Expert-grade financial datasets and evaluations from current and former investment bankers, hedge fund analysts, and VC investors. Higher quality and lower cost than generalist marketplaces.
Purpose-built datasets from domain experts to train, align, and evaluate your AI models.
Supervised Fine-Tuning
Expert-crafted prompt-response pairs that mirror live investment decision processes. Your models learn valuation methodologies, deal analysis, and investment frameworks with the depth and realism of actual Wall Street workflows.
Human Feedback
Human-in-the-loop feedback from finance professionals. Our investment bankers, analysts, and portfolio managers review and rewrite model outputs for clarity, judgment, and rigor—aligning with institutional-grade standards.
Comparative Evaluation
Side-by-side comparisons ranked by domain experts analyzing reasoning quality for depth and realism. Train models to select outputs that meet the standards of investment committees and deal teams.
AI-Assisted Feedback
Scalable rubric-based scoring designed to capture authentic investor reasoning patterns. Combine human judgment on complex analyses with high-coverage automated feedback for accounting accuracy, valuation logic, and market reasoning.
Reinforcement Learning
Simulation-driven learning for financial decision-making. Train agents on portfolio optimization, trade execution, and risk management through iterative feedback in market environments.
Workflow Recording
Detailed recordings of expert financial workflows—building models, analyzing filings, executing due diligence. Teach agents to navigate Bloomberg, Excel, and research platforms like seasoned analysts.
Structured Context
Build financial knowledge graphs linking entities, events, and relationships across documents. Make precedent searchable—connect deal terms, management changes, and market events into queryable structures.
Your AI needs to reason like a portfolio manager, not an entry-level analyst. We go beyond basic KPI extraction—our work spans the real business of investing across every major asset class: interpreting management guidance, stress-testing deal memos, and parsing central bank signals.
Single-name analysis and sector-level reasoning
Parse earnings calls for what was actually said vs implied. Detect guidance posture—sandbagging, optimism, uncertainty. Extract strategy shifts, pricing signals, and risk flags.
Bridge performance through volume/price/mix, FX, and one-time items. Decompose beats and misses into core vs non-core. Identify pull-forward, channel stuffing, and cost timing.
Summarize sell-side changes—PT revisions, estimate moves, key debates. Identify consensus narrative and where dispersion is highest. Infer positioning signals with appropriate caveats.
Translate narrative into model line items—revenue, margins, FCF. Validate assumptions against history and peers. Flag sensitivities: "If price -2%, what happens to EPS?"
Map competitor actions to share shifts, pricing power, and switching costs. Detect competitive inflections—new entrants, substitution threats, regulatory changes.
Identify catalysts: earnings, product launches, regulatory, M&A. Build event trees with probability-weighted outcomes and expected value calculations.
Evaluate fundamental risks—accounting, customer concentration, cyclicality. Assess technical risks—liquidity, borrow availability, options skew.
Structure the key debates on a company—thesis and anti-thesis. Identify where consensus is wrong, what's priced in, and what would change the narrative.
Identify early warning signs—subtle language shifts, unusual footnotes, emerging competitive threats. Flag issues before they become consensus concerns.
Venture capital and private equity
Structure investment memos covering market, product, moat, GTM, unit economics, and team. Build the bull case with appropriate risk callouts.
Parse preferences, participation, anti-dilution, pro-rata, covenants. Identify economically meaningful clauses.
Choose comps, normalize metrics, reason about multiples with appropriate caveats. Assess comp set quality.
Model exit scenarios across valuation outcomes. Calculate proceeds by share class through liquidation preferences, participation, and cap structures.
Identify inconsistencies, hand-wavy claims, unrealistic projections, and missing information. Flag where narrative doesn't match numbers.
Synthesize founder background, track record, and public signals. Assess founder-market fit, leadership gaps, and red flags from prior ventures.
Rates, credit, and structured products
Identify curve moves: parallel, steepening, butterfly, real vs nominal. Map to macro narrative—growth, inflation, term premium.
Extract reaction function shifts and forward guidance nuance. Infer thresholds: "What would change their mind?"
Decompose spread changes: fundamentals vs technicals vs rates vs sector vs idiosyncratic. Identify primary driver with confidence and evidence.
Analyze seniority, covenants, maturity walls, collateral, guarantees. Map equity story to credit story—default risk vs upside participation.
Extract covenant terms and what's permitted—restricted payments, baskets. Identify loopholes and priming risk.
Assess liquidity runway, refinancing probability, exchange offer dynamics. Model recovery waterfalls qualitatively and quantitatively.
FX, commodities, and cross-asset themes
Pre/print/post reasoning for data releases. Decompose surprises and explain market reactions.
Identify drivers: rates, risk appetite, commodities, flows, intervention, politics. Build balance of payments narratives.
Classify regimes: risk-on/off, carry vs momentum, crisis correlations. Detect regime transitions.
Translate headlines into balances—inventory draws, spare capacity. Separate transient shocks from structural shifts.
Evaluate convertibility controls, reserves adequacy, refinancing risk, political stability. Catalog tail risks and triggers.
Analyze tariff implications across companies and industries. Map exposure, pass-through ability, and supply chain shifts. Track policy rhetoric vs implementation.
Derivatives, crypto, real estate, and event-driven
Read surface changes—skew shifts, term structure moves. Translate views into structures with Greeks exposure intent. Detect "hidden short gamma" risks.
Interpret funding rates, open interest, liquidations, basis, on-chain flows. Analyze tokenomics, protocol fundamentals, and governance risk.
Translate narratives into model inputs—rent growth, occupancy, cap rate. Analyze tenant credit, lease rollover, and refi risk.
Parse deal terms, regulatory risk, timeline, break price logic. Calculate probability-weighted spread explanations.
Identify forced flows from spins, splits, index inclusion. Convert litigation and regulatory outcomes into scenario impacts.
Assess event probability calibration and identify mispricings. Reconcile prediction market odds with polls, expert forecasts, and base rates.
Asset class categories with deep specialization
Coverage across public and private markets
Edge cases only practitioners know
Let's discuss how our team can help you build better training data and evaluations.