Trusted by teams and leaders from
Every decision is a prediction.
Its conviction is a product of what the firm reads, how it weighs signal against noise, and what context it holds at the moment of commitment.
We built a household of AI-native co-workers under a single mandate. To lift the conviction of every judgment the firm makes, especially those taken under weight and against time.
More context doesn't buy more trust. Push a full data room through even the top model and it slips below two-thirds accuracy. It misses facts, conflates them, and fills the gaps with things that were never in the file. At report scale, unreliable is the default, not the exception.
Model accuracy at 750 pages of context
GPT-5.6 Sol 61.3%
Claude Opus 4.6 54.3%
Gemini 3.5 Flash 38.8%
GLM 5.2 34.9%
DeepSeek V4 Pro 28.8%
500K tokens · 8-needle retrieval · benchmark from Context Arena
Every number we hand back is computed in deterministic code. The model reasons over the evidence and proposes, and never emits the score itself. What you get is an honest distribution, not a false point, and every figure drills down to the claim and the source behind it.
Models propose, code computes
The authoritative score is deterministic and reproducible over a human-frozen methodology. The model does the reasoning; it never emits the number.
Distributions, not points
Each methodology runs as a probabilistic graph evaluated by forward Monte-Carlo over non-compensatory nodes. Ranges, multimodality and boundary-mass come out by construction.
Decorrelated generate-and-verify
Three independent frames, prior withheld, ideally across model families, then one whole-case synthesis. Verification from the right frame is cheaper, and more accurate, than generation.
One fatal flaw caps the result
A single grounded blocker is non-compensatory: no amount of enthusiasm elsewhere is allowed to average it away, the way ordinary scorecards quietly do.
The same technology, embedded into two specialized products. Each is purpose-built for a single job, and honed for it alone.
Prospecting
Finds the deals and the decision-makers to reach, then builds the case through to a signed IC memo.
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Due Diligence
Reads the entire company and the open web before you commit: deck to cap table, on every risk.
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Teams
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via MCP
Every co-worker is designed, trained, and held to one standard in-house, by the same people who ship it.
Top 0.1% AI engineers, paired with operators from the buy- and sell-side desk. Each co-worker is trained by someone who has lived the decision it informs: instinct, not just intelligence.
The standard trained by the desk, for the desk.
Build cycles compressed by our proprietary Layer Method: reusable, versioned blocks that carry from one co-worker to the next. Nothing is rebuilt from scratch, so each one ships faster than the last.
The compound every build makes the next one faster.
We don't sell hours or seats. Our incentives are tied to the EBITDA we help create. We're paid when the desk is, and scope the work around the decisions that move it.
The stake we win when the desk wins.
