Fleak joins the Databricks startup accelerator. See the announcement

Your AI is only as smart as your worst data source.

Fleak is the AI infrastructure layer between your data and your AI applications — normalized, filtered, governed, delivered in real time.

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Your data is failing your AI. Not because the models are weak. Because the data going in is inconsistent, noisy, and ungoverned.

Data overload and blind spots

Raw, unqualified data flows into your AI applications and data stores. Your models reason over everything equally — and get everything equally wrong.

Endless engineering upkeep

Every new source takes months to onboard. Every schema change breaks something. Your data engineering team never escapes maintenance mode.

Soaring costs, shrinking coverage

You can control costs or maintain coverage — raw, unqualified data forces you to choose. Most teams quietly choose costs, and quietly accept the blind spots that follow.

You decide what your data is worth. Fleak enforces it — at any scale.

Value-aware routing

Fleak's AI evaluates every incoming data point against what your downstream applications need — then routes it accordingly. What has real-time value moves fast. What has compliance value goes to long-term storage. What has no value goes nowhere.

AI-orchestrated, self-healing pipelines

Schema changed upstream? Fleak detects it, generates a new config, and redeploys — automatically. Human review optional.

Governed delivery to any destination

Fine-grained access control enforced at the data layer. Every transformation logged. Full audit trail included.

→ SOC 2 TYPE II · FULL AUDIT TRAIL

6mo → 1wk

Time to first source live

vs. traditional pipeline development

90%

Integration cost reduction

vs. traditional pipeline development

50%

Storage cost reduction

from deduplication at ingestion

40%

LLM token cost savings

from normalized, deduplicated inputs

Millions

Events per second, Real-time

zero storage required

3 Minutes

Avg. self-heal time

when schema drift is detected

The pipeline that understands what your data means.

Fleak understands intent — what each event type is, what each downstream application needs, and how to reshape data accordingly.

01 / Connect any source

Cloud, OT, endpoints, APIs, databases — if it emits data, Fleak connects to it. No custom connectors.

02 / Orchestrate with AI

Use natural language to describe what you want to accomplish. Fleak's copilot will build it for you in minutes.

03 / Transform your data

Identify event types, branches by destination intent, normalizes to the right schema. If your data changes, Fleak will update itself.

04 / Deliver anywhere

Clean, governed data in the schema you specify — to AI applications, data lakes, SIEM, or any destination. Real time. Zero storage.

Watch us fix your messiest data.

Book a 30 minute demo

The only data layer that fixes itself.

Schema changed upstream? Fleak detects it, sends an alert, and generates a new config. All you have to do is approve and redeploy. No manual work. No on-call incidents.

For the verticals that can't afford dirty data

Your LLM-powered SOC tools are only as good as the data feeding them.

Schema mismatches, duplicate events, and drift-broken pipelines mean your detection systems reason over noise — not threats.

  • Normalize any log source to OCSF, UDM, or CSF in real time
  • Filter and deduplicate before data reaches your SIEM or detection tools
  • Govern what each tool can see — zero leakage, full audit trail

Your OT data is too noisy and fragmented for predictive models to trust.

Inconsistent tag names, duplicate sensor readings, and protocol mismatches mean your analytics layer is guessing — not predicting.

  • Normalize OT and IoT telemetry to a unified schema in real time
  • Deduplicate and filter sensor noise before it reaches your data lake
  • Enforce access controls per data stream — full audit trail included

Your compliance and fraud systems are only as reliable as the data behind them.

Inconsistent formats, duplicate transactions, and ungoverned data flows mean your risk models operate on incomplete information.

  • Normalize transaction data across sources to a canonical schema
  • Deduplicate and enrich events before they reach fraud detection
  • Enforce fine-grained data governance with full regulatory audit trail

Bring us your messiest data.

30 minutes. Bring your messiest data source.

Book a 30 minute demo