AI-ready data operating layer
Put hard-to-use data to work in your AI task. Compare before and after to see what improved and what still needs work.
AI EXECUTION
Your models, agents and analytics
CUBIG
Prepared for the task, checked on results, kept on record
ENTERPRISE DATA
The systems you already use
Prepared for the task, checked on results, kept on record
Gartner®Named in Gartner® research
What we hear most
Business team
We got 78% accuracy. Is that good enough to actually use?
Without a baseline under the same conditions, there is no way to show it got better.
Model evaluation
Current78%
Baseline?
Eval data ?Eval conditions ?
Data engineer
Everyone cleans our files a different way. Where do we even start?
Diagnose the data against the same criteria, and keep how it was handled as a version.
How each file was cleaned3 different rules
orders_2026_07.csvDropped blanks
orders_2026_08.csvFilled with mean
orders_2026_09.csvLeft as is
orders_2026_10.csvDropped blanks
ML engineer
We barely have any of the fraud cases we need to catch. Can a model learn from that?
When rare cases are scarce or skewed, the model barely learns them.
Training data mix
Normal98%
Fraud2%
The AI barely saw what it must catch.
ML engineer
Results shifted last week. Was it the data or the setup? I can't tell.
Without a record of what was used, you can't narrow the search.
Weekly prediction accuracy
AI developer
I gave the AI a doc with tables and it quoted the wrong number. Why?
Merged columns and orphaned numbers become the AI's evidence.
Quarterly revenue table
QuarterRevenue
Q11,200
Q284.50
Q3310.00
What was Q2 revenue?
Q2 revenue was 1,200. ✗ (actual: 84.50)
AI developer
We built an internal agent, but we can't tell which situations it gets wrong.
Build test cases from your work documents and check real runs against the same criteria to find the missing situations.
Agent test casesCases to fill in: 2
Return requestPass
Change delivery addressPass
Partial refund with couponFail
Overseas shipping exceptionNot run
AI operations
Our data has customer details, so we can't send it to an outside LLM as is. How do we use it?
Swap sensitive values for stand-ins that keep the context, send that, then restore the original values in the response.
What gets sent to AI
OriginalSent to AI
Dana Lee→Customer_A
110-234-567890→Account_A
3 late payments→3 late payments
AI response Customer_A restored to Dana Lee
Questions teams ask us most
Building solo or with a small team? You can start with the data you already have.
Explore SyntitanWhat CUBIG does
We check whether your data fits the task, fill what is missing, and keep the evidence after it is in use. Each product can also be adopted on its own.
ENTERPRISE DATAThe systems you already use
CUBIGPrepared for the task, checked on results, kept on record
AI-ready data platform
Prepares and verifies enterprise data for an AI task, then keeps track of changes and results after it's in use.
Raw Data
Model
Agent
Context-Preserving Data Layer for AI
Replaces controlled values with context-preserving stand-ins before they reach AI, then restores the original business values in the response.
Data Enablement for AI
Fills gaps in rare-case coverage, corrects skewed distributions and prepares hard-to-use data for a defined AI task.
Prepare·Adapt·Prove / Recover·Operate
AI EXECUTIONYour models, agents and analytics
Beyond the first prep
Prepare for the task, prove it under the same conditions, and keep the evidence while it runs.When data or conditions change, only the changed part gets rechecked.
Find what keeps the data from AI work, such as missing cases, mismatched values or values that can't go out as is, and fix it. The original stays untouched.
Decide which model, agent or analysis will use the data and how results will be judged. The same data needs different prep for different work.
Compare before and after under the same conditions to see if it meets the criteria you set. If it falls short, fill what is missing and check again.
While it is in use, keep a record of which data and conditions each run used. When data changes, recheck only the changed part instead of starting over.
How to start
Either way, you start with one task. Try it today with the data you have, or begin with a Trial that settles one decision.
Solo builders · small teams · in-house practitioners
Sign up, upload a file and pick what to check first: the data itself, a model result or an agent run. The original stays as is.
Multiple teams · fixed deployment environments · formal review
Pick one task and one decision for a Trial and check the result under the same conditions. Then expand to other teams and tasks.
Or start with just one product
Each one can be adopted on its own.
Proof
One customer record and three public-data experiments, plus our customers, certifications and patents.
Actual record · Insurance
58.55%→88.55%
Weighted F1 · same model · same evaluation criteria · January 2025, self-measured
Open experiments · examples
0.11→0.77
LR · F1 · Public datasets, single split. Not customer results.
Open experiments · examples
0.91→0.96
LR · F1 · Public datasets, single split. Not customer results.
Open experiments · examples
0.41→0.61
RF · F1 · Public datasets, single split. Not customer results.
Procurement
Proof
Use case examples
Pick your industry, and see where the data gets stuck and how it gets resolved.
Finance
| Tx ID | Amount | Currency | Label |
|---|---|---|---|
| T-2041 | Normal | ||
| T-2042 | USD | Normal | |
| T-2043 | 2,300.00 | Fraud | |
| + C-01 | 1,980.00 | USD | Rare AML pattern candidate |
Rare fraud patterns show up only a few times in training data, so the model often misses them. DTS fills in rare cases, and Syntitan compares before and after.
Example screen · your tools may differ
Insurance
| Claim | Type | Received | Status |
|---|---|---|---|
| CL-3307 | In review | ||
| CL-3308 | 2026-10-02 | Received | |
| CL-3309 | Fire | In review | |
| + C-01 | Rare rider | 2026-10-03 | candidate |
There are many claim types, and some have too little training data. DTS fills claim types with few examples, and Syntitan compares before and after.
Example screen · your tools may differ
Healthcare
| Patient | Birth date | Phone | Diagnosis |
|---|---|---|---|
| Rare disease X | |||
| Type 2 diabetes | |||
| Hypertension |
The response comes back with the original values restored
Patient data can't go into an LLM as is, and rare-disease data is too small. LLM Capsule swaps patient details, and DTS adds rare-disease data.
Example screen · your tools may differ
Retail
| Customer ID | Name | Channel | Last order |
|---|---|---|---|
| Kim Minji | App | 2026-10-01 | |
| Web | 2026-09-27 | ||
| Store | 2026-09-15 | ||
| C-1101 | Lee Seojun | Web | 2026-10-02 |
Each channel labels customers differently, so one person shows up as several. Syntitan aligns the labels and links the records for the recommendation model.
Example screen · your tools may differ
Manufacturing
| Line | Temp | Interval | Result |
|---|---|---|---|
| Line A | Normal | ||
| Line B | 25.4°C | 1m | Normal |
| Line C | Scratch | ||
| + C-01 | 25.2°C | 1m | Micro crack candidate |
Critical defects are rare, and every line records data in a different format. Syntitan aligns the formats, and DTS fills in the scarce defect cases.
Example screen · your tools may differ
FAQ
CUBIG builds the AI-ready data operating layer for enterprise AI execution. It has three products: Syntitan, LLM Capsule and DTS. Each can be adopted on its own.
No. Your warehouse, lakehouse and databases stay as they are. CUBIG checks whether that data fits a specific AI task and prepares what's missing.
Syntitan is an AI-ready data platform that prepares and validates data for an AI task and keeps the evidence after it is used. DTS fills missing cases and adjusts skewed distributions for a defined task. LLM Capsule replaces controlled values with context-preserving stand-ins before they reach AI, then restores the original values in the response. Each can be adopted on its own.
Start from your problem. To check and prove data for a model or agent, start with Syntitan. If sensitive data can't go to an outside AI as is, start with LLM Capsule. If the cases that matter are scarce or skewed, start with DTS. Each works on its own.
Syntitan proposes fixes first. You make only the decisions that need a person. Results are checked as a before-and-after under the same conditions.
AI-ready data is data prepared for a specific AI task, with the result checked before and after under the same conditions, and ready to recheck when the data or conditions change. It is not a one-time certification or a score.
If source data can't go to AI as is, LLM Capsule replaces controlled values with context-preserving stand-ins and restores the original values in the response. Deployment and scope are set with you for your environment.
No. Synthetic data is one of the methods DTS can use to fill missing cases, but the goal is data that fits a specific AI task. The effect is checked on the same task, model and evaluation conditions.
The original stays as is and work happens on a copy. Each successful processing run leaves a new version and its evidence, so you can see what changed and why.
Sign up and start with your own data or a sample. For organizations, we start with a Trial on one use case and one decision.
Download the confirmed version as a file or connect it to where it's used via MCP. Where processing runs and which environments are supported is confirmed with you for your setup.
On-premises and cloud options are available, and supported environments are confirmed per product for your setup. You can also find DTS and LLM Capsule on AWS Marketplace, and LLM Capsule on Microsoft Marketplace.
Worth reading before you decide
Read the definition, the terms and the experiment records first.When you are ready, check it on your own data.
