get them into a form a court could use - screenshots get challenged, printing everything is absurd, and forensic services cost $500-$2,000. He built the tool he needed: upload the export, search for what matters, download something you can hand to a judge.

TextTimeline's answer:

Python (FastAPI) backend and a React/TypeScript frontend, running on Google Cloud. Search is retrieval-first: semantic embeddings plus keyword search (FAISS + BM25) fused together, so plain-language queries find messages that don't share exact words. Language models are deliberately narrow in scope - used only for query expansion and for summaries grounded in already-selected messages, never to read or classify the full corpus, and never trained on user data.

TextTimeline's answer:

Individuals in family-law matters - our users are in confidential legal proceedings, so we do not disclose or name customers.