Transform Any Data,
Between Any System
AI agents shouldn't fail because two systems speak different data formats. DataGrout's data wrangling suite — Prism.refract, Data, Frame, and SemIO Types — gives agents dynamic, reliable transformation between incompatible structures at every step.
Free to start · No credit card required
Data wrangling is where agent pipelines break
Integrating disparate data sources requires dynamic transformation, cleaning, and restructuring — often between formats that were never designed to be compatible.
Incompatible data formats
CRM outputs nested JSON with camelCase keys. Your marketing tool expects flat CSV with snake_case. Hard-coded transforms break every time either system updates.
LLMs hallucinate transformations
Asking an LLM to reshape a 500-row dataset burns tokens, produces inconsistent results, and occasionally invents fields that don't exist.
Type mismatches silently corrupt pipelines
A CRM 'lead' object and a billing 'customer' object represent the same person — but an agent that doesn't understand semantic types passes the wrong structure and the workflow fails silently.
Tabular data is treated as text
Filtering, joining, and pivoting records through an LLM is slow, expensive, and non-deterministic. A spreadsheet operation shouldn't require an AI inference call.
No reuse across pipelines
Every integration builds its own one-off transformation logic. There's no shared, validated layer that agents can trust to handle data shaping consistently.
Every reshape costs tokens
Without a deterministic layer, agents route all data manipulation through the LLM — accumulating token costs on operations that should be free.
Six tools. Every data wrangling scenario covered.
From natural language transformation to deterministic JSON ops to semantic type bridging — DataGrout gives agents a complete, composable data preparation layer.
Prism.refract
Natural Language TransformationDescribe a data transformation in plain English — Prism generates sandboxed, verified executable code and runs it. Self-healing retries fix edge cases automatically. After the first call, subsequent identical transformations skip AI entirely and run deterministically from cache.
- →Natural language transform descriptions
- →Self-healing retries on edge case failures
- →Cached transforms run deterministically after first call
- →Works inline on any tool call via refract parameter
Data Tools
Deterministic JSON Manipulation15 precise, zero-credit tools for manipulating any JSON structure. Filter, sort, merge, flatten, deduplicate, fan-out — all in-process with no LLM, no external calls, and no hallucination risk. Every operation is bit-for-bit deterministic.
- →data.filter — declarative predicates (eq, gt, contains, in, is_null)
- →data.merge — shallow or deep map merging
- →data.flatten — nested maps to dot-path keys
- →data.map — fan-out: call any MCP tool once per list item
Frame Tools
Columnar & Tabular OperationsSpreadsheet-grade operations on arrays of records. Select, filter, sort, group, pivot, join, and slice columnar data without ever touching an LLM. Think of Frame as deterministic SQL-level power for any tabular payload your agent handles.
- →frame.join — inner, left, right, outer joins on shared keys
- →frame.pivot — reshape long data to wide format
- →frame.group — group-by with sum, mean, count, min, max
- →frame.filter — row-level filtering with 12 operators
SemIO Types
Semantic Type BridgingDataGrout's semantic type system enables type-safe planning and multi-hop type bridging across incompatible systems. When a CRM 'lead' needs to become a billing 'customer', SemIO Types finds the adapter chain automatically — and verifies it before execution.
- →Semantic type matching across integrations
- →Multi-hop adapter chains (lead → contact → customer)
- →Type compatibility checked before workflow execution
- →Used by Discovery.plan for verified, type-safe pipelines
Math Tools
Deterministic Numeric OperationsDescriptive statistics, outlier detection, trend fitting, correlation matrices, and sampling — all without LLM involvement. math.describe gives mean, median, and percentiles on 50,000 rows in one call. math.outliers returns exact anomaly indices with IQR or z-score bounds. Results are bit-for-bit reproducible.
- →math.describe — full descriptive stats in one call
- →math.outliers — anomaly detection with IQR / z-score
- →math.trend — linear/polynomial regression with r-squared
- →math.correlate — Pearson and Spearman matrix across all fields
Deliverables
Persistent Artifact StorageRegister any data wrangling output — transformed dataset, aggregation result, joined table — as a persistent deliverable with a semantic summary and tags. Future agents query with natural language to find prior work rather than re-running expensive transforms. Deliverables survive cache TTL and session expiry.
- →deliverables.register — store any cache_ref permanently
- →deliverables.list — semantic search over prior outputs
- →deliverables.get — retrieve full payload by ref
- →Tags, run_id, and agent provenance built in
CRM data → Marketing automation, reliably
An AI agent retrieves customer records from a CRM and needs to pass them to a marketing tool with an entirely different data structure. Here's how DataGrout handles it end-to-end.
Agent retrieves customer data from CRM
DiscoveryReturns nested JSON with CRM-native field names and types — incompatible with the downstream marketing tool.
Type bridging: crm.lead → marketing.contact
SemIO TypesDiscovery.plan uses SemIO Types to verify the type adapter chain exists before executing. Incompatibility caught at plan time, not runtime.
Deterministic reshape & field normalization
Data + Framedata.pick removes unneeded keys. data.flatten unwraps nested objects. frame.select renames columns. Zero tokens consumed — all operations are free.
Natural language final transformation
Prism.refract"Map email_address to contact_email and format phone as E.164." First call generates and caches verified code. Every subsequent run is fully deterministic.
Deliver to marketing automation tool
FlowThe transformed payload is handed off. The full pipeline is saved as a reusable skill — next run skips planning entirely.
Why developers choose DataGrout for data wrangling
Reliable, cost-efficient transformation that gets better over time.
Transforms run deterministically after the first call
Prism.refract generates and caches verified executable code on the first call. Every subsequent run skips AI entirely — same result, zero inference cost.
Type safety at plan time, not runtime
SemIO Types verifies adapter chains before any tool executes. Incompatible formats are caught during planning — not mid-pipeline when data is already corrupted.
Data and Frame tools are completely free
Filtering, sorting, merging, joining, pivoting — gateway costs waived entirely. Agents can reshape data thousands of times per hour without burning credits.
Self-healing on edge cases
When a Prism transform fails on an unexpected input shape, it auto-corrects and retries. The corrected version is cached — the same edge case never fails twice.
cache_ref chaining keeps data server-side
Pipe Data → Frame → Prism transformations without sending the full payload to your LLM. Data stays encrypted server-side; the agent only sees the final result.
Composes across your entire integration mesh
Every wrangling tool accepts cache_ref input from any upstream integration — Salesforce, QuickBooks, custom MCP servers. One consistent transformation layer for all sources.
Data wrangling across every industry
Wherever agents move data between systems, DataGrout's transformation layer handles the incompatibilities.
Sync leads between CRM and marketing automation
Automatically reshape CRM lead objects into the contact format required by the email platform. SemIO Types validates the adapter chain; Prism.refract handles field-level remapping in natural language.
Normalize transaction records from multiple banks
Frame joins and pivots transaction tables from different providers. Data flattens nested account hierarchies. Prism.refract standardizes date formats and currency fields across all sources.
Prepare order data for reporting dashboards
Frame groups and aggregates order records by region, SKU, and period. Data filters and sorts for top-N analysis. Prism.render generates a structured markdown report ready for delivery.
Bridge patient records between incompatible EHR systems
SemIO Types bridges patient entity schemas between systems. Prism.refract maps field names and coerces data types. Flow enforces a human approval gate before any write operation.
Frequently asked questions
Ready to wrangle your agent's data pipelines?
Connect your integrations, describe your transformations, and let DataGrout handle format bridging, type safety, and deterministic reshaping — automatically.
Get StartedFree to start · No credit card required