Case study snapshot: G2i wanted fewer noisy leads. Unburdn built an Agentuity workflow that finds hiring intent, filters for fit, and delivers qualified opportunities in Slack.
- Customer: G2i
- Builder: Unburdn
- Problem: outbound and intent-data tools produced too much noise after roughly two years and more than 20 evaluations
- Workflow: discover hiring signals, filter for ICP, enrich company context, dedupe, identify contacts, and deliver approved opportunities in Slack
- Pilot signal: roughly 10 to 15 qualified leads per run, about a 10% response rate, and the first job post and first fill from the motion
For go-to-market teams, the problem is rarely a lack of data. It is a lack of signal.
G2i had already tried the off-the-shelf path: spend months evaluating AI tools, intent-data products, enrichment platforms, and services that promised to surface the right opportunities. None of them came close to what the team needed.
The company knew exactly what it wanted: identify companies with real hiring intent that matched its ideal customer profile, then use that context to start a high-quality conversation.
But after evaluating tool after tool, the results were not good enough. The output was noisy. The targeting was inconsistent. The workflows still required too much manual work. And the leads were not the kind of companies G2i wanted its executive team spending time on.
So G2i took a different approach.
Working with Unburdn, and building on Agentuity, G2i turned outbound prospecting into an agentic exercise: one designed not to generate as many leads as possible, but to find the right ones.
Why This Fit
G2i is a video-based hiring platform for contract and full-time engineers. Its own positioning is built around reducing hiring noise and increasing signal through technical matching, talent evaluation, and a mix of AI assistance and human judgment.
Unburdn helps companies adopt AI. Their methodology is simple on purpose: educate the team, discover the work that actually matters, then build the automation, workflow, or agentic system when the opportunity is clear.
Their AI enablement work keeps that from becoming a one-off workshop, with ongoing support, reinforcement, and review after the first workflow ships.
That context matters here. G2i already thinks in terms of hiring signal. Unburdn already thinks in terms of workflows that get built, measured, and improved. As Unburdn puts it in its manifesto, prompts are fine, but workflows are better.
Outbound Was Too Noisy
Patrick Severs, G2i's Head of Revenue and Human Data, framed the pain clearly: every go-to-market team is looking for intent. Whether you sell software, services, or something else entirely, you want to know when a company has a real reason to talk to you.
For G2i, that signal was hiring intent.
The team wanted to identify companies hiring for the kinds of engineering roles G2i could support, especially ones that matched a very specific profile: high-growth, growth-stage, often around Series A or Series B, or recently through a growth event that could create new hiring demand.
But the existing market did not deliver what G2i needed. Traditional intent data and enrichment tools produced too much noise: duplicate entries, mismatched company types, incomplete context, and leads that looked interesting in a spreadsheet but were not worth pursuing.
G2i had spent roughly two years exploring options. They demoed more than twenty products and evaluated different approaches, but the conclusion was consistent: generalized outbound did not feel worth the cost or effort.
That mattered because G2i was not trying to automate a spray-and-pray motion. They already knew that highly targeted executive outreach could work. When the right person had the right context and reached out to the right company, prospects responded. The missing piece was a reliable way to find those companies at scale.
The bar was not "more leads."
The bar was: can we surface a short list of companies we actually feel confident reaching out to?
Find, Filter, Enrich, Deliver
Unburdn built a workflow that starts where G2i's buyers actually spend time.
Instead of relying only on static databases or shallow enrichment, the agentic workflow searches across high-value sources where hiring signals appear: the web, social platforms, communities, job postings, and other places where relevant companies and individuals are active.
That broad collection step is only the beginning. The real value comes from the filtering.
The agents look for companies that match G2i's ICP, screen out companies that have already been recommended, enrich the opportunity with funding and context, and identify the right person to contact when possible. If the signal starts with an individual post, the workflow may already know who the contact is. If the signal starts with a company-level posting, another agent works to identify the most relevant person inside that company.
The final result is not a giant spreadsheet. It is a curated set of opportunities.
On each run, G2i receives roughly 10 to 15 highly qualified leads directly in Slack, with the context needed to decide whether and how to reach out. For the sales team, the experience is simple: the agentic workflow runs behind the scenes on a schedule, and the outcomes appear where the team already works.
All the team has to do is react with an emoji, and that is enough to keep the workflow moving.
Where Agentuity Fits
The system is built around multiple agents, each responsible for a specific part of the process:
- Signal discovery — agents search high-value sources across the web and communities to find relevant hiring signals.
- ICP filtering — another agent evaluates whether the company fits G2i's criteria, including stage, funding, growth signals, and role fit.
- Deduplication — the system checks whether G2i has already seen or received that company before.
- Contact discovery — when the signal is company-level, an agent works to identify the right person to contact.
- Human review — Unburdn reviews the output before delivery, keeping a human in the loop while the workflow matures.
- Slack delivery — the workflow formats the approved leads and posts them into G2i's Slack channel.
Agentuity is the infrastructure layer underneath those agents.
Unburdn uses Agentuity sandboxes for the isolated, coding-agent-style work this needs. The workflow spins one up, runs an agent inside with the right skills, lets it traverse and reason over web signals, then tears the sandbox down when the task is done.
That pattern matters because this is not a simple API call or a single prompt. The workflow is dynamic, long-running, and non-deterministic. It needs to search, inspect, reason, filter, enrich, and hand off results between steps.
Agentuity provides the cloud foundation for that kind of agentic execution: sandboxes, managed runtime, model access, logs and traces, and the services needed to run agents as workflows instead of demos.
For G2i, the complexity stays behind the scenes. For Unburdn, Agentuity provides the infrastructure to build and operate the workflow. And for us, the use case shows what agent-native cloud infrastructure is built for: real business processes that need more than a chatbot.
A Working Workflow in Weeks
G2i had spent nearly two years evaluating outbound and intent-signal products without finding anything worth adopting. With Unburdn, the first working demo was built during the sales process. After the engagement began, G2i started seeing real leads quickly, then refined the agents over the following weeks.
Within roughly three weeks, the team had a functional agentic outbound workflow they could run and evaluate.
The result was not just faster implementation. It was better outcomes.
G2i began receiving a focused list of high-quality opportunities every week. The team started outreach, refined the messaging, and saw responses come in. Near the end of the initial pilot, G2i received its first job post from the motion, followed by its first fill.
Patrick estimated the workflow was producing about a 10% response rate, a strong result for targeted outbound. He described the ROI threshold as low: a single filled role could justify the investment.
Just as important, the workflow changed how the team felt about outbound. Instead of asking people to chase weak leads, G2i could give its team a curated list of companies worth contacting, with enough context to make the outreach personal.
That is the difference between automating activity and automating leverage.
Quality Over Quantity
A lot of outbound automation fails because it optimizes for the wrong metric.
It creates more contacts, more records, more enrichment fields, more sequences, and more messages. But if the underlying signal is poor, the workflow only moves the noise faster.
G2i and Unburdn took the opposite approach. They designed the agents around quality constraints:
- Does this company fit the ICP?
- Is there a real hiring signal?
- Has G2i already seen this company?
- Is there enough context to justify outreach?
- Can the team identify the right person to contact?
- Would an executive feel confident reaching out?
Those constraints are what made the agent useful.
The agent did not replace the human relationship-building motion. G2i still keeps outreach personal and targeted, often coming from executive-level channels. The agent's job is to make that human motion more effective by finding the right opportunities first.
What Came Next
The hiring-signal workflow also opened the door to adjacent use cases.
Unburdn built another workflow for G2i to monitor a list of companies they had already worked with. The agent checks those companies' career pages, detects newly posted jobs, and alerts the team when there is a relevant change.
That is a simple but useful pattern: agents can continuously monitor the world for business events that matter, compare what changed, and notify the team only when action is needed.
G2i is also exploring additional agentic workflows across its go-to-market and delivery operations, including workflows that listen to intake calls, generate internal summaries for the talent team, and support interview review processes. The broader goal is to reduce the amount of manual work required after a sale while improving the quality and consistency of internal operations.
Why It Worked
This story works because each participant brings a different piece of the puzzle.
G2i brought a clear business problem: find high-fit companies with real hiring intent, without creating another noisy outbound machine.
Unburdn brought the agent-building expertise: how to translate that business problem into a secure, private, outcome-driven workflow with the right search, filtering, enrichment, review, and delivery steps.
Agentuity brought the infrastructure: an agent-native cloud platform that lets teams build and run workflows with the runtime, sandboxes, model access, observability, and deployment foundation needed for agents.
Together, the result is a practical example of what agents can do when they are aimed at a concrete business outcome.
Not "AI for sales."
Not another database.
A set of agents that find the right signals, apply the right judgment, and deliver the right opportunities to the team at the right time.