Cut onboarding time by 95% with an AI Onboarding Agent

A marketing agency needed to collect comprehensive client info to create a strong content strategy and run a solid kickoff:

• •positioning + messaging
• •target audiences
• •competitors
• •current channels + performance signals
• •brand guidelines / tone
• •key stakeholders
• •past campaigns, proof points, case studies, etc.

Their current approach:

• •Client fills a long questionnaire
• •Schedule a 1-hour onboarding interview
• •Team does manual online research to fill the gaps

The consequences:

• •High-value consultants stuck doing low-value intake work
• •Prospects dropping off (too much effort / too many steps)
• •Onboarding takes too long, delaying time-to-value
• •Inconsistent info across accounts (depends on who ran the onboarding)
• •Lost customers because the process felt heavy and slow

They didn't need "a better questionnaire." They needed a system that does the research automatically, and only pulls humans in for final judgment.

The goal wasn't "build an AI bot." The goal was: walk into kickoff with a complete, consistent brief—without making the client do all the work.

Here's the path we took:

1. Standardize what "good onboarding info" actually means

First we defined the agency's onboarding as a structured data model, not a doc. We created a consistent template of fields such as:

• •company basics (offer, pricing signals, geo, target segments)
• •ICP + personas (inferred + confirmed)
• •positioning + messaging (site language + repeated claims)
• •proof points (case studies, testimonials, metrics)
• •competitors + differentiators
• •channels + content patterns (blog, LinkedIn, YouTube, ads, etc.)
• •brand guidelines (tone, visual rules, logos, colors, fonts — when available)
• •stakeholders (leadership, marketing team, sales team)
• •"risks & unknowns" the agent couldn't confirm

This gave the agent a clear definition of "done."

2. Build a research engine that pulls from the right sources

Next we built a crawler/scraper workflow that collects information from:

• •company website (core pages + copy patterns)
• •blogs / resources
• •press releases + news
• •public PDFs (reports, decks, brand docs, product one-pagers)
• •social content (what they post, what performs, how they talk)
• •job posts (usually reveals positioning, ICP, stack, priorities)
• •review sites / directories (if relevant)
• •stakeholder profiles (roles, priorities, messaging patterns)

Instead of "searching the web," the system gathers specific evidence and keeps track of where each claim came from.

3. Use the agent to extract, summarize, and fill the onboarding brief

Then we added the agent layer to:

• •extract key facts and map them into the template
• •summarize long documents into usable strategy inputs
• •detect contradictions or missing data (and flag them)
• •produce clean outputs in a consistent format every time

Output examples:

• •a structured onboarding profile (ready for internal systems)
• •a kickoff-ready brief (what to say / ask / confirm)
• •a first-pass content strategy skeleton (themes, angles, audiences, proof)

4. Add "human-in-the-loop" review so quality stays high

We didn't try to fully automate judgment. Instead, we designed the flow so a strategist can:

• •review the brief quickly
• •spot-check sources
• •adjust positioning assumptions
• •add nuance from the relationship context

Result: the humans do the high-value thinking, not the scavenger hunt.

5. Make it operational (so it actually gets used)

Finally, we made the agent fit into the agency's day-to-day workflow:

• •consistent outputs every time
• •easy handoff from sales → delivery
• •onboarding becomes a repeatable system, not a custom effort per account