Aetherix
The F&B execution agent for boutique hotels.
Tomorrow's covers, a staffing grid that fits, and less waste. One message a day, in WhatsApp, with the reasoning attached.
Aetherix is the food and beverage agent of a five-agent architecture. The architecture
- Phase 3 · Pilot partner in recruitment, go-live September
- Staging Live
- MCP-Native
- Claude Sonnet
- Python 3.12
- FastAPI
- Fly.io (Paris)
What it already does.
- States its drivers the evening before, then reports its own miss the next morning, in plain language.
- Flagged a corrupted POS export rather than training on it.
- Named its own drift after three consecutive misses, and recovered from a 25% regime shift through weekly recalibration.
- Stores every recommendation next to what actually happened, with the manager's override recorded beside it.
- Blocks its own merges on evaluation exit codes; every guardrail trip carries a machine-readable reason.
Sandbox data, real mechanics, deterministic reruns.
Target (Design): overrides recorded with the responder's role and reason, not a named person.
Three promises, one message a day.
Two of the three are pilot hypotheses, not measured results. They are the outcomes the live pilot is set up to test.
Tomorrow's covers
Running daily against real data. Operator delivery begins with the pilot. Anticipates how many breakfasts, lunches, dinners to expect, with a confidence score and the drivers behind it.
Staffing that fits
Proposes a shift grid based on occupancy, capture rate history and local context. The manager approves, adjusts or overrides.
Less waste
Spots over-ordering risk before purchase, with the historical signals that triggered the flag.
Understand, measure, learn.
Understand, like a human.
Contextual reasoning grounded in each property's history.
Measure, like a machine.
Every recommendation is stored next to its real outcome, so the system knows what it said and what happened.
Learn, like a network.
Per-property memory today, federated priors: Research, to give independents the power of a hive.
Five agents, honest status.
- AetherixThe F&B agent: forecast, staffing, memory.Built
- TacetThe environmental-signal agent.Built · maintenance mode since July 2026
- AnimaThe guest memory node.Synthetic PoC
- PeritiaThe trade agent: house knowledge, what the people who work here know.Design
- OrchestratorThe supervisory agent.Design · proto-stub
Where this sits
- AetherixYou are here
A food & beverage execution agent for hotels. It tells a manager how many covers to expect, how to staff for them, and how to cut food waste.
- For
- Other agents via MCP, or a hotel / F&B manager with WhatsApp.
- Status
- Built, 0 real users, forecast in shadow mode, pilot partner in recruitment.
Headless environmental twin: street, weather and crowd signals turned into yield rules, exposed via MCP.
- For
- RMS, PMS and orchestration agents, not humans.
- Status
- Built, maintenance mode since July 2026.
A voice AI mentor that captures, qualifies, and leverages the tacit expertise from senior to junior collaborators. Just ask.
- For
- A junior technician on the shop floor, hands busy.
- Status
- Prototype, synthetic data, harness in CI.
Guest memory node: it keeps what a guest said and what happened, so nobody has to ask twice, without watching.
- For
- The nodes that need to know who is arriving.
- Status
- Synthetic PoC, production gated behind a data-protection assessment.
Open-data SaaS mapping urban noise so cities can act on acoustic health.
- For
- European cities and metropolitan areas.
- Status
- Shipped 2020 to 2022, company closed.
Project Info
Role
Product Builder & AI Agent Designer (solo)
Duration
2026, ongoing (validation phase)
Team
Solo architect & builder
Status
Built · 0 real users · forecast in shadow mode · pilot partner in recruitment · Apaleo OAuth + MCP read operational
A three-minute walk-through of the loop: signal in, forecast, WhatsApp receipt, manager reply, memory update.
Your greatest operational asset walks out the door.
In hospitality, the people who know how the property really runs (which Tuesdays spike, how a local conference reshapes breakfast, how to staff a rainy Sunday) carry that knowledge in their heads. When a manager leaves, the property starts again from scratch.
Combined with the inherent complexity of operations (multiple outlets, mixed guest segments, volatile demand, labor shortages), this creates chronic inefficiencies:
- Persistent over or under-staffing
- Avoidable food waste
- Reactive decision-making instead of anticipation
- Slow onboarding and inconsistent execution across properties
Traditional systems store data. Very few build living operational memory that learns from real outcomes and makes that knowledge available at the exact moment it is needed.
This is the gap Aetherix was created to close.
The underlying bottleneck is not data, it is synthesis: combining internal context (occupancy, capture rates, POS history) with external signals (weather, local events) into a recommendation calibrated to this property.
Memory, Prediction, Action, Learning.
Memory is not the offer, it is the mechanism that makes the forecast specific to one property rather than generic. Every day, Aetherix runs the same loop and adds one more record: recommendation, manager reply, measured outcome. This is why the day-90 recommendation is expected to be sharper than the day-7 one.
Memory
Recall what worked here, on similar days, in similar contexts. Local history first, plus generic cross-property context patterns while local history is thin.
Day 7Cold-start. Confidence is honest about what is not yet known.
Day 90Expected: drivers become property-specific, calibrated by enough Tuesdays. This is what the pilot measures.
Prediction
A calibrated forecast for tomorrow: covers, staffing, waste risk, with the drivers behind it.
Day 7Baseline leans on generic patterns and first-week local signals.
Day 90Expected: tighter confidence intervals and fewer overrides, to be verified on live data.
Action
One message on WhatsApp. Reply accept, reject, or the right figure. No new dashboard to learn.
Day 7The receipt is verbose, spells out every assumption.
Day 90Intended: compact. The manager reads a paragraph, replies in a word.
Learning
An exact accept or reject, or a corrected figure, is recorded as feedback; free-text replies are not yet mapped. The pilot tests whether feedback improves future recommendations.
Day 7Every recorded accept, reject or correction deposits one grain of memory.
Day 90Hypothesis under test: the system reflects how this property behaves, not just how hotels behave.
Caveat: what is recallable today is local history plus generic cross-property context patterns. A true federated cohort prior (HiveMemory) is research, targeted for Phase 4 and beyond, and would remain anonymized aggregates only. No cross-tenant raw data, ever. Measured gains begin with the live pilot, not the synthetic baseline.
What a manager actually sees.
Aetherix
today · 18:42
Breakfast 84 covers · confidence 88% (illustrative)
72 rooms booked + 12 typical Tue walk-ins
Staff: 4 FOH (you ran 3 last Tue → 1 understaffing flag)
Driver local conference check-ins + clear weather
Last week forecast 79 · actual 81 · MAPE 2.5%One reply: accept, reject, or the right figure. That reply is the signal Aetherix learns from. The pilot tests whether the day-90 receipt is sharper than the day-7 one.
- Forecast
- 74
- Actual
- 71
- Variance
- -3 · 4%
DecisionPrep cut on two slow-moving dishes, one runner moved to the later shift.
ReadingForecast held. Waste stayed inside the usual range, no manual override needed.
Illustrative figures from shadow-mode runs, not a signed pilot
Aetherix executes. It does not orchestrate.
Aetherix is one node in a network of specialized, -native agents. Each node owns a strict bounded context and communicates only through MCP, no shared code, no monolith to displace. Target: the Orchestrator (Design) decides when to call it. Until it is built, Tacet's output reaches Aetherix directly: the one documented exception, non-personal, read-only, logged.
Tacet
Sensory
Orchestrator
Supervisory
Anima
Guest memory
Aetherix
F&B execution
this nodeHiveMemory
Anonymized peers
Manager
Tacet
Sensory
Orchestrator
Supervisory
Anima
Guest memory
Aetherix
F&B execution
this nodeHiveMemory
Anonymized peers, side input
Manager
Aetherix executes. It does not orchestrate.
Target: the Orchestrator (Design) calls both. Until it is built, Tacet's output reaches Aetherix directly: the one documented exception, non-personal, read-only, logged.
| Node | Role | Status |
|---|---|---|
| Aetherix | F&B execution: forecast, staffing, memory, receipts | Live (staging) |
| Tacet | Sensory: weather, noise, open data → risk | Built · maintenance mode since July 2026 |
| Peritia | House knowledge: what the people who work in a property know | Design |
| Orchestrator | Supervisory: MCP routing, business rules. Proto-stub in Phase 3, dedicated repo in Phase 4. | Design · proto-stub |
| Anima | Guest memory node | Synthetic PoC, hackathon prototype; production DPIA-gated |
Push-first pipeline.
Apaleo (OAuth2 and MCP read) + Open-Meteo + PredictHQ → Prophet forecast and staffing → pgvector memory recall (OperationalMemory + HiveMemory cold-start, research) → Claude Sonnet reasoning → WhatsApp or Email.
Apaleo PMS
OAuth, MCP read
Weather
Open Meteo
Events
PredictHQ
Social sentiment
Web, reviews
PMS Sync
Capture rate
Prophet
Forecast & staffing
Claude Sonnet
Reasoning, fallback Gemini/GPT
Explainability
Why? service
Supabase
Operational data
pgvector
Per-hotel memory
HiveMemory
Anonymized peers
LLMProvider
Claude Sonnet · Gemini · GPT
MCP server
Three atomic tools, multi-tenant
Numerical · Semantic · Cognitive · Reasoning · Action
Stack
- FastAPI
- Python 3.12
- Prophet
- pgvector (Mistral 1024d, HNSW)
- Claude Sonnet (Gemini/GPT fallback)
- Redis (Upstash)
- Supabase (PostgreSQL, RLS, EU)
- Fly.io (Paris)
Trust is engineered, not promised.
Evaluation as a contract
Qonto-inspired. Versioned golden datasets. Any change to forecasting, providers, memory or prompts needs a green eval pipeline to merge. A hard CI gate, not a habit.
Observability & metering
Every MCP tool call is one structured event. Auditable, queryable, priceable. The product is operable, not opaque.
Multi-tenant security
hotel_id scoped at every layer (RLS, Python guards, MCP token binding), resolved server-side, never a tool input. Read-only PMS access. GDPR: Supabase EU, HiveMemory is anonymized aggregates only.
Three artifacts, three proofs.
What remains private
The project includes proprietary research on cross-domain operational context and cold-start learning. Public materials focus on the engineering evidence: evaluation gates, traceability, safety guardrails, privacy boundaries, and outcome measurement.
- Evaluation gates
- Traceability
- Safety guardrails
- Privacy boundaries
- Outcome measurement
Why Lore cannot close the same loop
Lore, in aviation maintenance, shares this architecture and cannot close the same loop. Nothing there tells the system whether an observation was right, and the moment a technician escalates the counterfactual disappears. Aetherix has an outcome to compare against; that difference decides how much each product is allowed to learn on its own.
The evaluation vocabulary behind these gates, harnesses, graders, acceptance tiers and closed loops, is written up in a field note: Harnais, graders, boucles fermées (in French).
Where we are, honestly.
No live forecasting accuracy claim, yet. Phase status, target outcomes, and the security gate baseline. Every claim carries its caveat: synthetic, target, or measured.
Where we are, where we're going.
- Phase 0
Validation
- Phase 1
Observability
- Phase 2
Receipts
- Phase 3
Pilot
Pilot design partner in recruitment. Go-live targeted end of September.
Current - Phase 4
Scale
Mews · POS · Federated HiveMemory · Context Graph · Anima
- Phase 5
Vision
Revenue Orchestrator
Anima. Guest memory, four layers deep.
Where Aetherix remembers the property, Anima remembers the guest. A synthetic proof of concept built at the Anthropic hackathon (June 2026): a four-layer temporal memory with confidence-weighted claims, run on synthetic data. Production is Phase 4, DPIA-gated.
- Layer 01
Working
The current stay, in progress. Expires at check-out.
- Layer 02
Episodic
This stay and a short tail after it. Weeks.
- Layer 03
Semantic
Durable preferences, stated or repeatedly confirmed. Long, with decay.
- Layer 04
Segment
Anonymised patterns, what guests like this tend to need. Aggregate, never individual.
Solo architect & builder. Results-as-a-Service.
Product Builder & AI Agent Designer, 2026, ongoing (validation phase). Pricing follows outcomes, not seats: we win when you win. Pilots and product conversations open.