our methodology

Built on systems.
Driven by intent.

Building the AI infra layer for investing

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We begin by understanding the problem behind the problem, then we architect solutions using the most effective technologies at each stage.

01

Sourcing

We scan global private markets using always-on AI agents that identify opportunities early, based on real-time data signals and pattern recognition. Every signal is filtered through your investment mandate or business context.

What we aim to achieve

Identify high-potential companies before they enter traditional pipelines

Surface deals that match thesis, stage, and sector alignment

Detect early traction via web, hiring, product, and capital signals

Eliminate noise with auto-relevance scoring

Technology leveraged

Web & social signal APIs (Crunchbase, LinkedIn, GitHub, Vercel etc)

Vector-based similarity search using LLM embeddings

Custom-trained LLMs with reinforcement learning for domain relevance

Elastic data infrastructure for real-time ingestion and scoring

02

Diligence

Our diligence engine synthesizes publicly available content, structured data, and direct interviews—creating a holistic profile of each company, updated dynamically.

What we aim to achieve

Automate the first 80% of diligence workflows

Analyze financials, customers, team, product velocity, and regulatory flags

Generate questions and collect structured responses via AI-led interviews

Compare to benchmark data from similar companies

Technology leveraged

OCR + NLP over pitch decks, PDFs, websites, docs

Custom QA agents powered by OpenAI GPT-4 Turbo for interviews

Financial data extraction via data parsers (CapIQ, PitchBook, SEC)

Risk classification models trained on historical failure patterns

03

Screening

Every opportunity is scored against customizable investment or strategic criteria, letting you focus on deals that match your preferences—across capital, traction, or sector edge.

What we aim to achieve

Score companies by fit (sector, geo, round, team background)

Apply firm-specific logic (e.g. preferred revenue model, tech stacks etc)

Maintain a living ranked pipeline—not static spreadsheets

Use portfolio feedback to refine scoring over time

Technology leveraged

Multi-input scoring models (regression + heuristics)

Memory-backed reinforcement from past declines or investments

Network mapping of data, tools, and decision points

Fine-tuned BERT models for matching investor/GP criteria

04

Matching

Our matching engine identifies the best capital or strategic partner for each company. For investors, it ensures deal flow is precision-aligned. For founders, it means warm access to relevant capital.

What We Deliver

Identify the best-fit investor or capital partner per company

Rank allocators by historical pattern, vintage, check size, geography

Surface only the most aligned deals to each investor’s inbox

Maintain context across multiple iterations of engagement

Technology leveraged

Investor preference vectorization (using historical allocation patterns)

Multi-agent memory graphs for LP/GP relationship intelligence

Dynamic CRM tagging systems for allocation appetite

Feedback-loop optimization based on opens, interest, and calls scheduled

Built by a team of founders, bankers & researchers

Our team has worked on building technology platforms, as investment bankers, as founders seeking capital and as limited partners. So we understand the nuiances of the funnel. Some of the firms we previously worked with

"We cut our analysis time by over 70% using Scion's AI systems. What used to take days now happens in real time—and with greater accuracy."

Dr. Nia Bennett
Director of Molecular Research, Givonni

Behind the scenes

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Bringing the technology together to build a team of digital analysts for you to deploy

Integrated into your workflow

Whether you're running a venture fund, a credit strategy, or a corporate development team, each AI agent plugs directly into your investment stack. Activate sourcing, diligence, or IR workflows independently, or run them as a full-stack system.

Continuously Learning

Each agent improves through reinforcement signals: from what you pass on, what you fund, and how your thesis evolves. Over time, your digital team becomes a sharper extension of your thinking—proactive, contextual, and data-rich.

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Built on systems.Driven by intent.

Built on systems.
Driven by intent.