Your firm's knowledge, structured and queryable — on hardware you own.
Toryx turns your documents and data into a private, traversable knowledge graph — Palantir-grade context without forward-deployed engineers or seven-figure contracts. Evaluated through a 12-week, fixed-scope pilot.
Every regulated firm has the same locked asset: decades of documents, filings, memos, models, and records that no one can query. The firms that solved this paid Palantir millions and hosted an army of forward-deployed engineers. Everyone else got chatbots that guess.
Point STRATUM at your corpus. It extracts entities and relationships automatically — subject, predicate, object — and builds a traversable knowledge graph as it embeds. Evaluate the extracted relationships on your own corpus. Processing time depends on document size, format, and the selected hardware.
- 01Ingest — Your documents, your systems, your sensors. Nothing leaves your perimeter.
- 02Structure — STRATUM builds the ontology automatically. Patent pending (Serial 64/010,149).
- 03Retrieve — Multi-hop questions across everything you know, with sources.
- 04Answer — A local model, on your hardware, at zero marginal cost.
1. Credit memo CM-2023-114: flagged Meridian Logistics at 3.4× leverage against a 3.5× covenant ceiling (analyst note, Mar 2023).
2. Master agreement §7.2: collateral step-up triggers at 3.5× — additional posting within 10 business days of breach notice.
3. Position ledger: exposure still held as of last close — memo → agreement → ledger resolved in one query.
"Your alpha never touches an API."
Research memory, deal history, and proprietary data — structured into a private graph your analysts and agents can query. Zero cloud exposure, complete model-weight sovereignty.
Toryx for Finance →"Air-gapped is not a constraint. It's the requirement."
Multimodal retrieval across documents and sensor evidence — thermal, acoustic, depth — in classified and disconnected environments. SAM.gov registered, active CAGE code, competing in U.S. Army xTech.
Toryx for Defense →"Every answer auditable. Every source on-prem."
Underwriting guidelines, claims history, and catastrophe imagery in one queryable layer — built for model-risk governance and tight SLAs.
Toryx for Insurance →Most vendors claim accuracy. We publish benchmarks, confidence intervals, and the harness that produced them.
Recall@5 on MuSiQue multi-hop retrieval (N=1,000, nested CV, 95% CI [81.1, 84.3]) — research pipeline, non-commercial embedding backbone.
Less energy per answer than frontier cloud LLMs, at ~90% of their accuracy, on a single box. (~630× on a dedicated H200.)
F1 added by KMS grounding to our own LLM in an 8-model study — the largest boost of any LLM tested. The grounding layer, not the model, sets the outcome.
The cloud pattern bills you twice, forever. A production terabyte of managed vector storage — replication, read/write units, backup, compliance — is one meter, running at institutional query volume. Answering questions through a frontier API is a second meter, and it grows with every query. The Toryx appliance replaces both: your corpus on hardware you buy once, queries at zero marginal cost. TurboQuant compression is what makes it fit — a full institutional knowledge base, quantized and rescored at full precision, on hardware that sits on one desk.
A production terabyte of managed vector storage, all-in. Rent, forever.
Frontier LLM query fees, compounding with every question you ask.
Apple hardware and NVIDIA DGX Spark systems, configured around the workload.
Per-query cost on owned hardware. The meter never runs.
Managed hosting buys you HA and backups. Our answer: stack a second box. You're still ahead in year one, and permanently ahead after. Your recurring cost becomes the platform license — a number you know, not a meter that runs.
Office appliance
Apple + NVIDIA DGX Spark · mobile options
Scope the corpus and workload first, then agree the compute, indexing, storage, and MacBook Pro options for mobile users.
Air-gapped site
zero network · classified-ready
The same stack, physically disconnected. Designed for SCIFs, deployed sites, and data rooms where no packet leaves.
Customer-controlled VPC
your cloud account · your keys
By contract, for firms whose perimeter is a private cloud. Your boundary, your audit trail — never our infrastructure.
The Toryx demo environment runs on public data only. Client corpora are ingested, embedded, stored, and queried inside the client's perimeter — never on Toryx-operated infrastructure. Access boundaries and audit requirements are defined for each integration and tested during the pilot; authorization metadata and an LLM refusal are not substitutes for verified access controls.
OpenRatings — credit-style ratings (AAA to D) for AI agents and software repositories, anchored immutably. Because firms that deploy agents will need to know which ones to trust. openratings.ai →
Luis M. Sánchez
FounderFormer Lehman Brothers; quant and structured-finance background. Built the entire Toryx stack with a governed fleet of coding agents — the company runs on the discipline it sells.
Michael Simoff
Co-founderFormer portfolio manager, Elliott Management; led a $400M SPAC.
Kosrow Dehnad
AdvisorProfessor, Columbia University; derivatives and applied mathematics.
Our senior team has advised on structured finance, insurance risk, and AI infrastructure at SGX Analytics.
The pilot is the proof.
Twelve weeks, fixed scope, your corpus, agreed hardware, your evaluation questions. If we don't beat your current retrieval, you'll know exactly by how much — we'll show you the eval.
Start the conversationinfo@toryx.ai