A GTM question used to start with a scavenger hunt. Pull one set of data from the warehouse. Grab another source. Export CRM context. Stitch it together in a spreadsheet, filter it by hand, and only then start looking for the pattern you actually cared about.
Today, many of those same questions start as a conversation. Someone in GTM can ask, in plain language, how a part of the recruiting funnel is behaving, where a target population sits in our process, or what changed in a slice of pipeline. The answer can come back in minutes, grounded in the same governed data and tools our internal systems use.
That before and after is the clearest way I know to explain what it feels like to build at Savvy Wealth.
We build worlds here
I use that phrase because it is the best description I have found. At Savvy, you get to build worlds.
Not a ticket that disappears into a backlog for six weeks. Not one more dashboard hanging off a reporting stack. You can start with a real operating problem and build the platform that changes how the work gets done.
We are small enough that there is very little distance between seeing the problem and putting something useful in production. We are also AI enabled in a practical sense. AI is in the building process and in the systems people use.
Over the last eight months, a very small team has built three internal platforms totaling roughly 650,000 lines of code across about 2,100 commits. The analytics platform started in January. The call and coaching platform started in April. Savvy Labs, our internal builders platform, started in July.
Connecting the disconnected neurons
The recruiting and sales MCP is probably the cleanest example.
MCP is a standard way to give an AI model a set of governed tools instead of a pile of raw data.
Before we built it, four important parts of our GTM operation lived separately: our sales knowledge base, call intelligence, funnel metrics, and licensed industry advisor data. The problem was that they did not talk to one another.
Human beings were the integration layer.
If you wanted to understand a target segment, build a prospect list, inspect a pipeline pattern, and add context from what the team was hearing on calls, you had to do the joining yourself. That usually meant exports, spreadsheets, manual filters, and context switching.
Now those sources sit behind one governed surface with more than 50 tools. Someone can ask a question in Claude, Claude Code, or Slack, and the request hits the same controlled layer. Identity is resolved on the server, so a phrase like "my pipeline" means the person asking. The user cannot repoint that phrase at somebody else.
That changes the pace of the work. ICP analysis, TAM work, target list building, and pipeline analysis can happen in an afternoon instead of requiring manual assembly across systems.
Ryan Walker, who works on marketing and demand generation at Savvy, wrote about this in a LinkedIn post. He is not an engineer, which is exactly why his perspective matters:
"It's crazy to think that at the beginning of this calendar year, I had never used Claude. Now it's hard to imagine working without it.
Analytics. In past jobs I'd build every chart by hand for reporting. Hours of meticulously assembling datasets, running the numbers, then hunting for patterns. All of that now takes minutes."
That is the shift I care about. A GTM operator can use the system directly without becoming a data engineer first.
Why we built instead of bought
We made the same choice with analytics and call intelligence.
We did not buy Tableau for the analytics layer, and we did not buy Gong for the call and coaching layer. That choice was about fit for our motion, not a verdict on either product.
Our analytics platform sits on a BigQuery warehouse fed from Salesforce on a six hour cycle. It covers funnel analytics, forecasting, self service reporting, and call intelligence in one place. A single semantic layer governs how every AI surface reads the warehouse. Change that file, push it, and every AI surface picks up the change within five minutes. The Slack bot uses the same governed tools to answer a natural language question with a written response, charts, or a spreadsheet.
The call platform follows the same philosophy. Recorded sales calls can become a clean written summary, but that summary goes to the rep's manager for review and nothing reaches Salesforce until a human approves it. Coaching is scored against a role specific rubric, and every claim has to cite a specific line in the transcript or a specific piece of the knowledge base. If the citation requirement is not met, validation fails and the system retries. That is enforced in code. Reps do not automatically receive the AI evaluation. Only a manager can share it.
When the AI proposes a CRM field update, a manager accepts, edits, or rejects each field one at a time. The approved change is recorded as the human's action.
Buying is often faster on day one. Building can be faster on day two hundred, when the workflow you need does not exist cleanly inside somebody else's product. For us, the benefit is shaping the software around the recruiting motion rather than reshaping the motion around the software.
The outbound machine
Outbound followed the same pattern.
Instead of buying a sequencing tool, we built one around how advisor recruiting works here. It builds a prospect list from the warehouse, dedupes it against the live CRM, allocates prospects across the active rep roster while accounting for time off, and schedules sequenced touches. Outbound sends remain gated behind feature flags and a supervised pilot.
Each rep gets a small installable web app and a browser side panel for the day's work. Outcomes feed back into the system monthly so the next draw can re rank prospect segments based on what actually converted.
It is a closed loop between selection, execution, and learning.
A reply from a prospect stops the sequence immediately. We are interested in moving quickly, but speed without controls is not the goal.
Automation should remove mechanical work. When a system touches a person or writes to a system of record, there is a human review or supervision point in the flow.
Savvy Labs
Savvy Labs is the newest platform, and in some ways the most meta.
It is where we catalog, share, and build approved AI skills and small internal apps. There is one authorization core with two front doors: a web app for people, and a remote MCP server that Claude can connect to. A person and an AI see the same authorized catalog because both go through the same permission checks before anything is retrieved. Having a link grants nothing by itself.
It also holds the governed, versioned answer to a question every growing technical organization has to answer: how do we build things here?
That guidance is not a loose wiki page. It has a draft and publish lifecycle, version history, and an audit trail. An assistant can answer questions across that corpus, and its citations are tied to what the server actually read and validated before the answer is composed.
Savvy Labs can also provision a new project, repository, hosting, and database, then list the finished project in the internal catalog. A person can drive that from a wizard or through a conversation with Claude. Provisioning sits behind an explicit confirmation step and a per person quota.
The deeper value is distribution. In a lot of companies, someone builds a useful internal tool and the story ends there. A few people know about it. Maybe there is a Slack thread. Maybe somebody bookmarked the link.
We are trying to make "someone built a useful thing" become "the people who are allowed to use it can find it, understand it, and use the same approved version." Published versions are immutable and retained, while the live version is a separate pointer, so rollback is a real operational action rather than a new artifact pretending to be the old one.
Governance and speed are often discussed like opposing forces. We are building them into the same system.
Who likes working this way
This environment is not for everyone.
It is probably a good fit for the person who is happier opening an empty repository next to a real business problem than picking up a well groomed backlog item. The person who wants to understand the operation closely enough to design the system around it. The engineer who likes GTM problems. The operator who wants to build. The GTM person who looks at a manual process and starts sketching the machine that should replace the mechanical parts.
What I value most at Savvy is that we are not hamstrung by layers between idea and execution. We can move fast, think big, and build big, while putting real controls around the places where automation touches people or records.
That is what I mean when I say we get to build worlds here.
