Prove AI
Before You Build It
NayaOne provides a safe, off-premise sandbox to test vendor technology under real conditions without needing production access or data.
The Blocker Is Not Ideas.
It is getting a safe environment, data, and approvals to actually test them.
01
Environments Take Months
Teams wait for access, infra, approvals, and security reviews before they can even start.
02
Data Is Hard to Get
Production data is locked down. Synthetic data is not ready. Nothing to actually test with.
03
Validation Happens Too Late
Vendors are chosen before anyone has seen real output, leading to rework and risk later.
What You Can Do With NayaOne
With NayaOne, builders can discover, evaluate, experiment with, and select the right-fit technology - all in one secure sandbox.
Discover
Access an enterprise gateway of pre-integrated AI and fintech vendors, ready for real-world evaluation.
Evaluate
Run secure proofs of concept in NayaOne’s controlled sandbox. Use realistic synthetic datasets and built-in observability to see how systems behave under enterprise conditions.
Experiment
Generate measurable results and explainable evidence to inform risk, security and architecture reviews. Turn AI experimentation into outcomes you can defend.
Select
Make clear build versus buy decisions with comparable results, audit trails and handover packages for a smooth move to vendor onboarding.
What Teams Are Testing
Live evaluations being run by architecture and product teams.
CUSTOMER LIFECYCLE
Identity Verification
Test document checks, biometric liveness, and watchlist screening using realistic synthetic profiles to compare accuracy and failure rates.
- Document Liveness Check
- Face Match Scoring
- Synthetic Identity Detection
FRAUD AND FINANCIAL CRIME
Payment Fraud Detection
Run transactions through multiple fraud engines to measure precision, recall, latency, and how well each model handles edge cases.
- Real-Time Transaction Scoring
- Identity Behaviour Profiling
- Suspicious Pattern Surfacing
GENERATIVE AI
GenAI Document Processing
Evaluate extraction, summarisation, and classification on synthetic claims, forms, and correspondence to assess accuracy and explainability.
- Auto Label Training
- PDF to JSON
- RAG Source Chunking
PAYMENT INFRASTRUCTURE
Cross-Border
Payments Orchestration
Simulate multi-currency payment flows and routing logic to compare settlement speed, cost, and reliability across providers.
- FX Rate Routing
- Compliance Check Automation
- Network Cost Optimisation
FINANCIAL CRIME
Sanctions & AML
Screening
Test name matching, entity resolution, and case flagging behaviour under realistic data variations to reduce false positives and missed hits.
- Entity Name Matching
- PEP List Screening
- Risk Alert Prioritisation
ENTERPRISE KNOWLEDGE
RAG for Internal
Knowledge Retrieval
Experiment with retrieval pipelines on synthetic knowledge bases to measure grounding quality, hallucination control, and auditability.
- Policy Answer Retrieval
- Chat With Documents
- Context-Aware Search
From the Teams Using It
Book a 30 Minute Sandbox Walkthrough With a Solutions Architect.
Get a guided 30 minute walkthrough of the sandbox and ask questions live.
FAQs
You start in a ready-to-use environment with vendors already integrated. You test capability in weeks instead of waiting months for access, data, and approvals.
Access and environments are provisioned in days. No procurement or infrastructure build required.
No. The sandbox includes high-quality synthetic datasets designed to mimic real transaction, customer, and behaviour patterns.
Datasets are synthetic and modelled on real patterns. You can explore schemas directly in the workspace.
It is isolated, access-controlled, fully audited, and designed specifically for regulated enterprises. All activity is logged.us-buy decisions. Timelines can vary depending on the scope and deliverables, but the process is structured to keep momentum and reduce wasted effort.
No. It sits before procurement. You only onboard the vendors that prove they are a fit.
Yes. Workspaces can be shared or separated. Results and artefacts can be reused across data, product, architecture, and risk.
