You asked a question about last quarter. The answer came back fast, sounded confident, and was wrong in three different ways: the time math drifted, a detail got fabricated somewhere in the middle, and the KPI definition flattened to something generic that had nothing to do with how your business actually runs.
You either caught it before the meeting or you did not. One of those is a minor correction. The other is a credibility problem.
This is the problem Modern Gravity is built for. It is generally available today.
If you run a function and own the numbers upstream, you have already been here. You are not asking for a simpler question. You need the answer to be right, and you need to be able to show your work.
Modern Gravity is an analytics agent built to answer the strategic questions you have to defend. Not the easy ones. The ones where being wrong has a cost.
The three problems it closes
The first is time math drift. Ask the same question twice, using different phrasing, and a generic agent often returns different numbers. It picks a calendar because it guesses, not because it knows. Modern Gravity holds the period semantics for your business as defined facts, not inference.
The second is hallucinated details. Numbers that sound right but weren't computed. The agent fills gaps with confidence because it has no other option. Modern Gravity grounds every answer in deterministic computation. The number comes from SQL that ran against your actual data. Every answer ships with its receipt: the query that ran, the aggregated rows it read, and the metric definition behind the figure.
The third is a lack of business understanding. Your operating segments, your channel definitions, your KPI variants, your fiscal calendar. A generic AI stack has none of that. It gives you the industry-average answer. Modern Gravity carries your business context explicitly, so the answer reflects how your company actually works.
Why this exists.
Matt Kott, Founder
I ran a function at the C-Suite level. At some point in that role I needed an answer I could actually put in front of a board, not one that sounded right in the meeting and fell apart on closer inspection. I went looking for a tool that could give me that. I found tools that gave me fast answers and tools that gave me impressive demos. I did not find one that gave me a number I could defend, with the computation attached, grounded in how my specific business was actually structured.
So I built one. Modern Gravity is what I needed that tool to be.
Modern Gravity is built for those questions.
Where this comes from
Matt Kott, Founder
Before Modern Gravity was a product, it was something we built for ourselves.
The team that runs Modern AI coordinates through a shared, searchable business context layer: every operating definition, every decision, every active principle in one place. The team searches it. The AI agents on the team read it before they act. Humans and AI drawing from the same source so neither one goes off in a direction the other cannot follow.
That is the pattern Modern Gravity brings to your enterprise. We built it because we needed it. We use it every day. When we say it works, we are describing something we run our own company on, not a product we designed top-down from a market thesis.
Most analytics-AI vendors built their products to fill a gap they saw in the market. We built ours to fill a gap we experienced ourselves. That difference shows up in how the product is designed and in what we know about where it breaks.
How it works, in one sentence
Modern Gravity takes the strategic question you would ask a senior analyst, answers it using your actual data, and has two independent frontier model families verify the result before it ships.
That last part is not a feature. It is the point. When you present a number to a board or a leadership team, one model's confidence is not enough. Two independent families have to agree.
Built for your team and your AI, on the same context layer
The business-context layer Modern Gravity builds does two jobs, not one.
The first is the one most buyers lead with: grounding the AI agent. When the agent answers a question, it draws from your definitions, your calendar, your segment structure. That is what closes the accuracy gap.
The second job is for your team. The same business-context layer is searchable by the humans who run your business. A controller can look up the metric definition. An operations lead can check what period boundary is in force. A new hire can understand how the company actually measures what it measures. Single source of truth your team and your AI both use.
This matters because business context lives in too many places at once: slide decks, email threads, tribal knowledge, a spreadsheet someone made in 2019. Modern Gravity is designed to consolidate that into one layer your team edits in plain text and your AI reads before every answer. When the layer is accurate, both the humans and the agents are working from the same ground truth.
The number
0.95 accuracy on a difficult internal benchmark of strategic business questions. That number reflects our internal benchmark. It is not a production-environment guarantee. Questions a senior operator would actually ask, not simple retrievals. The same benchmark where a pure retrieval approach scores substantially lower, underscoring that business-specific context is the variable that moves the number.
That is the gap Modern Gravity closes.
Why traceability is not optional
An answer that is right 95% of the time is only useful if you can tell which 5% is wrong. Modern Gravity ships every answer with the audit trail: the SQL query, the aggregated rows it read, and the metric definition used. If the number looks off, you check the receipt. If the receipt is wrong, you have something concrete to fix. This is different from asking a model to explain its reasoning after the fact. The receipt is the answer, not a footnote.
That also means the result is consistent. Run the same analysis next quarter and you get the same computation, because the path is documented, not guessed.
What it is for
You own a function. You have to answer to someone above you. The strategic accountability work is demanding: performance by segment, period-over-period comparisons, attribution across channels, efficiency metrics by operating unit. These are not simple queries. They require business understanding, correct time math, and traceable computation.
Modern Gravity is built for those questions. It is not a dashboarding layer. It is not a query tool. It is an agent that produces answers a senior operator can defend, grounded in business context, with the receipt attached.
It is available today. If you are responsible for answering hard questions about a business you run, that is what it was built for.