Why Octosight
We’ve Lived These Problems
Our team has delivered high-stakes, cannot-fail projects alongside law firms, consulting teams, and government agencies, managing hundreds of millions of files across systems, jurisdictions, and fixed deadlines.
That experience became the foundation for forming Octosight and building Thermal.
We started by building the regulatory-grade data engine we always needed: a cloud-native system that connects directly to sensitive enterprise data, prepares structured, review-ready content at scale, and enables in-place actions to be taken from the same platform used for discovery and review.
Thermal isn’t an LLM wrapper. Our platform begins at the data layer — extracting, structuring, and indexing unstructured content — and prepares that data for domain-specific models fine-tuned for breach response, closing deals, regulatory compliance, and legal matters. Each model is being developed to keep humans in control and deliver outcomes that are repeatable and defensible.
We’re rebuilding what we’ve delivered for years, integrating AI where it reinforces the workflows teams already trust to perform under pressure.
Where Today’s AI Tools Fall Short
Today’s AI tools aren’t built for high-stakes work. They struggle with context, skip human oversight, and deliver outputs that can’t be explained or defended. In regulated markets, that’s not just a limitation — it’s a blocker.
Assessments Break Down
Teams lack the ability to find, review, and curate what gets sent to AI models. Longer assessments break context limits, and out-of-the-box models aren’t built to prioritize the data that matters.
Sensitive Data Falls Through the Cracks
Regex-based pattern matching generates too much noise. Real PII, PHI, and PCI gets missed or buried, and what’s found often can’t be linked back to the impacted customer, patient, or employee.
Discovery Without Action
Teams can’t start from one sensitive file and find similar content across the enterprise. So they miss what matters, over-delete, and can’t take remediation actions in the same system that found the data.
Octosight Delivers the Last Mile
We’re integrating an agent into our actionable discovery engine to orchestrate domain-specific fine-tuned models — built for regulated environments where humans remain in the loop and every output must stand up to scrutiny.
Before invoking the model, teams can search across massive volumes of unstructured data, preview why each file matched, and tag only the most relevant content for inclusion — packaged in a format optimized for language models.
By curating precise inputs, Thermal reduces token consumption and preserves context. This enables deeper analysis and richer outputs, from completing risk assessments and identifying sensitive personal information to discovering related data across the enterprise from a small sample of relevant files.
That’s made possible by three fine-tuned models, each tailored to a core enterprise use case.
Risk Assessment Model
Built for assessments like cybersecurity risk and deal due diligence, this model answers detailed questionnaires using curated source files. It breaks long-form inputs into structured categories for efficient token use, cites the underlying files, and flags unanswered sections for human review to support traceability.
Sensitive Personal Data Model
Designed for breach notifications and pre-deal risk checks, this model goes beyond pattern matching to classify PII, PHI, and PCI in context. It links attributes to real customers, patients, or employees for accurate, auditable reporting — fast and at scale.
Targeted Data Similarity Model
Trained on a handful of critical files, this model finds similar content across the enterprise to support CFIUS mitigations, ITAR controls, antitrust separations, IP protection, legal discovery, and everyday tasks like locating contracts and licensing agreements. Once surfaced, files can be remediated in place — securely preserved, permanently deleted, or curated for downstream AI enrichment — all without copying sensitive data into another tool.
We’ve spent a decade preparing for this moment. Now we’re scaling what we’ve already delivered — with AI.
Why Teams Trust Us
We stay close to the people doing the work. Every week, we speak with our consulting and legal design partners, past clients, former colleagues, and future Service Partners to understand where existing tools fall short and where pressure is building. That real-world insight shapes every feature we build and guides our product roadmap.