From idea to alpha.

Two kinds of teams. Quant research teams use Arkraft to discover and validate alpha signals. Asset managers, securities desks, and fundamental funds use it to build defensible portfolios — without building a quant operation of their own.

They diagnose a portfolio well — factor exposure, risk, benchmark comparison — but stop at "what’s wrong." Arkraft starts there: it builds and backtests the fix. The two are complementary, not competing.

No. The AI proposes and structures the research; deterministic, point-in-time-safe tools run the validation. You get a traceable workflow, not a one-shot chat answer.

Yes. Describe what you want or bring a book as a CSV, and Arkraft aligns on your intent, matches validated alphas, and returns a backtested portfolio with every allocation explained.

Yes. Proprietary data, alternative datasets, and internal factor inputs can all be brought into the same reviewable workflow.

Every result comes with a trace — what data was used, what alternatives were tested, what thresholds were applied. Backtests run on point-in-time-safe data, so there’s no lookahead.

Most start with a live hypothesis, external research, or their own data — then judge Arkraft on three things: signal quality, data fit, and how cleanly it slots into their existing workflow.

Both. Quant funds with their own data and infrastructure run Arkraft on-premise, inside their private AWS. Teams that would rather not run a data project use the fully managed SaaS — nothing to set up.