If you came to CUBIG for LLM Capsule, you need DTS only when your data itself cannot teach or answer the model, not when your worry is sending confidential data to an LLM; LLM Capsule solves the second problem, DTS solves the first.
Most teams arrive with a concrete blocker: legal will not let a model see raw customer records, so an AI project that looked ready in a demo stalls the moment it touches production data. That is the LLM data privacy question, and it is what LLM Capsule answers. The DTS question is quieter and shows up later, when the data you are allowed to use turns out to be too thin, too skewed, or too locked-down to train or evaluate anything reliable. The first blocker is well grounded: Rocher et al. estimated that 99.98% of Americans would be correctly re-identified in any dataset using 15 demographic attributes, and concluded that even heavily sampled anonymized datasets are unlikely to satisfy GDPR anonymization standards. In practice those two failure modes, no access and no usable data, account for most stalled AI projects. Knowing which one you actually have keeps you from buying the wrong tool.
LLM data privacy: what LLM Capsule does, and where it stops
LLM Capsule is a Context-Preserving Data Layer for AI, running on the CUBIG Syntitan platform. It sits between your workflow and the model so the model receives the structure of the work, not the raw sensitive values. The pattern is Substitute, Execute, Reconstruct: substitute confidential fields with placeholders before anything leaves your boundary, execute the model on that safe surface, then reconstruct the real answer locally from a protected mapping layer that never left your side.
That closes a specific LLM data privacy gap. Your analysts can run a frontier model over contracts, tickets, or patient notes without the underlying values crossing the LLM data egress line. The work completes in place, and you keep an operational artifact you can hand to an auditor. The gap itself is not small: Cyberhaven’s analysis of ChatGPT use across 1.6 million workers found that 11% of what employees paste into ChatGPT is confidential data, from source code to client records.
Here is the boundary of what it does. LLM Capsule assumes the data you hold is already good enough for the model to do the job once it can see the structure. It does not improve the data. If your records are too sparse to represent the case you care about, or a whole category of examples is legally off-limits, protecting them on the way to the model changes nothing, because the model still has too little to work with.
What DTS does that a data boundary cannot
DTS is CUBIG’s AI-ready data transformation engine. Where LLM Capsule governs how data crosses a boundary, DTS rebuilds the data itself into something a model can learn from. Its loop is Diagnose, Transform, Rebuild: it diagnoses what makes a dataset unusable, transforms it through structure-preserving synthesis so the statistical shape survives, and rebuilds the parts that were missing, including rare-pattern augmentation for the cases that barely appear in your real records. It uses a non-access architecture, and differential privacy is available as a technique when the source is restricted. That technique deserves care in its own right: NIST SP 800-226 provides guidelines for evaluating differential privacy guarantees and documents the common implementation pitfalls it calls privacy hazards.
The tell is simple. If your problem is that the data exists and is fine but must stay confidential, DTS is not your answer. If your problem is that the data you are allowed to touch cannot support training or evaluation, no boundary product will fix that, and DTS is exactly the tool for it. DTS also runs on the Syntitan platform, so adopting one does not strand you from the other.
LLM Capsule vs DTS: which blocker are you solving?
The two products answer different questions. One is about access, the other about substance. This table lays them side by side so you can place your own situation.
| LLM Capsule | DTS | |
|---|---|---|
| The blocker it solves | Access — you cannot send the data to the model. | Substance — the data itself cannot teach or answer the model. |
| Your situation | The data already exists and is accurate, but must stay confidential. | The data you are allowed to use is too thin, too skewed, or too locked-down. |
| What it does to the data | Governs how data crosses your boundary; it does not improve the data. | Rebuilds the data itself into something a model can learn from. |
| How it works | Substitute, Execute, Reconstruct. | Diagnose, Transform, Rebuild. |
| Where the data sits | Raw values never cross the egress line; the protected mapping layer stays on your side. | Non-access architecture, with differential privacy available when the source is restricted. |
| Platform | Runs on the Syntitan platform. | Runs on the Syntitan platform. |
When you came for LLM Capsule but actually need both
Plenty of teams need both, and the order matters. A bank that wants a model to read confidential deal memos starts with LLM Capsule, because the immediate blocker is egress. Months later the same bank tries to train a fraud model and finds it has only a few dozen real fraud cases, far too few to learn a reliable pattern. That is a DTS problem, and no amount of boundary protection creates the missing examples.
The reverse also happens. A team adopts DTS to augment a rare clinical cohort, gets a workable dataset, then wants to run a frontier LLM over the real patient notes for a separate task. Now the access question returns, and LLM Capsule handles it. Treat the two as answers to two independent questions rather than as competitors, and the sequencing takes care of itself.
There is a third thread worth naming, because production teams hit it fast. Once a model runs on rebuilt or boundary-protected data, the question is not only which model ran but which data state and execution conditions produced the result. Syntitan, CUBIG’s AI-Ready Data Platform, scores enterprise data on six axes, rebuilds what blocks execution, and binds every AI or agent run to a data state you can diff and reproduce, so an answer you got last quarter is still explainable this quarter.
A quick self-test before you choose
Run through these. If most of your yes answers land in the first cluster, you came to the right place for LLM Capsule. If they land in the second, you are looking at a DTS problem instead.
- The data I need already exists and is accurate, but compliance will not let it reach the model. (LLM Capsule)
- My analysts want to use a frontier LLM on confidential records without those records leaving our boundary. (LLM Capsule)
- The case I most need to detect barely appears in my real data. (DTS)
- A whole category of records is legally off-limits for training, so my model never sees it. (DTS)
- My evaluation set is too small or too skewed to trust the numbers it produces. (DTS)
- I need both: run a model on confidential data now, and rebuild a scarce dataset for a separate task later. (Both, sequenced)
Where it fits in CUBIG’s operating layer
LLM Capsule and DTS are two entry points into the same operating layer for AI-ready data, and they meet on the Syntitan platform. LLM Capsule keeps confidential work inside your boundary; DTS rebuilds data that cannot yet teach a model; Syntitan holds the reproducible AI-ready state underneath both, so the run you approved is the run you can reproduce. You do not have to decide the whole architecture on day one. You do have to name the blocker in front of you correctly, because the right first product follows directly from that.
If you are still unsure which side of the line you are on, the fastest way to find out is to look at the data you would hand the model tomorrow and ask whether the problem is that you cannot send it or that it cannot answer. For the deeper split between rebuilding data and protecting it, our guide on whether synthetic data buyers actually need LLM Capsule takes the same decision from the other direction, and what DTS is covers the rebuild engine in full.
Try it on your data for free. Run a sample proof and see it on your own workflow.
