Building an AI-First Research Workflow

Traditional research workflows are broken. You spend hours reading feeds, taking notes, and synthesizing insights—only to forget most of it a week later. AI-first research workflows flip this. Agents handle the reading, summarizing, and tagging. You handle the judgment, frameworks, and narrative. The result: research that compounds instead of decays. This guide shows how to build an AI-first research workflow that compresses your research day from 8 hours to 2 hours.

Stack the Right Inputs

Great research workflows start with ruthless curation. You can't process everything, so you need to process the right things. Use feeds that map to your edge: patents, specialist Discords, investor updates, and founder logs—not generic tech news.

Feedly Board Setup

Feedly is the foundation. Set up boards for different signal types:

• Patents: Track patent filings in your space. Patents signal where companies are investing R&D.
• Specialist Discords: Join 3-5 specialist Discords. These are where early signals appear before they hit mainstream.
• Investor Updates: Track investor newsletters and portfolio company updates. These signal where capital is moving.
• Founder Logs: Follow founder blogs and Twitter accounts. These signal what builders are thinking and building.

Example setup: A board for "Synthetic Cofounders" with feeds from patent databases, AI agent Discords, investor newsletters, and founder blogs. This gives you a curated feed of signals specific to your edge.

Ruthless Curation

Curation is the key. Most feeds are noise. You need to filter for signal. Use Feedly's AI to highlight articles that match your interests, then manually review to ensure quality.

Filter criteria:

• Does it relate to your edge? (If not, skip)
• Does it provide new information? (If not, skip)
• Does it have actionable insights? (If not, skip)

Most articles fail these filters. That's fine—you only need the 5-10% that pass. Ruthless curation saves hours of reading time.

Signal Sources

Different signal sources provide different value:

• Patents: Early R&D signals (6-12 months ahead)
• Discords: Real-time community signals (immediate)
• Investor updates: Capital flow signals (3-6 months ahead)
• Founder logs: Builder thinking signals (immediate)

Combine these sources to get a complete picture. Patents show where R&D is going; Discords show what communities are talking about; investor updates show where capital is moving; founder logs show what builders are thinking.

Agentic Memo Generation

Once you have curated feeds, use agents to generate memos. Agents can read articles, extract key points, and tag signals by theme and probability. This compresses hours of reading into minutes of review.

Memo Structure

Structure memos for quick scanning:

• Summary: 2-3 sentence summary of the article
• Key points: 3-5 bullet points of key insights
• Signals: Tagged signals by theme (e.g., "synthetic cofounders," "AI creators")
• Probability: Estimated probability the signal is real (high/medium/low)
• Action items: What to do with this information

Example memo:

Summary: A startup raised $5M to build a synthetic cofounder platform for product teams.

Key points:

• Focuses on product teams (spec writing, ticket grooming)
• Uses GPT-4 for complex reasoning, GPT-3.5 for routine tasks
• Targets 30-50% time savings on routine work

Signals: synthetic cofounders, product automation

Probability: High (funded, has customers)

Action items: Track this company, research their approach, consider similar opportunities

Tagging by Theme

Tag signals by theme so you can find related signals later. Use consistent tags across memos:

• By topic: "synthetic cofounders," "AI creators," "token-gated economies"
• By stage: "early-stage," "growth-stage," "public"
• By signal type: "funding," "product launch," "partnership"

Consistent tagging lets you search for all signals on "synthetic cofounders" or all "funding" signals. This creates a searchable knowledge base that compounds over time.

Probability Estimation

Estimate probability that a signal is real. This helps you prioritize which signals to act on:

• High: Funded, has customers, public data
• Medium: Rumored, early-stage, limited data
• Low: Speculation, unverified, unlikely

Focus on high-probability signals first. Medium-probability signals are worth tracking but not acting on. Low-probability signals can be ignored.

Research Compression Techniques

Research compression means reducing information to its essential signals. Instead of storing full articles, store extracted signals. This makes research searchable and actionable.

Extract Signals, Not Articles

Don't store full articles. Store extracted signals. A signal is a piece of information that informs decisions. Articles contain multiple signals; extract the signals, discard the rest.

Example: Instead of storing a 2,000-word article on "synthetic cofounders," extract the signal: "Startup X raised $5M to build synthetic cofounder platform for product teams." This is the signal; the article is just context.

Link Related Signals

Link related signals to create knowledge graphs. If you see multiple signals about "synthetic cofounders," link them together. This creates a knowledge graph that shows patterns and trends.

Example: Link signals about "synthetic cofounder funding," "synthetic cofounder products," and "synthetic cofounder customers" to create a knowledge graph of the synthetic cofounder market. This graph shows where the market is going.

Compress Over Time

Compress signals over time. As you learn more, older signals become less relevant. Archive or delete signals that are no longer useful.

Example: A signal about "Company X raising Series A" becomes less relevant after the raise is public. Archive it, but keep the signal about "Company X's product strategy" if it's still relevant.

Tool Stack Recommendations

The right tool stack makes AI-first research workflows possible. Here's what works:

Feedly for Feed Curation

Feedly is the best tool for feed curation. It supports RSS feeds, newsletters, and social media. Use Feedly's AI to highlight articles, then manually review for quality.

Setup: Create boards for different signal types, add feeds, use AI to highlight articles, manually review highlights.

Claude/GPT for Memo Generation

Use Claude or GPT to generate memos from articles. Feed articles to the model, ask it to extract key points and tag signals, then review the output.

Prompt template: "Read this article and create a memo with: (1) 2-3 sentence summary, (2) 3-5 key points, (3) tagged signals by theme, (4) probability estimation, (5) action items."

Notion for Signal Storage

Use Notion to store signals in a searchable database. Create a database with fields for: summary, key points, signals, probability, action items, source, date.

Setup: Create a "Signals" database, add fields, import memos from Claude/GPT, tag and link related signals.

Custom Scripts for Automation

Use custom scripts to automate the workflow. Scripts can:

• Fetch articles from Feedly
• Generate memos using Claude/GPT
• Store signals in Notion
• Link related signals

Example: A script that runs daily, fetches new articles from Feedly, generates memos, stores signals in Notion, and links related signals. This automates the entire workflow.

Workflow Examples

Example 1: Daily Research Workflow

Morning (30 minutes):

• Review Feedly highlights (10 minutes)
• Select 5-10 articles to process (5 minutes)
• Generate memos using Claude/GPT (15 minutes)

Afternoon (30 minutes):

• Review memos, extract signals (15 minutes)
• Store signals in Notion, tag and link (15 minutes)

Total time: 1 hour per day. Traditional workflow: 4-6 hours per day. Time saved: 3-5 hours per day.

Example 2: Weekly Synthesis Workflow

Weekly (2 hours):

• Review all signals from the week (30 minutes)
• Identify patterns and trends (30 minutes)
• Write synthesis memo (1 hour)

Output: A weekly synthesis memo that summarizes the week's signals, identifies patterns, and provides actionable insights. This memo can be shared with your team or published as a newsletter.

Example 3: Article Generation Workflow

Monthly (4 hours):

• Review signals from the month (1 hour)
• Identify compelling narratives (1 hour)
• Write article based on signals (2 hours)

Output: A public article that compounds SEO and authority. Articles like "Synthetic Cofounders Guide" and "Prediction Markets 2026" are generated from research signals.

From Notes to Narratives

The best research workflows route signals into narratives. Instead of letting signals decay in a database, turn them into articles, newsletters, or frameworks that compound value.

Route Signals to Articles

When you see a compelling pattern in signals, route it to an article. Articles compound SEO and authority, turning research into distribution.

Example: If you see multiple signals about "synthetic cofounder platforms," write an article on "The Operator's Guide to Synthetic Cofounders." This article ranks for high-intent keywords and establishes you as an authority.

Route Signals to Newsletters

When you see weekly patterns in signals, route them to a newsletter. Newsletters build audience and authority, turning research into distribution.

Example: If you see weekly signals about "AI creators," include them in your newsletter. This provides value to subscribers and establishes you as a signal source.

Route Signals to Frameworks

When you see patterns that form frameworks, route them to frameworks. Frameworks help people think, turning research into tools.

Example: If you see patterns in "synthetic cofounder deployment," create a framework for "How to Deploy Synthetic Cofounders." This framework helps people make decisions, establishing you as a thought leader.

Conclusion

AI-first research workflows compress research time from 8 hours to 2 hours by using agents for reading, summarizing, and tagging. You handle judgment, frameworks, and narratives. The result: research that compounds instead of decays.

The key is starting with ruthless curation, using agents for memo generation, compressing signals over time, and routing signals to narratives. Research workflows that do this create compounding value: signals become articles, articles become authority, authority becomes distribution.

For deeper insights on deploying AI agents, see our guide on synthetic cofounders and our analysis of AI creators as influencers.