Each model instance is trained only on data available up to its cutoff date.
Safe for backtesting and alpha research
Point-in-time vintages, back to 1999
Self-verify cutoffs with auditable probe sets
Runs in your environment, self-host or on-prem
Our Vintage™ architecture enforces strict time cutoff
AI built to eliminate lookahead bias
A frontier model that reasons only on information available at each point in time.
ChronoLLM
The financial sector needs a new type of large language model that doesn't cheat.
Point-in-time training
Each vintage is trained exclusively on a timestamped corpus using a rolling 20-year training window and a walk-forward training methodology.
Verifiable Integrity
Fiduciary standards require independent verification. Thousands of post-cutoff leak probes let you verify the model’s time boundary yourself.
Self-hosted
Download the weights and run ChronoLLM entirely on your own hardware. Your prompts, signals, and strategies never leave your infrastructure.
Use Cases
Frontier AI + chronological consistency
Ask the same prompt of two vintages and watch the model knowledge evolve. Each one responds with only what it knows up to its cutoff - The gap between them is the lookahead bias, contained in every other large language model.
An unexplored dimension of new signals
Point-in-time LLMs transform financial analysis allowing you to measure nuances of financial markets at distinct points in time. This compressed representation of the world at regular intervals in history is an unprecedented opportunity for asset managers to generate new signals.
Get Started
Build new alpha in just 4 lines of code.
ChronoLLM will power your quant team with frontier analytic capability.
Python
import chronollm as cl
client = cl.Client('YOUR_API_KEY')
signals = client.checkpoint('2015').score(
prompt='Classify as FAVORABLE or UNFAVORABLE for the stock: {headline}',
data=headlines_2015,
)
Research
Models and parameters growth projection
Papers
Operated by Crunch
A crowdsourced ML research platform of 12,000+ data scientists serving institutional clients including ADIA Lab and the Broad Institute of MIT and Harvard.
Individual
Free
The full model, free to download and run on your own hardware. Built for quants and engineers who want to self-host and verify it themselves.
All vintages and checkpoints
Full weights
Institutional
Use-Based
ChronoLLM running inside your own infrastructure, fine-tuned on your proprietary data, with dedicated support and SLAs.
Everything in Open Weight
Private or on-prem deployment
Fine-tuned on your data
Dedicated support and SLAs
Commercial license
Priced to your deployment
Academic Research
Free
Free hosted access for non-commercial research, so you can build on ChronoLLM without standing up your own GPUs.
Hosted API, no infra needed
All vintages and checkpoints
Benchmark suite
Non-commercial license
Citation requested