DataGrout
The Unified AI Operating Layer for Agentic Intelligence
110 followers
The Unified AI Operating Layer for Agentic Intelligence
110 followers
DataGrout gives your AI agents memory, reliability, and cost control. Most agent frameworks fall apart at scale, context windows explode, workflows fail silently, and token costs spiral before you notice. DataGrout fixes the plumbing: connect to any system, remember across sessions, and run continuously without burning your budget. Built for teams shipping production agents, not just demos.
This is the 5th launch from DataGrout . View more
DataGrout Math
Launched this week
Deterministic numeric tools for AI agents. Generate sequences, compute statistics, fit regressions, detect outliers, and normalize data, all in-process, no LLM, no credits. Every result is reproducible and verifiable. Works with any MCP-compatible agent.
Free Options
Launch Team
Hey Product Hunt 👋,
We built DataGrout Math because we kept running into the same problem. When an agent pulls a large dataset from an integration, the usual next step is sending it back through LLM context so the model can operate on it. That is expensive and unnecessary.
DataGrout Math tools accept a cache_ref from any previous tool call. The array never touches the LLM context. Math runs in-process against cached data, only the result comes back. You get outlier detection, regression, normalization, and stats over large datasets without the token cost of shuttling data back and forth.
Results return as both a values array and a records array. Records pipe directly into our chart tool. Values pipe into the next math tool. No reshaping between steps. Every result is deterministic with a verifiable receipt. Same inputs, same output, every time.
Not conceptually bad. Just unreliable in practice. They drift on floating point arithmetic, hallucinate statistics, and spend tokens doing work that should cost nothing. When you are running production agents that make decisions based on numeric data, "close enough" is not acceptable.
So we built a set of deterministic math tools that plug directly into any MCP-compatible agent: Claude, Cursor, any framework using the Model Context Protocol.
Here is what is in it:
- `math.range` and `math.linspace` for clean sequence generation without floating point drift
- `math.sequence` for named series: Fibonacci, primes, geometric, triangular, squares, powers
- `math.sample` with seeded reproducibility across normal, uniform, and exponential distributions
- `math.interpolate` with 12 easings + 6 modes (lerp, inverse_lerp, clamp, remap, smoothstep, smootherstep) = 18 modes
- `math.describe` for full descriptive stats with percentiles and a histogram
- `math.window` for moving averages, EWMA, cumulative sums, diffs, and lag
- `math.normalize` with z-score, min-max, and percentile rank
- `math.outliers` using IQR or z-score, with clean arrays and boolean masks included
- `math.rank` with ordinal, dense, average, and percentile methods
- `math.correlate` for Pearson and Spearman correlation with interpretation labels
- `math.trend` for regression fitting and forward forecasting
Zero credits. Zero LLM involvement. Every result is deterministic and verified with a receipt.
If you are building AI agents and you want reliable numerics, give this a try and let us know what you think. We read every comment.