Archon Studios
ARCHON

Independent AI Studio The next is built

ARCHON

We design and build the models, agents, tools, and architecture that turn ambitious ideas into working systems.

Intelligence, by design.Scroll

01 The studio

Possibility is easy to imagine. Progress takes craft.

We bring strategy, engineering, and imagination together to make AI useful where it counts. From the first question to a working system, we build with purpose.

Explore our expertise
  • Independent by nature.
  • Intelligence, by design.
  • Building what's next.

Blueprint to stone, as you scroll.

02 Capabilities

What we build.

Ideas become powerful when the thinking and the technology move together.

Useful products shaped around how people actually work.

Connected systems that reason, act, and support real workflows.

Strong foundations for reliable AI, including locally hosted systems.

Clarity on where AI can make a meaningful difference.

Diagram · illustrative A tool panel, organised around the work.

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03 Custom intelligence

CUSTOM AI. ANY INDUSTRY.

Built around your world

We design, train, and deploy custom models and agents around your data, workflows, and goals—wherever your industry operates.

  1. 01 / DESIGN

    Define the right system.

    Shape the model or agent around the problem worth solving.

  2. 02 / TRAIN

    Make it yours.

    Adapt intelligence to your domain, data, and standards.

  3. 03 / DEPLOY

    Put it to work.

    Bring it into the workflows where it can make a difference.

Try it: shape a curve, train a small model on it, deploy it.

Illustrative demo · a toy model trained in this tab. Not an Archon product.

Step
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Loss (MSE)
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Parameters
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Compute: this tab · main thread · at most 2.5 ms per frame

Your design, drawn as a blueprint arch.

Local deployment / Private control

Your AI. In your environment.

We also build locally hosted models, agents, and systems that run on your own infrastructure.

The curve you shaped and the model you trained stay in this browser tab. This page doesn't send them anywhere.

A boundary you can push. The model inside stays put. A visual metaphor.

04 Point of view

MAKE WHAT'S NEXT REAL.

From a question worth asking to an AI system worth using. That's the work.

05 Our first product

Socrates

Evidence-grounded, teacher-approved learning intelligence.

Status Launching soon

From evidence to outcome, with the teacher deciding.

  1. objective
  2. evidence
  3. cited diagnosis
  4. teacher decision
  5. intervention
  6. activity
  7. reassessment
  8. outcome
  9. learning history, the Student Intelligence Card

cited diagnosis: A draft finding that cites its evidence.

Student Intelligence Card · teacher reviewIllustrative example · synthetic data

1 · Evidence Objective: Compare fractions with unlike denominators

    1. E1 · Exit ticket · rec_ex_014 [STUDENT-A] wrote 1/4 > 1/3 because 4 is bigger than 3.
    2. E2 · Homework, question 3 · rec_ex_022 [STUDENT-A] ordered 1/6, 1/3, 1/2 from largest to smallest.
    3. E3 · Class discussion note · rec_ex_031 [STUDENT-A] said a fraction with a bigger bottom number is bigger.

    Masking runs in your browser before the draft step. Only the processed text is used.

    2 · Draft diagnosis DRAFT · not a conclusion

    Compares fractions by the size of the denominator, treating a bigger denominator as a bigger fraction. [E1] [E2] [E3]

    Pre-written example draft.

    3 · Teacher decision

    4 · Student Intelligence Card · [STUDENT-A]

      1. · Objective set: compare fractions with unlike denominators.
      2. · Approved: finds equivalent fractions using a fraction wall. Cites rec_ex_009.
      Record entries
      2
      Record checksum
      —

      Teachers decide

      Only teacher-approved diagnoses become conclusions. Drafts and rejected suggestions never change a student's record.

      Every claim is cited

      Each approved claim links to a short, redacted citation and its source record.

      Private by design

      Student names are masked before evidence is processed; raw evidence and prompts stay out of operational logs.

      The whole class in view

      Class-level priorities, plus year and weekly plans, sit alongside each student's history.

      06 The next chapter

      Have a challenge worth solving? Let's build the answer.

      Sketch your system first

      Describe a workflow you'd like AI to help with. The field draws a first sketch of it.

      Example sketch · built from the blocks above

      Data to reviewed result

      Your data feeds a custom model; a person reviews the result before it is used.

      Where it runs: not stated

      Questions to bring to a first conversation

      1. What data do you already have, and who owns it?
      2. What should the model do well that general models don't?
      3. Who reviews the output, and what happens when they disagree?

      Write to Archon

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