Whisper Forge.

The problem

Inside a company, high-quality customer conversations are created and then forgotten — living only in one person's head. Each team keeps its own tools for interviews and transcripts, and pulling the relevant metadata for each (industry, tenure, meeting count) is tedious enough that it usually doesn't get done.

Every team ends up with its own set of customer truths that are hard to share, and information doesn't flow between the people doing product research and the people solving customer problems day to day. The result is a wealth of unstructured, siloed data that never turns into roadmap insight. When a new question arises, the answer may be in a past interview, but reviewing them is time-consuming and frustrating.

The solution

Whisperforge helps product teams collect customer conversations from across departments and turn them into actionable roadmap insight. A user drops in a transcript, identifies the customer, and adds any notes; the tool generates summaries and extracts key quotes, then lets anyone query the whole corpus by buyer segment or industry.

The query experience answers questions like "What questions do customers in their first month have most often?" across many transcripts at once. Every extracted quote is set off in quotation marks with a line number back to the source, so a human can quickly verify it before it appears in any executive or sales document. The goal is to break down siloed knowledge and let each team keep the interview tools it already prefers while sharing usable signal.

How it works

The user deposits a transcript plus a customer identifier and optional notes; metadata filters winnow the dataset to relevant records before the AI summarizes. Claude Sonnet 4.6 produced the best results on real data and is the intended production model; the Lovable prototype uses gemini-3-flash-preview as the option fully configurable through Lovable, with data stubbed and anonymized to protect private information.

The prompt casts the AI as a product manager reviewing transcripts for pain points, praise, insights, and quotes, with strict instructions to capture quotes exactly (allowing ellipses) and cite line numbers. It is scoped to draw only from the transcripts it is given — which the builder found handled most edge cases, from out-of-domain questions to duplicated transcripts. An OpenAI eval run scored 78% on key quotes (mostly failing on timestamps versus line numbers) and 89% on summaries; adding "each bullet point should have a separate idea" fixed muddled summaries.

Who it's for

Whisperforge 1.0 is for internal users at an enterprise software marketplace for cybersecurity and AI software — product, sales, customer success, onboarding, and support teams who conduct or rely on customer interviews. Product owns the tool and acts as the human in the loop.

The company is a two-sided marketplace whose paying customers are software vendors and whose harder-to-win side is anonymous enterprise buyers (CISOs, CTOs, directors). If the internal version proves valuable, a variation may be offered to vendor customers to help them analyze their data and improve product-market fit. The internal product won't generate revenue but increases efficiency.

Why it matters

The cybersecurity and AI software markets are projected to grow at 20% over the next three years, and vendors on the marketplace increasingly want data and insights around their buyer meetings. Success for the internal product is defined by adoption: every department depositing at least one transcript a week, weekly queries from stakeholders, and a companywide weekly report.

Data handling is treated carefully — all LLM calls to the database are treated as untrusted input and permissioned to what each user may already see, with enterprise contracts forbidding training on the data and SOC 2 compliance in progress. By turning forgotten interviews into a shared, queryable corpus, Whisperforge makes customer signal a company asset rather than a personal one.

At a glance

Project

Whisper Forge

Built by

Elaine Lindelef

One-liner

Whisper Forge helps product teams collect customer conversations from across departments and turn them into actionable roadmap insight.