NSGX Enterprise Evaluation
More useful context for the AI systems your business runs.
Your AI needs the right information to do useful work. NSGX organizes that information and its connections so it can be inspected and retrieved. Start with one workload and see whether the difference matters for your business.
No account, dataset upload, or purchase required to start the conversation.
Start with your problem
What would you like to make easier?
Choose a goal to see where NSGX may fit and what a useful test should measure.
A familiar problem
Why am I explaining the same decision again?
An earlier requirement can get lost when a project grows or an AI starts a new session.
What NSGX brings to the comparison
Keep decisions connected to their supporting records, then test whether later work uses them correctly.
What would a useful result look like?
- Requirements remembered
- Repeated work and completed tasks
- Source traceability and task cost
Measured against your current approach. Improvements are a question to test, not a promised result.
Discuss this goalA practical next step
A focused evaluation. A decision you can explain.
Keep the question small enough to answer clearly before expanding to a larger deployment.
- 01
Choose one real workload
Tell us where information gets lost, repeated reading gets expensive, or a system becomes difficult to understand.
- 02
Agree on a fair comparison
Choose representative data, your current baseline, success criteria and data-handling rules. Scope, timing and commercial terms come before a pilot.
- 03
Decide from the evidence
Inspect the results, including failures. Decide whether to stop, refine the test or discuss an integration.
A fair cost comparison includes preparing, storing, updating and retrieving information, plus model processing. Fewer input tokens alone do not establish a lower total cost. Existing search, caching and compression belong in the baseline.
Before you ask
Do we need to replace our AI provider?
That is not the goal. NSGX supplies structure and context for AI applications; the model still produces the answer. We confirm supported providers and integration requirements for your workload during scoping.
Does a large dataset automatically make us a good fit?
No. Strong candidates repeatedly consult growing information, depend on past decisions, need current sources, or struggle to understand relationships. A short one-off task may not justify an integration.
Is NSGX only a visualization tool?
No. Studio makes connected information inspectable by people. NSGX memory and retrieval work focuses on delivering relevant context to AI. A visual demonstration shows structure; a measured evaluation establishes retrieval quality and cost.
Do we need to send you our data now?
No. Start with a description. Any sample data, access permissions, retention requirements and deployment constraints are agreed before an evaluation. Do not upload sensitive information with an inquiry.
How would we buy it?
Depending on fit, the conversation may lead to developer integration, engine licensing, or a scoped enterprise deployment. Licensing means paying to use NSGX capabilities inside your own product or environment. Availability and terms are agreed for the proposed integration.
Let's talk about your project
You do not need an integration plan to ask.
Tell us what your system needs to remember, retrieve, or make easier to understand.
The first conversation is about your workload and a practical comparison. If there is a fit, the next step is an agreed evaluation scope—not a promise that every workload will improve.
Looking for Weav in your own AI chats? The private beta is a separate path.
Prefer email? enterbeta@nsgcore.com