Platform

One Company Brain and one sovereign inference core under every product.

Data and compute under Greek and European control.

Five layers, each one measured. Products are built on top, so an improvement in the core improves every call, every booking and every answer.

Architecture

Five layers, top to bottom

  1. Layer 5

    Factory

    Turns a request or a “hire” into an App Manifest (entities, workflows, skills, channels, policies and connectors) and provisions it. Publishing is provisioning.

    App ManifestWorkflow engineSigned skill packs
  2. Layer 4

    Channels

    Voice, WhatsApp, web apps and telephony (SIP) all reach the same Brain, so a booking made by phone is visible in chat.

    VoiceWhatsAppWebSIP
  3. Layer 3

    Skills & Agents registry

    An employee is a role plus skills plus policies. Each skill is a typed contract with deterministic code, tests and applicability conditions, signed and evaluated with and without the skill.

    Typed contractsTestsSigningPaired evaluation
  4. Layer 2

    Company Brain

    Your business as typed entities, relations and rules in a bitemporal store. A constraint gate checks every proposed action, and every decision is written to an append-only, hash-linked ledger. The model proposes; the rules decide.

    Ontology templatesBitemporal storeConstraint gateAudit trail
  5. Layer 1

    Sovereign Inference Core

    Open-weight models on vLLM with FP8 and prefix caching, on GPUs in Greek data centres. Confidence-based routing between models; every failed trace becomes a skill fix or a fine-tuning example.

    vLLM FP8Greek fine-tuned modelsRoutingAI Act dossiers
Data residency

The data-residency rule

  • No US API in the serving path
  • Frontier models only as judges and teachers, on data cleared for that purpose
  • Benchmarks on rented GPUs use non-customer data only

The rule applies to every product served from the Hellas Inference core. AI Employee runs today on EU cloud with third-party model APIs and moves onto the Greek core in 2027; the rule applies to it from that migration.

Customer data and all serving stay in Greek data centres. US APIs are never in the serving path. Frontier models are used only as judges and teachers in evaluation.

The Hellas Inference data-residency rule
Models

Four models, each named and measured

Each is an open-weight base model with our own fine-tuned adapter. Every one ships with an AI Act (Regulation (EU) 2024/1689) dossier and a model card.

Model roles. Iris: the instant model, for Greek contact-centre text and call summaries. Metis: the thinking model, for document Q&A and agents that call tools. Clio: speech to text. Calliope: voice. Each is an open-weight base model with our own fine-tuned adapter; the base is named on every card. Llama and Qwen stay available as routing alternatives.

Instant LLM

Iris 8B

Named after the swift messenger. Greek contact-centre text and call summaries, answered at once.

Parameters
8.2 billion
Base model
Llama-Krikri-8B-Instruct (ILSP / Athena RC). Built with Llama.
Adapter
LoRA, rank 16, merged into the weights
Tuned on
1,765 Greek contact-centre call summaries
Served
FP8 · vLLM · prefix caching
  • 200concurrent summaries per H200 within 2 seconds
  • −45%latency at 200 concurrent requests, from prompt order
  • ≈ 1/30the cost per call of a frontier API
Thinking LLM

Metis 12B · 26B

Named after the goddess of counsel. Document Q&A on 12B; agents that understand the caller and call tools on 26B.

Parameters
12 billion · 25.8 billion (4 billion active per token)
Base model
Gemma 4 12B · Gemma 4 26B-A4B (Google)
Adapter
LoRA, rank 16, 18.6M parameters, for language understanding (26B)
Tuned on
9,444 Greek dialogue turns, real and generated
Served
FP8 · 26 GB (26B) · vLLM · 32k context
  • 4.41 / 5answer quality on Greek enterprise documents (12B)
  • 131 / 190booking and service tasks completed correctly (26B)
  • 88.8%of spoken turns understood in full, up from 85.6% before the adapter
Speech to text

Clio 1.5B

Named after the muse who records what was said. Streaming Greek transcription for telephone audio.

Parameters
1.55 billion
Base model
Whisper large-v3 (OpenAI)
Adapter
LoRA, rank 32, merged into the weights
Tuned on
Greek telephone audio, including human-reviewed real calls
Served
fp16 · 3.4 GB · streaming with partial transcripts
  • 10.2%word error rate on held-out Greek audio; the base model scores 17.2%
  • −43%word errors on real 8 kHz phone calls after tuning on real callers
  • 32streams per GPU with batching
Voice · text to speech

Calliope 3B

Named after the muse with the beautiful voice. One consistent Greek voice for every call.

Parameters
3 billion
Base model
Orpheus 3B (Llama 3.2 backbone, 24 kHz codec). Built with Llama.
Adapter
LoRA, rank 32, 48.6M parameters, merged into the weights
Tuned on
15 hours of one professional Greek voice
Served
fp16 · 6.2 GB · vLLM
  • 0.19 sto first audio, median
  • 4.3×faster than real time when streaming
  • 423*concurrent streams per H200

* Measured on the Greek base model before the voice adapter was merged. A merged adapter does not change the size of the model.

Llama · Qwen

Routing alternatives

Other open models, routed in where a task or a licence favours them.

Serving

vLLM FP8

FP8 with prefix caching. Never INT4 on Hopper GPUs: we measured it 8× slower.

For developers

Call our models from your own code

An OpenAI-compatible API on GPUs in Greek data centres. Price your workload next to frontier APIs, then create an early-access account. An administrator reviews every account and grants access.

Providers

Where it runs

Training and serving are kept separate, and every serving site is in Greece.

What runs where today: Contact-centre workloads on dedicated capacity in Athens data centres. AI Employee on EU cloud until its 2027 migration to our Greek core. Training and evaluation at Greek sites and through PHAROS AI-Factory access; benchmarks on rented GPUs use non-customer data only.

  • Dedicated GPU capacity in Athens

    Dedicated GPU capacity in Athens data centres operated by Greek providers.

    Production serving, contracted per customer
  • PHAROS AI Factory / DAEDALUS (GRNET, EuroHPC)

    Greece's national AI factory at Lavrion, for fine-tuning and large evaluation runs through AI-Factory access.

    Training and evaluation · subject to access grant
  • Owned H200 nodes

    Owned H200 nodes in Athens colocation as capacity insurance.

    Planned 2027

Start with one workload.

Tell us the calls, documents or questions you want handled. We measure it on your data before you commit.