Investor research reduced from around 15 minutes to one minute per profile.
A capital-raising team needed to source, validate and prioritise relevant investors without rebuilding spreadsheets for every campaign. Framworq built a research and outreach system with reusable profiles, verification and controlled sending.
The 30-second version
A capital-raising firm was sourcing investors by hand, with about 15 minutes of LinkedIn research per profile, before a single email went out. Framworq built an AI platform that sources, enriches, verifies and reaches investors from one dashboard, with the team approving what is sent. Research dropped to about 1 minute per profile, and by the client’s estimate one campaign run now replaces around 3 weeks of associate work.
Manual sourcing
Hours of LinkedIn research per investor, scattered spreadsheets, and mail merges that bounced.
AI sourcing + outreach
One platform that finds, enriches, verifies and prepares outreach to the right investors for each deal.
~15 min → ~1
Research per profile fell from about 15 minutes to about 1, so the team can cover far more of the market.
Weeks, in one run
By the client’s estimate, one campaign run replaces around 3 weeks of associate work.
Who we built this for
Details are anonymised at the client’s request. The profile below reflects the real engagement.
- Industry
- Investment & capital markets
- Business type
- Boutique capital-raising firm
- Department involved
- Investor relations
- Existing systems
- LinkedIn, spreadsheets, email
- Investor database
- 7,968 profiles and growing
- Project scope
- Custom AI sourcing + outreach platform
Raising capital was gated by manual research.
Every raise started with the same slow grind, and the outreach that followed often didn’t reach the inbox.
Hours of manual research
Each investor took about 15 minutes to research on LinkedIn: a hard ceiling on how many a small team could reach.
Spreadsheet juggling
Investors were reused across deals, but only by hand, flipping between sheets with no single source of truth.
Emails that bounced
Addresses were unverified, so part of every send bounced, hurting deliverability and sender reputation.
No time to personalise
Drafting a tailored email for each investor, and sending them one by one, took too long, so outreach either stayed generic or stalled.
No delivery metrics
The team tracked contacts and pipeline stage by hand, with no view of deliverability, bounces or engagement.
Couldn’t scale
More capital to raise meant more manual hours. The process didn’t scale with the pipeline.
Six manual steps for every investor
Every hand-off was done by a person, and the last step was mostly hope.
- Step 1
Search
Hunt LinkedIn for matching investors by hand.
Slow - Step 2
Copy
Paste each investor’s details into a spreadsheet.
Manual - Step 3
Research
About 15 minutes reading each profile.
The bottleneck - Step 4
List
Build the outreach list manually.
Error-prone - Step 5
Send
Personalise by hand and send one email at a time.
Painfully slow - Step 6
Guess
No tracking, so no idea what actually landed.
No visibility
What success needed to look like
We set the targets up front, so the build was measured against outcomes, not features.
Source at scale
Reach hundreds of relevant investors per deal, not the handful a manual team can manage.
Cut research time
Replace 15 minutes of manual reading per investor with automated enrichment.
Protect email quality
Verify addresses before sending to keep bounces low and protect sender reputation.
Personalise at scale
Tailor each message to the investor’s focus without hand-writing every one.
One system of record
A central investor database that’s reused and re-scored across every new raise.
Keep humans in control
Automate the busywork, but keep sending, approvals and edits with the team.
One platform, from investor to inbox
Framworq built a custom AI sourcing and outreach platform that sits between LinkedIn and the team’s inbox: discovering matching investors, enriching and scoring them, verifying emails, and preparing personalised campaigns.
It runs on tools the firm already trusted, orchestrated with n8n, enriched by AI, sent through Resend and verified with Hunter.io, with every investor stored in a reusable database. People stay in control of sending and approvals; the platform does the sourcing, research and tracking.
The same investor, with the busywork automated.
The research steps run automatically. A person approves the outreach before it is sent.
- Step 1
Source
AI finds investors that match the deal criteria.
- Step 2
Enrich
LinkedIn, company sites and the web build the profile.
- Step 3
Score
Each investor is ranked for relevance to the raise.
- Step 4
Verify
The email is verified before it is used.
- Step 5
Outreach
A personalised email, approved by the team, is sent or scheduled.
- Step 6
Track
Deliverability and status update on one dashboard.
What’s inside the platform
Components working together, each one replacing a manual step.
Investor sourcing
Discovers investor profiles that match each deal’s sector, stage and geography, from a single brief.
Multi-source enrichment
Pulls from LinkedIn, company sites and the web to build sector focus, stage, mandate and an AI summary.
AI relevance scoring
Scores each investor’s fit for the active deal, with its reasoning, so the team works the best matches first.
Email verification
Addresses are verified, with a finder as fallback. Valid emails are flagged so bounces stay low.
Outreach & campaign engine
Compose, schedule and send personalised campaigns, with merge tags, sender identities and attachments.
Deliverability dashboard
A live funnel (sourced, contacted, deliverability, bounce) with a timestamped activity feed.
Reusable investor database
Every profile is stored and re-scored for the next raise, so research done once is used again.
What stays with the team, and what we chose not to automate.
The platform does the research and the record-keeping. Decisions about who is contacted, and what they receive, stay with people.
What remained human-controlled
- Approving and sending. The platform prepares personalised outreach; the team approves it before it goes out.
- Edits to every message. The team can change any draft before it is scheduled or sent.
- Who to contact. Relevance scores order the list; the team decides which investors to approach.
- The record. Every action is kept in an activity log the team can review.
What we deliberately did not automate
- Sending without approval. Outreach is not sent on the platform’s own judgement.
- Using unchecked addresses. Emails are verified before they are used, so not every sourced profile has a usable email.
- Deliverability promises. Verification lowers bounces; it does not guarantee every email is delivered.
Delivered in phases, shipped as it was built
Structured and iterative: the team used a working sourcing engine while the outreach module was still being built.
Discovery
Mapped the manual sourcing and outreach flow, the deal criteria and the tools already in use.
Sourcing engine
Built the sourcing pipeline, the investor database and run history: the foundation.
Outreach module
Added campaigns, the email composer, scheduling, sender identities and status tracking.
Intelligence & deliverability
Layered in deep enrichment, email verification and the unified sourcing and outreach dashboard.
A team of AI agents on a stack they already run
The platform runs as coordinated multi-agent AI over the tools the firm already used, LinkedIn, Google Sheets and Gmail, with everything tracked on one live dashboard.
Multi-agent AI
Specialised agents split the work: one sources, one enriches, one scores, one drafts.
LinkedIn sourcing
Investor profiles are discovered and enriched from LinkedIn, matched to each deal’s focus and stage.
Google Sheets database
Investors and campaigns live in Google Sheets: a reusable single source of truth across deals.
Gmail + live dashboard
Personalised outreach sends from the team’s own Gmail, tracked live on the sourcing and outreach dashboard.
The same team, moving at a different speed.
The platform keeps running, so the totals keep climbing. The lasting win is speed.
By the client’s estimate, one campaign run replaces roughly three weeks of associate work, in a market where speed decides who wins the deal.
- Research time Before vs after
- About 15 minutes of manual LinkedIn research per investor profile before; about 1 minute after. This compares research time only. Verification, approval and sending are separate steps.
- Platform totals Platform dashboard
- 7,968 profiles sourced and 1,341 investors contacted, as shown on the platform’s dashboard. The totals grow as the platform keeps running.
- Deliverability and bounce Platform dashboard
- 92% deliverability and a 7% bounce rate, reported as separate measures. They are not meant to add up to 100%, and delivery is not guaranteed.
- Associate time Client estimate
- “One campaign run replaces about three weeks of associate work” is the client’s own estimate, not a timed measurement.
The dashboard at the top of this page is a recreated interface: the totals are the platform’s, the investor names are samples.
“This platform reshaped how we raise. We reach the right investors at a scale and speed we couldn’t touch before. It’s quickly become one of the most valuable tools we have.”
Why it matters
More of our work.
Two more client projects, with the workflow before and the system after.
How an Accounting Firm Saved 100+ Hours a Month with AI Automation
- 1,500
- invoices processed a month
- 100+ hrs
- of keying recovered monthly
How a Private Credit Firm Researches a Target Desk in About 7 Minutes
- ~7 min
- per target firm
- Evidence-linked
- contact details, no guessed emails
Is your team still researching one profile at a time?
Start with a free discovery. We’ll map how your team sources and contacts prospects today, then prepare a tailored initial plan and proposal. Implementation is quoted after discovery.