Every Company Bought AI. Almost None Can Act On It.
Go Fig is an AI advisory firm. We capture the data a business already generates into infrastructure they own, then build and run the agents on top of it. Custom, senior-led engagements. Most clients come in through their sales system, and the work expands from there.
The Data Problem Wearing an AI Costume
A decade of software buying left mid-market and enterprise companies with a dozen systems that do not talk to each other. The data exists. Nothing reliable acts on it, so a person does the joining by hand.
The current wave of AI is being sold into exactly that mess. Most agents on the market are a chat window with an API bolted on the side, reasoning over a handful of rows someone pasted into a prompt, with no history, no relationships, and no way to check their work. They fail on the data, not on the model. Every AI budget eventually hits this wall.
Go Fig sells the order of operations that survives contact with a real company: capture the data first into infrastructure the client owns, model it, make every value traceable, and only then put agents on top. That sequence is the whole thesis. It is also why the work is an engagement rather than a license.
The Wall Is Already Documented
Three quarters of the leaders we interviewed told us the honest use case for AI right now is cleaning the data, not making the decision. They were not asking for agents. They were asking for their systems to be connected, reconciled, and traceable. That is the work nobody wants to do and the reason most AI pilots stall.
Agents Are the Deliverable
An agent has skills that read the governed data, triggers that wake it up on a schedule or a signal, and actions it either takes autonomously or queues for a person to approve. It runs whether or not anyone is watching. That is the difference between software a company bought and work a company stopped doing.
Sales Is the Wedge
Clients enter through the sales system because that is where the pain is legible and the return proves out in weeks: CRM admin nobody wants to do, follow-up that never happens, signals nobody acts on. Once the data layer is in and the first agents are running, the same foundation extends into finance, service, and operations without a second capture project.
Defensible Data Moat
Every connection, every reconciliation, every correction deepens Go Fig's model of that client's specific business, inside their own environment. The agents get better because the data underneath them gets better, and neither is portable to a competitor. Ripping us out means rebuilding the layer everything else now runs on.
A $240B Market by 2030
The global AI marketing and sales market is projected to pass $240B by 2030, with roughly $100B of that in the US alone. We model a $1.6B marketable opportunity inside it: the relationship-driven B2B service firms where administrative work throttles revenue directly, and where the relationship is part of the product being sold. Source: MarketsandMarkets Research, Global Forecast to 2030
Founder-Market Fit
Built by a data scientist who spent a decade on the enterprise data stack at Square, Capital One, and Oportun, then ran a small business and lived the other side of the problem. The thesis came out of a year of customer interviews, not a market map.
One Governed Data Layer. Agents On Top.
The engagement delivers both. The data layer is what makes the agents work, and it is what makes them hard to remove.
Agents
Skills, triggers, actions, and approval queues, built for the client's process and operated in production. Autonomous versus approval-gated is set per agent and per action, by the client. Teams start in approval mode and graduate what they trust. Every read, action, and approval is logged.
The Data Layer They Own
Every system the business runs on, captured into one governed model: CRM, ERP, accounting, support, product, warehouses, and the spreadsheets nobody will give up. Role-based access, audit trails, and lineage on every value. It stays inside the client's security boundary and never trains an outside model.
Celeste and Analytics
Celeste is our AI analyst, tuned to the client's data model and cadence, working in Excel, Slack, and email rather than another destination to log into. Dashboards and agents read the same governed model, so people and agents never end up arguing about whose number is right.
Why the Wedge Moved
Go Fig started in finance. That is where Nathan spent his career, so that is who he went and talked to: twelve structured interviews with finance leaders at manufacturers, PE portfolio companies, and logistics firms, run at the end of 2025.
Every one of them named the same problem. A dozen systems that did not talk to each other. Half the week spent gathering and reconciling instead of deciding. An ERP that cost millions and still had a spreadsheet taped to the side of it.
We built for that, and the building taught us the thing worth knowing: the analyst was never the hard part. The data layer was. Once it exists, the work on top of it is comparatively easy, and it is not particularly picky about which department it serves.
Strip the accounting vocabulary out of those interviews and the finding is not about finance at all. The company had bought a dozen systems, connected none of them, and put a person in the gap. That same shape shows up in sales, in service, and in operations. It was simply louder in finance, because finance has a deadline.
So the wedge moved to where the same pain is easier to price: sales. The buyer has budget, the loss is legible, and an agent that drafts the follow-up and keeps the CRM honest proves its worth in weeks rather than quarters. Finance did not go away. It is one domain among several now, and the case studies and Celeste both came out of it.
The category changed and the thesis did not. Capture the data first. Then the agents are worth building.
Built on Real Customer Problems
Institutional recognition, enterprise-grade security certification, and a thesis that came out of structured customer research rather than a market map.
SOC 2 Type II
The security and compliance bar enterprise buyers set before they will let anything read their systems. It is what makes an engagement that captures a company's whole data estate sellable at all.
Top 20 South Carolina Startup
Named among South Carolina's top emerging technology companies. Also recognized by the SC Governor's office for innovation during Innovation Awareness Month.
South Carolina Research Authority
Selected for grant funding from SCRA, South Carolina's technology commercialization agency, validating the company's technology and go-to-market approach.
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We'll be in touch with updates on Go Fig's progress. In the meantime, feel free to reach out directly at nathan@gofig.ai.
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