Selected outcomes from prior in-house growth and revenue operations roles.
The point is not that AI produced those numbers. It did not. The point is that the systems behind pipeline, campaigns, CRM, handoffs, and reporting are familiar territory.
The AI work started there too. One of the systems I built at Labra was Fastbot, a GPT-based assistant running inside Slack and CRM workflows with defined tasks, guardrails, and human review on anything customer-facing.
AI changes what those systems can now do. I started finding that out on someone else's payroll. See the work and its context.
Your team uses AI.
Your GTM operation still
depends on manual handoffs.
AI is already in the organization. People use it to research, draft, summarize, analyze, and move faster.
But useful output is not the same thing as a working system.
Research still starts from scratch.
Customer context still lives across calls, CRM fields, docs, and inboxes.
Campaign ideas still become manual production chains.
Someone still has to move information from one tool to another and decide what happens next.
The opportunity is not another chatbot. It is connecting AI to the way your business actually works: your context, your tools, your standards, and your decisions.
Dandelion Fire helps B2B teams move from individual AI use to operational AI capability.
These are examples of the kinds of bounded systems an engagement can build. The starting point is the business problem, not a predetermined stack.
Build a research workflow that gathers account evidence, applies your qualification criteria, identifies relevant buying signals, and prepares a recommendation for review.
The output is not another unfiltered list. It is a repeatable way to decide which accounts deserve attention and why.
Turn calls, account context, objections, and next steps into reviewed follow-up, usable CRM records, and structured intelligence your team can actually act on.
AI handles defined work. Your team controls the decision and the customer-facing action.
Connect approved positioning, customer evidence, campaign strategy, and creative direction to AI-assisted production and review.
Build a repeatable operating system for the work, without flattening the judgment that makes it useful.
Connect the systems underneath GTM: routing, CRM hygiene, reporting, enrichment, handoffs, and recurring operational work.
Use agents, automation, integrations, or custom development where they materially change the process.
The four systems above are scoped examples of what an engagement can build. Here is one that already runs.
At Labra I built Fastbot, a GPT-based assistant embedded directly in Slack and CRM workflows. Structured outputs, instruction hierarchies, compliance guardrails, and human review on anything customer-facing.
It operated inside a live revenue team with real pipeline moving through it, not a pilot environment. Built in-house in a prior role, before Dandelion Fire existed.
That is why I am specific about context, exceptions, and handoff. The model was the easy part. Getting a system to hold up inside a working revenue operation, and getting a team to actually use it, is the work.
Move one AI use case from ambition into operation.
A focused engagement to identify the right opportunity, design the system, and build a working AI-enabled workflow around your team.
I start by understanding what the work looks like today: who owns it, where time disappears, what context the system needs, what tools are already in place, and how we would know the new version is better.
Then I build. That can mean agent workflows, integrations, automations, custom interfaces, or a combination of them. Existing tools stay when they are the right tools. Custom development earns its place.
Map the current workflow, establish the business case, review the available systems and data, and design the implementation.
The diagnostic stands on its own. You can stop there, take the specification elsewhere, or proceed with Dandelion Fire. If we proceed, the $5,000 is included in the total engagement price.
Implement the agreed workflow against real inputs. Depending on the use case, that may include:
A demo is not the finish line. I test against representative work, document failure and exception paths, compare performance to the baseline, and hand the system to a named internal owner.
Final implementation scope and fee are agreed after the diagnostic and before the build begins. Third-party software costs and ongoing support are separate and agreed in advance.
Give your AI offering a story sales can use, and defend.
Your product or service has changed. Now buyers need a clear explanation of what the AI capability does, why it matters, where the boundaries are, and what supports the promise.
I turn your capabilities, customer evidence, buyer questions, and sales context into a coherent commercial story.
This is commercial positioning and evidence organization. It is not a technical certification, legal opinion, or assurance engagement.
Dandelion Fire runs on Atelier, an agent system I built and operate in-house. It is where I test how context, agents, review, and accountability fit together in real work. It is an internal operating system, not a claim that every component has been deployed inside a client organization. The same design principles guide client systems.
Generic models know the internet. Useful systems need to understand your company: your positioning, criteria, data, customer language, standards, and constraints.
Agents are useful when they have a job, an input, an expected output, and boundaries. The goal is not autonomous everything. It is reliable work.
Human approval is not a disclaimer bolted onto the end. For high-judgment or customer-facing actions, review is designed into the workflow. The system proposes. A person decides.
A workflow should be tested against examples, exceptions, and an agreed standard before it becomes operating infrastructure.
Ownership, documentation, operating costs, limitations, and exception handling are part of the implementation.
The strongest fit is a software or services business with an active AI priority and a GTM use case important enough to implement properly.
You may already have people experimenting with AI. You may have a RevOps team, a marketing stack, and developers.
That is fine.
Dandelion Fire is not there to replace the organization. The work is most useful when a team knows AI matters but needs one valuable capability designed and operational sooner than internal capacity allows.
You do not need another AI experiment. You need something working.
"The model was never the hard part. The hard part is the system around it, and the team that has to trust it."
I spent my career on the operating side of go-to-market before starting Dandelion Fire. At Labra, I owned growth across marketing, RevOps, SalesOps, CRM, and cloud-marketplace motion, building the demand engine from near zero to more than $6M in net-new qualified pipeline. Before that, I led growth marketing and revenue operations at Shelf, rebuilding the systems underneath a B2B AI company's demand engine.
That operating background is why I approach AI differently. The question is not where can we add a model. It is what the GTM team needs to be able to do, what context the system needs, where judgment belongs, and how the implementation earns its place.
I also built my first production AI workflow inside a revenue team rather than a lab. Fastbot ran in Slack and CRM at Labra, with guardrails and human review on customer-facing steps, and it taught me what breaks between a working demo and a system a team relies on.
Dandelion Fire combines that commercial perspective with hands-on AI implementation: agentic workflows, integrations, structured context, automation, and custom systems. You work directly with me from the initial diagnosis through the build.
Outside client work, I make music and build things because I like seeing systems become real. The field on this site is one of them.
Bring an initiative, a workflow, or a recurring piece of work that you know should operate differently. We will talk through what happens today, what a better capability would look like, and whether a focused implementation makes sense.
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