We can't tell if our data is AI-ready.
Upload a dataset and see the readiness gaps before your team spends weeks cleaning it.
AI-Ready Enhancement
Syntitan · AI-Ready Data Platform
Make enterprise data AI‑ready. And keep every run traceable.
Syntitan diagnoses whether your data is AI-ready, prepares it for use, and keeps every AI or agent run traceable to the data state behind it.
Where you're blocked
Four problems. One workflow.
Most teams enter Syntitan from one of four problems. The workflow afterwards is the same.
Our best data is locked behind restrictions.
Prepare sensitive or regulated data through a restricted-data path before it enters AI workflows.
Restricted data preparationOur PoC worked, but production didn't.
PoC data is fixed, production data changes. Syntitan preserves the data state behind every AI run.
Release & Run BindingOur agents answer from guesses, not data.
Run agents only after the dataset is qualified and prepared for AI use.
Agents on prepared dataAlso possible with Syntitan
Evaluating a model API?
Preview whether your internal data has the signal and context required for a target model.
Is production data drifting?
Compare live data against an AI-ready baseline to see what moved.
Do teams disagree on which data version is correct?
Anchor reports, agent outputs, and decisions to the same Release State so debates move from "whose file" to "what changed".
The first value is seeing the problem.
Syntitan starts with your dataset, not a pipeline. The first screen already shows what blocks AI use and what to fix.
How it works
Syntitan closes two data gaps.
Your AI is only as ready as the data state behind it.
Make enterprise data AI-ready
Enterprise data is often scattered, restricted, and missing the context AI needs. Syntitan connects to internal data sources, understands structure and semantics, detects sensitive elements, and prepares data into governed AI-ready states.
So teams can move from fragmented enterprise data to trusted AI inputs that models, agents, and applications can actually use.
Syntitan prepares
AI readinessContext enrichmentGoverned accessRestricted data preparation
Make AI execution reproducible
AI results change when the data state changes. Syntitan releases approved AI-ready data states as controlled versions and binds every AI or agent run to the exact data state used.
So when results move, teams can trace what changed, compare versions, and reproduce prior outcomes with confidence.
Syntitan controls
AI-ready state releaseRun bindingData diffReproducibility
Solving only the first gap makes Syntitan look like preprocessing. Solving only the second makes it look like storage. Syntitan connects both: readiness on the way in, provenance on the way out.
The Syntitan workflow
One flow from raw data to traceable AI execution.
1
Diagnose
See readiness gaps.
2
Enhance
Improve data and context.
3
Prepare
Route restricted data through a restricted-data path.
4
Release
Freeze the AI-ready state.
5
Bind
Connect every run to that state.
6
Trace
Compare, reproduce, review.
7
Activate
Run agents on the prepared state.
Inside the workflow
How Syntitan makes data AI-ready
Four stages that turn enterprise data into AI-ready operating states.
Release & Run Binding
AI-ready is not enough.
AI-ready is not enough.
The state has to be fixed.
A Release State is a fixed AI-ready data state. It's the reference point analysis, agent runs, and review point back to.
New Release
This will be published as v4.
Version History
Jun 9, 7:23 PM
Sensitive fields handled. Column meaning added for product_category. Distribution stable vs v2.
Jun 6, 11:18 AM
Class balancing applied · low-signal columns removed
Jun 6, 10:16 AM
Initial upload
v3 released successfully.
A cleaned dataset can still drift. A transformed file can still be reused incorrectly.
A Release State isn't a snapshot, it's an operational reference point every AI run points back to.
When AI results change,
start with the data state.
Every AI or agent run binds to a Release State. When results shift, investigation starts from the data state, not from guesswork.
Diff · top 3 changes across versions
- 1Class distribution changed in high-value customersImpact: High · Compare segment distribution
- 2Product category context missingImpact: Medium · Add column meaning
- 3New null pattern in account_ageImpact: Medium · Preserve missingness or impute
Run Binding
Which data state produced this run?
Every AI or agent execution is connected to the exact Release State used.
Diff
What changed between two states?
Compare two Release States and surface the top likely causes of result change.
Reproduce
Can we inspect the previous state again?
Restore the data state used by a previous run so teams can investigate from evidence.
Agents that run on ready data, with more on the way.
Generic agents guess from assumptions. Syntitan agents run on qualified data states, grounded with semantic context attached.
100K synthetic personas that reproduce real response distributions
Generate survey responses from synthetic personas and analyze behavior patterns.
Result report
Which customers to focus on, simulated down to ROI
Segment customers by behavior and demographics, and simulate ROI-based strategy.
Result report
Catch churn signals before customers leave
Identify at-risk customers from behavioral signals, predict churn, and suggest retention strategy.
Result report
Launch-price simulation that pre-validates revenue and churn impact
Analyze price sensitivity and recommend a launch price that accounts for revenue and churn impact.
Result report
Already have a model or agent?
Through API connection, we compare performance before and after AI-Ready.
Talk to ArchitectCan't find the agent you need?
Tell us your analysis scenario and we'll design a custom agent.
Talk to Architect
Proof & fit
How Syntitan proves value in 4 weeks
Four weeks, four releases. A working data state you can run AI on every Friday, not a 6-month roadmap.
See the problem
Dataset upload → readiness qualification
Improve the dataset
AI-ready enhancement → before/after comparison
Fix the state
Release A/B → bound runs
Trace what changed
Diff Top 3 → reproduce data state
Where your team starts
Wherever your team begins, the path lands on the same Release State.
When AI breaks, how do you know if the cause is data or execution?
Run Binding, Release State, and Diff narrow the cause from evidence, not memory.
Can you prove which data state produced this result?
Every risk analysis is bound to a Release State with version history that internal review can inspect.
Can you recreate the exact segment used in the last campaign?
Release campaign data states and compare before-and-after changes across versions.
How long does it take to reproduce last month's analysis?
Recurring analysis stays attached to the same Release State, so reproduction is a click, not a rebuild.
Do prediction results change every quarter without a clear reason?
Prepare sensitive workforce data through a restricted-data path, release the analysis state, then compare quarter to quarter.
Where Syntitan fits in your AI data stack
Syntitan does not replace the tools your team already runs. It fills the missing step between enterprise data and AI execution.
Syntitan in one line
The path from raw enterprise data to a fixed, traceable AI-ready data state for production AI and agentic workflows, with data provenance attached.
-
Data platforms
Store enterprise data: warehouses, lakehouses, pipelines.
Syntitan addsThe AI-ready data state on top: qualification, enhancement, release.
-
Data quality tools
Detect issues: null rates, type errors, schema drift.
Syntitan addsAI readiness qualification, semantic enhancement, and a fixed Release State.
-
Observability tools
Detect that something changed in pipelines, models, or systems.
Syntitan addsWhich data state changed: Diff between Release States, reproduce the prior one.
-
Agent tools
Run agents and agentic workflows on top of data.
Syntitan addsA prepared data state for agents to run on, with outputs grounded, not guessed.
-
Sensitive-data transformation tools
Synthetic transformation and sensitive-data preparation.
Syntitan addsOne entry path in a broader workflow: readiness → enhancement → release → binding → trace.
FAQ
Frequently asked questions
Syntitan is an AI-Ready Data Platform that helps enterprise teams diagnose data readiness, enhance data and context, prepare restricted data, release fixed AI-ready states, and trace every AI or agent run back to that state.
An AI-Ready Data Platform prepares enterprise data for AI use by qualifying readiness, enhancing data and context, preparing restricted data, and binding production AI runs to fixed data states.
AI Readiness Qualification checks whether data can be reliably and traceably used by AI models or agents. It surfaces gaps across Privacy, Integrity, Contextuality, Conciseness, Operational Reliability, and Traceability.
AI-Ready Enhancement fixes data values, distribution, and class balance, and adds the context AI systems need to understand what the data means.
A Release State is a fixed AI-ready data state. Once released, it becomes the reference point for analysis, agent runs, and operational review.
Run Binding connects every AI or agent execution to the Release State used for that run.
Diff compares two Release States to narrow down what changed between them. Reproduce restores a previous data state so teams can investigate from evidence.
Syntitan can compare live production data against a released AI-ready baseline and surface which fields and distributions have moved.
Syntitan agents and agentic workflows run on qualified, data-grounded states with semantic context attached, not on raw files. Their outputs share the same Release State every team uses.
Data platforms store and process data. Data quality tools detect issues. Observability tools detect that something changed. Syntitan sits between them and AI execution: it qualifies whether the data is ready, enhances data and semantic context, fixes the state as a Release State, and binds every AI or agent run to that state. Other tools describe or detect; Syntitan prepares.
For selected design partners, Syntitan can preview whether internal data has the signal and context required for a target model, before full evals are run.
Which data state is your AI running on?
Most teams can’t answer. In one upload, Syntitan can.