Resonant Analytics builds the pipelines that gather, process, and structure text data so it's ready to query, build on, ship, or sell.
Compliance documents, regulatory filings, earnings transcripts, market data feeds, vendor assessments, customer communications - the most signal-rich information in any operation is unstructured text, whether it lives inside your organization or outside it. The problem isn't access. It's that there's no infrastructure to make it legible to the people who need to act on it.
We design and build that infrastructure. Clean ingestion, careful transformation, structured output - engineered to preserve the meaning in the language, not just the words on the page. The goal isn't to automate decisions. It's to make sure the people making them have the full picture.
End-to-end pipelines from raw text to structured, queryable output - built around your data sources, your schema, and what you need the output to do. Whether the text lives in your systems or needs to be gathered, the pipeline starts there.
Regulatory filings, AML/KYC documentation, vendor risk assessments, audit trails - structured and analyzed so the people responsible for decisions have what they need to make them. Less time in documents, more time on judgment.
Large-scale datasets built for serious analysis - earnings transcripts, market data, industry documents, or any text corpus worth studying at scale. Speaker-separated, section-isolated, multi-format, documented. Built to be queried, licensed, or published.
Language delta analysis revealing which import agency portfolios carried the most distinctly negative reception - before sales data reflected the problem.
A research-grade corpus built from raw JSON - Q&A sections extracted, executive voices separated, delivered in multiple formats for layered institutional analysis.
Resonant Analytics was founded in Montreal by Jack Geddes, a practitioner with deep experience in behavioral language analytics - building data pipelines, leading technical client engagements, and working at the intersection of language, data, and commercial outcomes.
That background shapes everything about how we work. We don't just move data from one format to another - we think carefully about what structure will make the output most useful downstream, and what the output needs to enable for the people who will act on it.
We work with a small number of clients at a time, deliberately. The work requires focus and precision, and we'd rather do fewer engagements exceptionally well than scale at the expense of quality.
We work with a small number of clients at a time. If you're dealing with unstructured text at scale and not getting the intelligence you need from it, we'd like to hear about it.