Fleak joins the Databricks startup accelerator. See the announcement

Your AI agent is doing data engineering in its context window.

40% of your token budget disappears on parsing. Your agent never gets to spend it on thinking.

Book a demo See how it works
  • IoT & OT telemetry
  • Industrial sensor data
  • Security & device logs
  • No model changes
  • SOC 2 Type II

40%

LLM token reduction

upstream normalization eliminates the parsing tax — same model, sharper reasoning

0

Model changes required

same LLM, same prompt — different data quality, different results

< 5 min

New data source onboarded

AI-generated parsers for any schema — seen or unseen — in minutes, not weeks

Every token your agent spends parsing
is a token it can't spend reasoning.

Structured data agents hit an intelligence ceiling. Bad data is always the reason.

40% tax. Every query. Before your agent thinks once.

Mismatched fields, vendor schemas, cryptic codes. Your LLM decodes structure before it reads meaning. That's your token budget doing data engineering.

AI gateway logs: the fastest-growing structured data problem nobody's solved

Every LLM API call generates structured JSON. Bedrock, Vertex, Azure OpenAI — all different formats, all growing. Dump it in your SIEM and watch the budget disappear. Ignore it and accept the blind spot.

A misread sensor in a SCADA system is a missed alarm.

Industrial and OT agents can't afford to infer. Ambiguous data forces guessing. In a power grid, a pipeline, or a flight system — guessing has consequences.

Your LLM is a reasoning engine. Stop making it a data janitor.

Normalize before the model, not inside it. Every token arrives with meaning already attached. Same model. Same prompt. The ceiling disappears.

Read the docs Book a demo

The token budget
your agent deserves.

Fleak sits between your data and your agent. It normalizes structured machine data — OT telemetry, IoT events, device logs, AI gateway activity — into a clean, semantically consistent schema. Your agent receives data that already means something. No disambiguation. Straight to reasoning.

normalized to any target schema

  • OCSF
  • IEC 61968
  • OPC-UA
  • DICOM
  • UDM
  • TIA
  • Custom schema

Same agent. Same query.
One variable changed: did the data arrive clean?

Raw data

Token-heavy parsing

Agent receives ts:1714123456, val:87.3, src:PLC_04B, st:2. Must infer: is this Celsius? What is status 2? What does PLC_04B map to in this facility? Context window fills with disambiguation attempts.

After Fleak

Pure analysis

Agent receives structured JSON: device.name: "Compressor Unit 4B", metric: "temperature_celsius": 87.3, status: "warning_threshold_exceeded". Zero disambiguation. Immediate anomaly reasoning.

Same model. Same prompt. Different data quality. Different results.

"Feeding Fleak-normalized data to our AI engine allowed the model to bypass the parsing phase and move immediately to high-fidelity analysis — achieving Tier 3 threat hunter performance without increasing token costs or latency."

Arif Shaikh, Head of AI Innovations · Gruve.ai

Stop paying for data engineering inside your LLM.

30 minutes. Bring your messiest data source and your current agent architecture.

Book a demo

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Self-heals in-stream — zero manual intervention

Your pipelines didn't fail. They just went quietly wrong.

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