Early Access — API v1.0

Reliable structured data from messy text.

Paste any log, config, or document.
Get validated structured output instantly.

Try Qrynt → Read the docs ↗
// What Qrynt returns

Messy text in. Typed structure out.

Qrynt reads your input, detects its structure, and returns typed segments you can connect directly to a database, an AI pipeline, or a review interface — without writing a parser.

// Input — raw text
# mixed content — chat export + explanation

Matrix row explanation
You said: In this matrix or 2D array
the first row is data of one object...

ChatGPT said: I see what you're describing
Let's clarify:

You have a 2D array (matrix).
Each row corresponds to one object.
Qrynt
// Output — typed segments
{
  "segment_id": "fc6cfd28...",
  "type": "prose",
  "content": {
    "text": "Each row corresponds
      to one object."
  },
  "confidence": 1,
  "flags": []
}
What each field means
segment A coherent chunk of your input, classified independently Learn more → confidence How certain the engine is about the classification (0–1) Learn more → type: prose Natural language — sentences, paragraphs, chat messages Learn more → type: kv_block Key-value pairs — config files, forms, metadata Learn more → MachineAdapter Flat JSON records — connect directly to your database Learn more → AIAdapter / LDM Logical Document Model — structured input for LLMs Learn more →
Read the full documentation →
// OUTPUT FORMATS

One input.
Three outputs.

Every processed document is immediately available in the format your system actually needs.

◧
Human
Formatted, readable output for operators, analysts, and CLI tools. Downloadable as plain text.
TXN_ID AMOUNT STATUS ────────── ─────── ────────── txn_9k2m4p 1249.50 AUTHORIZED txn_7h4j9p 450.00 PENDING banking.core: enabled swift: v2024 fraud.ml: v3.1
◨
Machine
Typed structured records for database ingestion, analytics pipelines, and ETL workflows.
[ { "type": "table", "content": { "headers": ["TXN_ID","AMOUNT"], "rows": [["txn_9k2m4p", 1249.50]] }, "confidence": 1.0 } ]
◩
AI / LDM
Token-efficient Logical Document Model for LLM context windows, RAG pipelines, and fine-tuning.
{ "blocks": [ { "type": "kv_block", "tags": ["KV", "STRUCTURED"], "label": "Config", "confidence": 0.98 } ], "is_valid": true }
// TRUST LAYER

Every output
is validated.

Qrynt exposes structural trust metrics most tools hide. Know exactly how reliable your output is before using it.

✓
Schema Compliance — structure matches declared type
✓
Line Conservation — zero data loss guaranteed
✓
Matrix Uniformity — table column integrity verified
✓
Conflict Rate — classifier agreement measured
1.00
Quality Score
schema_compliance PASS
matrix_uniformity PASS
line_conservation PASS
anomalies_detected 0
conflict_rate 0.00
// USE CASES

Built for teams
that work with real data.

Same pipeline. Different language for each team.

Developers
API-first integration
Three-stage pipeline. Idempotent sessions. SSE streaming. Webhook push. Clean JSON output. Integrate in minutes.
REST API SSE Webhooks JSON
Data Teams
Clean data, fast
Stop cleaning logs and config files manually. Qrynt extracts KV pairs, tables, and structure from any messy operational data.
Log processing ETL Analytics Reports
AI Teams
Better context windows
Pre-process documents before they reach your LLM. Validated, typed, token-efficient LDM output designed for RAG pipelines.
RAG Context prep Fine-tuning LLM pipelines
// PIPELINE

Three stages.
One clean output.

Ingest stores your raw input immutably. Resolve runs the full classification pipeline. Export delivers the format you need.

1
POST /ingest
Accepts raw text → returns session_id
idempotent
2
POST /resolve
Classifies, detects, validates structure
sync / async
3
POST /export
Returns output in your format
3 formats
Human
text/plain
Machine
application/json
AI / LDM
application/json

Start processing
data today.

Paste any messy text. See structured output in seconds. No signup required to try.

Try Qrynt → Get started free See pricing