Under every agent, a platform that learns, remembers and improves.
Alice is the first visible agent. Underneath her is the BlueSky platform: stateful, closed-loop, auto-coaching. Every property of the agents we sell is born here — and every new agent we build inherits it for free.
Stateful by design. All our agents have memory.
Most AI platforms on the market are stateless: every conversation starts from scratch. Ours is designed stateful from day one.
Most AI platforms on the market are stateless. Technically simpler, commercially cheaper — but fundamentally limited. That's why chatbots disappoint after the first 5 minutes: they don't recognize you, don't remember what you said last week, repeat the same mistake endlessly.
The BlueSky platform is stateful by design. Every agent running on it inherits the same property: persistent memory per customer, context that accumulates over time, the ability to resume conversations interrupted weeks earlier. It's not an Alice feature — it's a platform architecture.
It doesn't just mean "I remember what you said." It means every interaction builds on the archive of all the previous ones, every decision takes history into account, every improvement on the agent propagates without losing the context already accumulated. It's the difference between a system that learns and one that forgets.
Behind the scenes, this is called Memory Fold: when a conversation gets long, the older history is automatically summarized and replaced by a synthesis. The conversation stays light and fast, the thread is never lost.
| Stateless platforms | BlueSky platform (stateful) | |
|---|---|---|
| Memory | Per single session | Persistent per customer, agent, organization |
| Context | Restarts at every request | Accumulates over time |
| Learning | Only in model training | Continuous, on every interaction |
| Multiple agents | Each isolated | Share relevant context |
| Practical impact | Same mistake repeated | Improves at every iteration |
"Building a stateless agent is easy. Building a stateful platform that supports it is the real engineering."
But stateful alone is an archive. Without a system that organizes it, an agent's memory becomes noise in a month. That's why we also built auto-coaching.
Auto-coaching. The platform that teaches itself, every night.
While your customers sleep, the BlueSky platform studies everything that happened during the day. Reorganizes memory. Rewrites its own instructions. Gets ready to do better tomorrow.
Every night, automatically, the platform runs an auto-coaching cycle: it re-reads all the day's conversations, analyzes agent behavior, identifies where they did well and where they struggled. Then it acts.
It reorganizes the knowledge base. Updates the RAG structure. Rewrites skills that underperformed. Tunes prompts that produced uncertain answers. Optimizes LLM usage by choosing the most efficient model for each request type. Consolidates long-term memory by separating noise from signal.
When the customer opens the dashboard the next morning, the agents are not the same as the night before. They are better. This is how a truly closed-loop system works: it doesn't just save logs — it transforms them into learning.
The engine that makes this possible is called SelfCoach: an "extractor" agent reads completed conversations, two agents debate to challenge every improvement proposal, and an "executor" applies the approved ones — using the connected tools, within the configured permissions.
What happens every night
- 00:00The platform collects all the day's conversations and agent behaviors.
- 00:30Analyzes where agents answered well and where they struggled — signals, not opinions.
- 01:00Reorganizes the knowledge base: relevant documents are promoted, obsolete material archived.
- 01:30Recomputes RAG embeddings on content that caused confusion.
- 02:00Rewrites underperforming skills: prompts, rules, few-shot examples.
- 02:30Optimizes LLM routing: the most powerful model only where needed, efficient models for the rest.
- 03:00Consolidates long-term memory: extracts stable patterns, discards noise.
- 06:00The agents start over. They are not the same as yesterday evening.
| Static | Auto-coaching | |
|---|---|---|
| KB updates | Manual, on customer ticket | Automatic, every night |
| Prompt tuning | Only if someone notices | Rewritten autonomously on patterns |
| LLM costs | Fixed, often oversized | Optimized: right model at the right time |
| Long-term memory | Grows chaotically | Consolidated, segmented, indexed |
| Time to improve an agent | Weeks / months | 1 night |
"We don't program agents — we adopt agents that learn on their own."
Closed-loop. Everything comes back, nothing is lost.
Traditional companies run open-loop: data accumulates and is lost. Our platform is designed so every signal closes the loop.
When an agent answers a customer, the system doesn't just send the reply. It measures how it was received, records the follow-up, tracks whether the problem was actually solved. That signal re-enters the auto-coaching cycle and becomes input to improve the next similar conversation.
When a new document is uploaded to the knowledge base, the system measures how much it's actually used by the agents. Documents never consulted get re-indexed or flagged as obsolete. Heavily used documents get promoted and enriched with metadata.
When a customer changes behavior — asks more technical questions, changes tone, shifts interaction hours — the platform notices and adapts the context. It's the "closed-loop" principle applied at every level: data → analysis → action → new data.
Agents don't work alone. They talk to each other.
An isolated agent solves a case. A platform of agents solves an entire process.
Our platform is multi-agent from day zero. Alice handles customer care. A sales agent qualifies leads. An analytics agent reads data and flags anomalies. An editorial agent produces content.
Each is specialized in its domain, but all share context through the platform. When a customer asks Alice a commercial question, Alice doesn't improvise: she passes the context to the sales agent, who comes back with the right answer.
For the customer it's a single conversation. For the platform it's three agents collaborating in real time, each doing what they do best.
We call this mechanism Agent Relay: an agent that can't do everything can delegate a task to another specialized agent, and get the result back — without the customer noticing.
For sales qualification, the platform also activates a dedicated mode — Lead Capture — designed specifically to collect and process contacts, distinct from knowledge-base-driven conversation.
"The difference between a chatbot and a platform is the same as between an employee and a company."
Right model, at the right time.
The way we pick the right model for each request has a name — and it's important enough to deserve its own page.
Discover ModelMatch™Your data stays yours.
Privacy, sovereignty, GDPR. Not as a slogan, as architecture.
The BlueSky platform is designed GDPR-compliant by design. Every customer's data is isolated in a dedicated tenant. Conversations are never used to train third-party models — ever. Long-term memory, embeddings, logs: everything lives within the customer's perimeter.
For companies requiring total data sovereignty, we offer on-premise deployment or dedicated private cloud. The same platform, the same agents, but installed in your data center.
Tenant isolation by default
Each customer in their own logical perimeter, data and memory never mixed.
EU hosting
Servers and backups in European territory. GDPR-compliant by design.
On-premise optional
Deploy in your data center for sensitive data or total sovereignty.
Built-in guardrails
Three layers of protection active on every conversation, in real time.
PII Shield
Masks or blocks personal data before it leaves the conversation.
Jailbreak Shield
Detects attempts to manipulate the agent's behavior.
Truth Check
Flags unverified answers before they reach the user.
The platform talks to what you already have.
Every integration is a dedicated connector, not a generic adapter. Already 10 and growing every month.
Discover Connector HubThree types of customers, one single platform.
SMBs with standard needs
You buy a ready-made agent.
Pick Alice in the plan that fits you. Setup in a few days, standard integrations, support included.
Go to pricingCompanies with specific processes
We build the agent together with you.
Have a unique use case — internal HR, repricing, complex lead qualification? We develop vertical agents on our platform. The advantage: all properties (stateful, auto-coaching, closed-loop) come free.
Talk to usEnterprises with sovereignty needs
We install the platform at your premises.
Perpetual license or dedicated private cloud. The same platform, but in your perimeter, under your control.
Request a quoteVoice & Vision. Not just text.
Our agents listen and see, not just read. The Voice Pipeline transcribes incoming audio and generates natural voice replies on widget, WhatsApp, Telegram and Discord. Vision Input lets agents understand attached photos and images — a construction site, a product, a photographed document — with no need to describe them in words.
Voice Pipeline
Speaks and listens, on every channel.
Vision Input
Understands what you show, too.
Want to see the platform under Alice?
In 30 minutes we'll show you how it really works: stateful, auto-coaching, multi-agent. No slides — the real system, on a real case like yours.