Know what's reaching your models.

We're building FlowSense.AI to run on the GPU servers and Mac Studios that serve your models. It shows which apps and agents call a model, how fast the first token comes back and what's competing for the link.

Start a conversationHow it works

Use cases

What a connection can tell you.

Eight questions we're building toward, from the machines that serve AI models to homes and shared networks. None of it is released yet.

AI Inference

Why is the local model slow to answer?

A Mac Studio or GPU server serving a model shares its network with everything else. FlowSense.AI shows which apps and agents call the model, how fast the first token comes back and what competes with it.

Example command: flowsense infer mac-studio --last 15m

CLIENT      ENDPOINT          REQS  TTFT
ide-agent   chat/completions   412  180ms
notes-app   api/generate        96  240ms
eval-job    embeddings        2210   40ms
pull        model 18.4 GB  link 91% busy
ttft p95    1.9 s during pull (usual 0.3 s)
try         schedule model pulls off-hours

Deep Packet Intelligence

One engine, from consumer to enterprise.

FlowSense.AI is our Deep Packet Intelligence engine. It recognizes applications from what traffic reveals, without decrypting it, and it's built to run wherever a network needs explaining.

  1. Homes

    Simple enough for anyone in the house.

  2. Businesses

    Offices, schools and shops sharing one connection.

  3. AI infrastructure

    The servers and workstations that run models.

  4. Enterprises and operators

    Large networks and the providers behind them.

A machine serving a model, as FlowSense.AI sees it: each application by name, with its rates and trend.

Technology

From traffic to a next step.

The questions differ. Underneath them, FlowSense.AI's Deep Packet Intelligence works in the same three steps, starting from traffic like this.

  1. 01

    Recognize the application.

    FlowSense.AI combines custom-trained ML models, behavioral heuristics and signatures. Protocol parsers read what TLS, QUIC, HTTP and DNS leave visible, so encrypted traffic is classified without being decrypted.

  2. 02

    Understand the experience.

    Recognizing a call or a model request is one question. Knowing whether it is working well is another. Experience models score delay, throughput and time to first response, and how they change over time.

  3. 03

    Suggest a next step.

    Anomaly models flag what changed. FlowSense.AI puts the evidence together into a likely cause and something to try. The decision stays with you.

About

Inspection without surveillance.

Useful context comes from how traffic moves. What it carries stays yours.

What we look at

  • Packet sizes and timing
  • Direction and flow behavior
  • The services a device reaches

What stays private

  • The words in a message
  • The picture in a call
  • The prompts you send a model

Where it runs

We're building toward processing on hardware you own, from the machine that serves your models to the router in your network.

Plain explanations, controls you set yourself and a smaller public footprint.

Quietly building.

ArkaType is building agentic cyber intelligence. FlowSense.AI recognizes applications from what traffic reveals, without decrypting it. On-device models act on what it finds.

Nothing here is for sale yet. If one of these questions is yours, we'd like to hear how you'd ask it.

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arkatype

Company brief

Know what's reaching
your models.

ArkaType Intelligence Inc. is building FlowSense.AI, a Deep Packet Intelligence engine that recognizes applications from what traffic reveals, without decrypting it. It is built to run from consumer to enterprise networks, and on the GPU servers and Mac Studios that serve AI models.

What a connection can tell you

Why is the local model slow to answer?

A Mac Studio or GPU server serving a model shares its network with everything else. FlowSense.AI shows which apps and agents call the model, how fast the first token comes back and what competes with it.

Why does this call keep breaking up?

A fast connection can still make a poor call. Delay, loss and competing traffic explain more than a speed test does.

What's slowing down the shared Wi-Fi?

Video calls, updates and streaming compete for one connection. Seeing who uses what shows where the bandwidth goes.

What changed on this device?

An unfamiliar destination or a new burst of activity is a reason to look closer. It is not proof of a threat.

Where did the evening go?

Application usage is easier to act on when you can see its rhythm across an evening.

What makes room for family time?

Homework, bedtime and time together need different boundaries, set per application by the family.

Which services does this device reach?

A device's connections show which services it depends on, including some you did not expect.

What are the quiet devices doing?

TVs, cameras and speakers keep working in the background. Their traffic shows what they are doing.

One engine, from consumer to enterprise

Homes: simple enough for anyone in the house. Businesses: offices, schools and shops sharing one connection. AI infrastructure: the servers and workstations that run models. Enterprises and operators: large networks and the providers behind them.

From traffic to a next step

Recognize. FlowSense.AI combines custom-trained ML models, behavioral heuristics and signatures; protocol parsers read what TLS, QUIC, HTTP and DNS leave visible, so encrypted traffic is classified without being decrypted. Understand. Experience models score delay, throughput and time to first response, and how they change over time. Suggest. Anomaly models flag what changed, and FlowSense.AI puts the evidence together into a likely cause and something to try. The decision stays with the user.

Inspection without surveillance

FlowSense.AI looks at packet sizes and timing, direction and flow behavior, and the services a device reaches. The words in a message, the picture in a call and the prompts sent to a model stay private. We're building toward processing on hardware you own, from the machine that serves your models to the router in your network.

Quietly building.

Nothing here is for sale yet. This brief describes our direction. If one of these questions is yours, we'd like to hear how you'd ask it.