Production experience
Built CECL and stress-testing models in production banks.
No-code model development for banks
Build and evaluate models in one guided workflow.
Luwian AI is a no-code platform for in-house model development at banks, structured around the principles in SR 26-2. TSXPert, our time series engine, is available for preview. LuwianIQ, our built-in AI assistant, is in development — it will guide you through the workflow, explain the analysis as you go, answer questions about the flow and upstream impact, and draft the model documentation.
Alignment with supervisory expectations depends on your implementation and your regulator's judgment. Luwian AI does not guarantee any examination outcome.
- Pre-built statistical workflows instead of hand-coded scripts
- A consistent, repeatable path for small modeling teams
- Preserve institutional knowledge when people move on
Preview access is by invitation.
Good afternoon
Build and evaluate your time series models.
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Pick up where you left off
Deposit Model Draft
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Projects
CECL Loss Rate Draft
ALM Balances Draft
Recent activity
Deposit Modeljust now
Selection committed
Deposit Model3m ago
Candidates compared
Deposit Model9m ago
Model run completed
Deposit Model18m ago
Transformations applied
Deposit Model1h ago
Data checks passed
Illustrative sample view — project names and activity shown are for demonstration only and do not represent actual customers or results. (draft — pending legal review)
“In my experience, the model that fits its purpose comes from one thing: a flow people can actually follow, with the business judgment still live inside it.”
Aykut Kahraman · CEO and co-founder
Guided, not blank canvas
Structured steps from project setup through model evaluation
Structured around the principles in SR 26-2
Structured around supervisory expectations for model risk management
One engine, three planned
TSXPert for time series today; machine learning, scorecards and LLM engines planned
No-code first
Pre-built statistical workflows — not hand-coded from scratch
Alignment with supervisory expectations depends on your implementation and your regulator's judgment. Luwian AI does not guarantee any examination outcome.
The problem
In-house model development is slow, hard to govern, and painful at exam time.
Executives evaluating Luwian AI care about time, governance, operational risk, and whether a small team can scale — not only the statistics under the hood.
Current reality
- CECL and related models take months to build and rebuild
- Documentation is manual and lags the work
- Teams juggle Excel, SAS, R, Python, and vendor black boxes
- Knowledge leaves when employees leave
- Validation becomes the bottleneck
What decision-makers ask
- How much time will this save our team?
- How does this improve governance and regulatory readiness?
- How does this reduce operational risk?
- How do we scale with limited resources?
- What is the return versus our current process?
The problem
Why we built Luwian AI
The gap we set out to close for in-house modeling teams.
Without Luwian AI
- Long development cycles and fragile spreadsheets
- Inconsistent workflows between development and validation
- Thin or late documentation for examiners
- Opaque vendor models your validators cannot inspect
With Luwian AI
- A guided pipeline instead of hand-coded scripts and spreadsheets
- A consistent, repeatable path for every model build — shared projects and independent validation in development
- Documentation generated alongside the model — in development
- Transparent, inspectable methodology — every step visible to your validators
Credibility
Built by model-risk practitioners
Luwian AI was created by professionals who lived the friction of CECL, stress testing, validation, and examiner scrutiny firsthand.
Years of hands-on work on both sides — developing models and validating them — taught me two things. A standardized flow is only useful if the people following it can understand it. And the decisions inside that flow have to stay live, so business intuition becomes an actual modeling choice rather than a comment in a memo. In my experience, getting those two things right is what produces a model that fits its purpose. That conviction, shaped as much by supervisory expectations as by practice, is the foundation of this platform — and what we are building it to do: help a team make the most of the data it already has.
Model risk, end to end
Managed model risk programs end to end.
Quant leadership
Led quantitative analytics teams.
Regulatory exposure
Worked directly with regulators and examiners.
Built for small teams
Designed workflows for 1–5 person modeling teams.
Aykut KahramanCEO & Co-founder · 17+ years in model risk
Nav VaidhyanathanChief Operating Officer · nearly two decades in model risk
Who we serve
Built for institutions that need serious modeling without a large quant team
Community banks
One- to five-person modeling teams replacing spreadsheets and opaque vendor packs.
Regional banks
Scaling model governance while keeping development in-house.
Credit unions
Resource-constrained analytics teams that still face examiner expectations.
Insurance
Time-series forecasting and risk modeling. Documentation generation is in development.
Status quo
Why not Excel, SAS, R, or Python alone?
Those tools stay valuable. Luwian AI packages the workflow around them so a small team gets speed without giving up transparency — with pre-built, not hand-coded, statistical paths.
| Dimension | Tools alone | Luwian AI |
|---|---|---|
| Coding required | Yes | No-code first |
| Tooling | Multiple environments | One platform |
| Documentation | Manual, after the fact | Drafted by LuwianIQ — in development |
| Audit trail | Fragile | Built in |
Products
One engine in preview. Three in development.
TSXPert is our time series engine, available for preview. Machine learning, scorecard and LLM engines are planned and will follow the same guided pattern.
Time series · available for preview
TSXPert
End-to-end time series work for CECL, deposits and ALM use cases. Set up the project, build the model through guided steps, and evaluate it dynamically.
In development
MLXPert, ScorecardXPert, LLMXPert
Machine learning, scorecards and bespoke AI, planned to follow the same guided pattern.
Tell us about your institution.
Send us a message with the models you are prioritizing and we will follow up directly.