Doesn't sleep
Launch a study at 6 pm and have the results before the morning meeting.
Hire 10 PhD‑level analysts for a third of the cost of one.
We build your own AI agents: a model fine-tuned on the curriculum you hire for, running on a GPU node inside your office, with your firm's knowledge and memory on top. Your know-how never leaves the building.
15
agents running at the same time on your node
Unlimited
tokens. No per-token bill, no rate limits
0
bytes of your research sent to a cloud model
1× / month
on-site visit to maintain hardware and software
Your own AI research desk, in four pieces.
We fine-tune a model on exactly what you hire for.
Send us the courses, qualifications and languages you screen candidates for. We turn the MFE and economics curriculum, the Python, C++ and OCaml your desk writes, and your own research playbook into training data, and fine-tune an open-source model on it. It starts the job already speaking your language.
- Stochastic Calculus
- Time-Series Econometrics
- Asset Pricing
- Derivatives & Options
- Market Microstructure
- Statistical Learning
- Portfolio Optimization
- Fixed Income
- Bayesian Inference
- Monte Carlo Methods
- Risk Management
- Causal Inference
- Python
- C++
- OCaml
- Your research playbook
- Your hiring bar
Your model. Open-source, fine-tuned for your desk.
We install a GPU node in your own office.
Your know-how is everything, so it stays on your premises. The node lets you train and test algorithms completely offline: no cloud account and no outbound connection needed.
We serve the whole stack, not just a model.
The fine-tuned model comes with skills for quant work, retrieval over your firm's research corpus, and a self-learning second brain that keeps everything your agents learn.
Second brainSelf-learning memory. Grows with every task and reviews the day overnight.
RAG on your corpusPast memos, notebooks, papers and desk notes, searchable by every agent.
SkillsBacktesting, data cleaning, paper-to-code, hyperparameter search, reporting.
Fine-tuned modelSpeaks quant from day one, served locally with no per-token cost.
Your GPU node · your office
Unlimited tokens for up to 15 concurrent agents.
No per-token bill and no rate limits. Give every researcher a team that runs studies in parallel, day and night, and hands back work you can check line by line.
Watch one at work ↓One prompt. 1,000 reproducible tests. One answer you can check.
What a quant researcher can ask an agent, and what comes back.
Quant researcher
PDFstat-arb-equities.pdfRead the attached paper and test similar strategies on BTC and ETH perpetual futures. Run at least 1,000 approaches with fully reproducible notebooks, tune the hyperparameters, and report the metrics we have used in previous notebooks.
Agent 07
Done. Approach #573 got the best results: a PCA decomposition of the cross-section to isolate residual returns, plus a fine-tuned Chronos-2 forecaster, trading long/short. Out-of-sample Sharpe is 3.42 net of fees and funding, and it holds after adjusting for the 1,000 trials. Here's the reproducible notebook so you can check the rationale and the results yourself. If you want, I can put it in paper-trading mode.
- Sharpe, OOS net
- 3.42
- Deflated SR
- 0.97
- Alpha, ann.
- 21.4%
- IC
- 0.068
- Gini
- 0.36
- Max drawdown
- −3.8%
Open notebook_573.ipynbStart paper trading
- Reading the papermethod and assumptions extracted
- Loading your previous notebooksmetrics, cost model, validation scheme
- Generating 1,000 approachesfeatures × models × horizons
- Running on your nodeBTC & ETH perps · funding · 2019–2026
- Tuning hyperparameterswalk-forward, no look-ahead
- Writing reproducible notebooksseeded, versioned, one per test
Tests 1,000 / 1,000Best Sharpe 3.42
weakstrongSharpe above 3
0102030405
0607080910
1112131415
While 07 runs this study, the other 14 keep working.
It learns from your company and from every previous test.
Every backtest, every dead end and every PM comment is written to a second brain that lives on your node. Overnight it reviews the day and improves its skills. The next study starts from what your desk already knows.
Own the know-how of your company.
A fraction of the cost of a new intern. It works like ten.
One PhD-level analyst
1 person · one study at a time
Kelvo · your AI desk
a third of the cost · 10 agents in parallel, 24/7
Doesn't get tired
The thousandth backtest gets the same care as the first one.
Knows all the context immediately
No onboarding. Your corpus, your past tests and your conventions are there from the first prompt.
Never leaves for a competitor
What it learns stays on your node, not in someone's head walking out the door.
We show up at your office every month.
Monthly on-site visit
We maintain the hardware in person: health checks, cleaning and part replacements.
Updates when they matter
New models, new skills and security patches, tested before they reach your desk.
Any emergency
If something breaks, call or text us. We respond.
You own the second brain
Everything your agents learned is yours. Export it, move it, keep it.
Totally private.
Your prompts, your data, your strategies and everything the agents learn stay inside your walls.
- No cloud model and no third-party API
- Runs fully offline when you need it to
- Your data trains no one else's model
- Your second brain is yours to export or move
Leading firms are building their own AI.
Kirkland & EllisBloomberg Law · Jun 2026
Kirkland's $500 Million AI Gambit Requires a Cast of Hundreds
An internal AI platform built with 180+ AI engineers and data scientists, designed to swap models without a rebuild.
Jane StreetBloomberg · Jun 2026Jane Street Plans New Data Center as Compute Power Runs Scarce
The trading firm plans to build and finance its own data center to meet its compute demand.
XTX MarketsBloomberg · Jan 2025Billionaire Alex Gerko's XTX to Build €1 Billion Data Hub in Machine-Learning Bet
Its own data centers for the machine-learning models behind its price forecasts.
Man GroupBloomberg · Jul 2025Man Group Says Agentic AI Is Now Devising Quant Trading Signals
AlphaGPT, built in-house, mines data, writes signal code and backtests it, with humans in the loop.
StripeTechCrunch · May 2025Stripe unveils AI foundation model for payments, reveals 'deeper partnership' with Nvidia
Its own foundation model, trained on its transaction data to improve fraud detection.
ShopifyShopify blog · Aug 2026Sidekick's continual learning loop
Shopify fine-tunes its own Sidekick agent every day and expects serving costs to fall about 96%.
Who we are
Let's set up your desk.
Tell us what you hire for. We'll show you an agent running your kind of study.