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Hi! I'm your AI Assistant
I can help you analyze interview sessions, understand candidate performance, and provide insights about your recruitment data.
Here's a dirty secret about most AI applications: they're either blazingly fast but amnesiac, or they remember everything but crawl like molasses. The industry has been selling you a false dichotomy—stateless speed versus stateful intelligence.
At JobInterview.live, we built the AI Board Room on a simple premise: why not both?
Our architecture reconstructs complete session state on every API call using Redis hot cache backed by Postgres persistence. It's stateless from the API's perspective, but your AI agents—Atlas, Cipher, Nova, and the rest—remember every nuance of your conversation. This isn't just clever engineering; it's the foundation that lets solo founders have genuinely productive conversations with AI, not just rapid-fire Q&A sessions.
Most chatbots are idiots with short-term memory loss.
You tell them your business context. They forget it three messages later. You explain your constraints. Gone. You share your goals. Vanished into the token void.
The typical "solution"? Cram everything into the prompt window. But context windows, even at 2M tokens, have a fatal flaw: they're linear cost structures. Every API call pays the full price of your entire conversation history. Your costs scale with engagement—the exact opposite of what you want as a founder building a product people actually use.
The alternative—truly stateful servers with sticky sessions—creates operational nightmares. Load balancing becomes complex. Horizontal scaling requires session migration. Server restarts mean lost state. It's the architecture that enterprise loves and startups die from.
Here's where we get provocative: state doesn't belong in your API layer.
State is data. Treat it like data. Store it in data systems—fast ones for reads, durable ones for writes. Then reconstruct it on-demand, every single request.
The AI Board Room's architecture is radically simple:
When you talk to Atlas about your startup strategy, here's what actually happens:
session:{uuid} from Redis (5-10ms)Total overhead? Under 100ms. Total memory? Zero between requests.
Redis is our working memory. Every active session lives here:
session:{uuid}:messages - Last 50 turns (circular buffer)
session:{uuid}:dossier - User context, goals, preferences
session:{uuid}:agents - Active agent assignments (Atlas, Cipher, Nova)
session:{uuid}:skills - Loaded expertise modules
session:{uuid}:actions - Extracted tasks awaiting execution
The magic number is 50 messages. Why? Because it covers 95% of meaningful conversation context while keeping memory footprint predictable. A typical session with full context weighs ~200KB in Redis—cheap enough to keep thousands hot simultaneously.
When you switch from talking strategy with Atlas to analyzing financials with Cipher, the system doesn't "remember" in the traditional sense. It reconstructs your conversation context, sees you were just discussing market positioning, and Cipher opens with relevant financial implications. It feels like memory. It's actually just really fast data retrieval.
Postgres is our long-term memory and analytical engine. Everything gets written here:
This is where we get sophisticated. The User Dossier isn't static—it evolves. When you tell Nova about a new product direction, that updates your dossier. When you consistently ask Cipher to focus on runway, that preference persists. The system learns your communication style, your priorities, your constraints.
But here's the key: Postgres writes are async. Your conversation never waits for database commits. Redis serves the experience; Postgres ensures durability. If Redis evicts your session (rare, but possible), we reconstruct from Postgres in under 100ms. You never notice.
This architecture enables something powerful: protocol diversity without complexity.
Model Context Protocol lets our agents use external tools (calendar, email, project management) without tight coupling. When Atlas needs to check your availability, it's an MCP call to your calendar. When Cipher needs market data, MCP to your data sources.
Because session state is reconstructed every turn, tool results get naturally incorporated into context. No special state management. No complex callbacks. Just: load session, call tool, update session, respond.
The really interesting part? Agent-to-Agent protocol for delegation. When you ask Atlas a complex question spanning strategy and execution, Atlas can delegate to Cipher (financial analysis) and Nova (technical feasibility) within a single conversation turn.
The stateless architecture makes this trivial:
Traditional stateful architectures make this a nightmare of locks, transactions, and race conditions. With stateless reconstruction, it's just parallel data operations.
Here's where it gets wild. native audio input means voice conversations skip transcription entirely. You speak, The model processes audio directly, responses flow back.
But audio is big. Storing raw audio in session state would kill our architecture. Instead:
Voice feels continuous and natural, but architecturally, it's the same stateless reconstruction pattern.
Google ADK's Deterministic Backbone solves the "AI is unpredictable" problem. But determinism without context is just consistent mediocrity.
The breakthrough is combining them:
This is why conversations with the AI Board Room feel different. It's not just that the AI is smart—it's that it's smart about you and your situation, consistently, every single time.
Here's the payoff. All this architecture enables one critical feature: Action Extraction.
As you talk with your AI Board Room, the system identifies:
These get extracted into structured data, stored in both Redis (immediate access) and Postgres (long-term tracking). They become the bridge between conversation and execution.
Next time you open the AI Board Room, your session reconstructs with awareness of open actions. Atlas might open with: "You mentioned drafting that pitch deck—want to work on positioning?" Not because it's "remembering" in some mystical way, but because Action Extraction + session reconstruction makes context retrieval instant and automatic.
If you're building solo or with a small team, you can't afford enterprise complexity. You also can't afford amnesia AI that forgets your context.
This architecture gives you:
You get the AI Board Room experience—Atlas, Cipher, Nova, and the team—that actually remembers your business, your goals, and your constraints. Not because of some architectural magic trick, but because we treat state as data and reconstruct it blazingly fast.
We made a bet: context reconstruction would get faster and cheaper than stateful server management.
We were right. Redis operations are single-digit milliseconds. Postgres queries on indexed session data run in 50-80ms. Network latency dominates over computation. Meanwhile, managing stateful servers—session affinity, graceful shutdowns, state migration—consumes engineering time that could build features.
As models get better at processing context efficiently (and they are, rapidly), reconstruction costs drop. As infrastructure gets faster (and it does, continuously), latency shrinks. The economics favor stateless reconstruction over stateful servers, and the gap widens every quarter.
This isn't just an architecture article. It's an invitation.
The AI Board Room at JobInterview.live is live, running this exact architecture. Talk to Atlas about your strategy. Work with Cipher on your financials. Brainstorm with Nova on product direction.
Notice how they remember your context. Notice how responses feel personalized. Notice how conversations flow naturally across sessions.
Then remember: it's all stateless reconstruction, Redis hot cache, and Postgres persistence. The best of both worlds, at scale, for founders who need AI that actually works.
Try the AI Board Room today. Your first conversation is free. Your context, always preserved.
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