Features/Deal Pipeline/Deal Scoring

Replaces gut feel + inconsistent notes

Score Every Deal on the Criteria That Actually Matter

Your partner loved the founder's energy. You thought the market was too small. Neither of you wrote down why. Three months later you can't remember what made you pass on a deal that just raised a Series A from Sequoia. Systematic scoring fixes this.

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Inconsistent evaluation costs you deals

Most emerging funds evaluate deals by vibes. One partner writes a paragraph of notes. Another gives a thumbs up in Slack. A third just says "interesting" and moves on. When IC meets on Monday, nobody is evaluating against the same criteria, and the loudest voice in the room wins.

This isn't just inefficient — it's how bias creeps into your portfolio. Without a structured framework, you over-index on pattern matching (the founder went to Stanford, the last deal like this worked) and under-weight the fundamentals that actually predict outcomes at the seed and Series A stage.

Archstone's deal scoring system gives your fund a shared language for evaluating opportunities. Define the criteria that align with your thesis, weight them by importance, and score every deal the same way. Over time, you build a dataset of your own judgment — and you can see whether your scoring actually predicts which deals perform.

A scoring framework built for investment decisions

Customizable scoring rubrics

Define criteria that match your thesis: team, market size, product-market fit signals, competitive moat, unit economics, capital efficiency. Add as many dimensions as you need, or start with Archstone's VC-standard template.

Weighted criteria

Not all criteria are equal. If your thesis prioritizes technical moat, give it 3x the weight of market timing. Weighted scores ensure your composite rating reflects what your fund actually cares about, not a simple average.

Team scoring with consensus view

Each partner scores independently, then Archstone generates a consensus view showing average score, standard deviation, and criteria where the team diverges. Walk into IC knowing exactly where you agree and where you need to debate.

Historical score analysis

After two or three funds, your scoring history becomes a goldmine. Analyze which criteria most strongly correlate with successful investments. Refine your rubric based on data, not theory, and improve your hit rate over time.

Pattern recognition

Archstone surfaces patterns in your scoring: "You tend to score B2B SaaS deals 1.5 points higher than consumer deals on average." Awareness of your biases helps you make more intentional portfolio construction decisions.

Score-based pipeline sorting

Sort your entire pipeline by composite score to see your strongest opportunities at the top. Set minimum score thresholds for advancing deals to IC so your committee only spends time on deals that meet your bar.

How GPs use Deal Scoring

IC Decision Framework

Walk into committee with data, not opinions

Before IC, every partner scores the deal independently. The consensus view shows a 7.2 average with high divergence on "market size" — one partner scored it a 4, another a 9. Now your discussion is focused and productive instead of open-ended.

Thesis Alignment

Check every deal against your investment thesis

Your LPA says you invest in B2B SaaS at seed with $500K-$1M checks. But your scoring data reveals you've been drifting into consumer deals with larger checks. Catch thesis drift early before your LPs notice it in your quarterly report.

Portfolio Construction

Build a balanced portfolio intentionally

View score distributions by sector and stage to understand where your pipeline skews. If 80% of your highest-scored deals are in fintech, you might be building concentration risk. Use scoring data to diversify intentionally, not accidentally.

Frequently asked questions

Can I create different scoring rubrics for different deal types?

Yes. You can build separate rubrics for seed versus Series A, or by sector. A deeptech rubric might weight IP and team technical depth heavily, while a consumer rubric emphasizes market size and unit economics. Switch rubrics per deal or set defaults by stage.

How does team scoring work?

Each partner scores independently using the same rubric. Archstone calculates individual scores, team average, and standard deviation. The consensus view highlights where partners agree and flags criteria with high divergence — so your IC discussion focuses on the areas of genuine disagreement.

Does deal scoring integrate with the kanban pipeline?

Directly. You can sort and filter the kanban board by score, set minimum score thresholds for advancing deals to IC, and view score distributions across pipeline stages. High-scoring deals in early stages get surfaced automatically so they don’t stall.

Can I see how my scoring patterns change over time?

Archstone tracks every score historically. After a few quarters, you can analyze whether your scoring predicts outcomes — do deals you scored 8+ actually close and perform? Pattern recognition surfaces scoring biases and helps you calibrate your framework to your actual investment results.

More Deal Pipeline capabilities

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