u/Low_Variation5730

I got the onboarding of the project called Opposite Classic, well it has very long onboarding for like 2 - 3 hours.

Is there anyone working on this ?

Describe your approach here.

- How does your system ingest and preprocess the varying data sources (market, macro, sentiment)?

- What risk modeling techniques were selected, and how are they integrated into the trading decision pipeline?

- How does your semi-automated strategy generate signals while respecting portfolio constraints and handling realistic conditions like slippage?

- How is the dashboard designed to provide explainable insights and key metrics (Sharpe, drawdown) to stakeholders?

I'm building an AI-powered creator intelligence platform — think of it as a growth strategist for content creators (Instagram, YouTube) that gets smarter the longer someone uses it. Not a chatbot. The goal is something closer to a senior advisor that genuinely knows your content patterns, audience behaviour, and growth trajectory over months.

I've gotten to a point where the core data infrastructure is solid but I'm hitting some real architectural decision points that I'd love expert input on. Posting here because I've found the ML and agent engineering communities give much more honest feedback than startup circles.

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**What the system broadly does:**

- Ingests signals from multiple external platforms (trending content, social signals, search trends) every few hours and stores everything in a tiered retrieval system (hot cache → vector embeddings → knowledge graph)

- Maintains a per-user longitudinal memory that tracks what advice was given, whether it was followed, and whether it correlated with better performance

- Generates personalised content strategy — scripts, growth roadmaps, posting schedules — that should sound and feel specific to that creator, not generic

The LLM orchestration layer is built on LangGraph and the retrieval is a 3-tier RAG (Redis hot window → pgvector → graph DB).

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**The questions I'm genuinely stuck on:**

**1. Algorithmic detection vs. agentic reasoning for intent classification**

Right now I'm deciding between two approaches for routing user queries to the right reasoning node (scripting vs. strategy vs. diagnosis):

- **Option A**: A lightweight classifier (fine-tuned small model or even a rules-based system) that categorises intent deterministically before the LLM touches it

- **Option B**: Let the LLM itself classify intent as the first step in the graph, using a constrained output (enum) call

Option A is faster and more predictable but adds a separate model to maintain. Option B is simpler but introduces LLM non-determinism at the routing layer, which feels wrong for a product that needs consistent behaviour.

For those who've built production agentic systems — is intent classification at the graph entry point something you keep algorithmic or do you trust the LLM for it? What broke for you?

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**2. Voice fingerprinting from behavioural data — how much data is enough?**

I want to build a "voice fingerprint" per user — essentially a compressed representation of their content style, vocabulary, tone, and what has historically performed well for them specifically. The plan is to rebuild this weekly from their content history.

The problem is cold start. A new user has maybe 10-20 posts. A 6-month user has 200+. The confidence in the fingerprint should scale with data volume but I'm not sure how to handle the transition gracefully — especially avoiding the problem where a small sample (maybe 2 viral posts) dominates the fingerprint and makes it unrepresentative.

Has anyone built user-level style models that handle sparse data well? Is there a principled way to blend a "population prior" (what works for similar creators) with an individual posterior (what works for this specific person) without it feeling generic?

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**3. Attribution in a closed-loop learning system — the fundamental problem**

This is the one I'm most stuck on. The system suggests actions to creators (post at this time, use this hook, try this format). Some creators follow the suggestions. Some posts perform well. Some don't.

The naive approach is to correlate suggestion → post → performance. But this is obviously confounded by a hundred things (algorithm spikes, trending audio, day of week, thumbnail quality).

I'm currently thinking about a creator-relative normalisation approach — measuring performance against the creator's own rolling baseline rather than absolute numbers — combined with a minimum evidence threshold before drawing any conclusions. But even then, proper causal attribution feels essentially impossible without a controlled experiment.

Is anyone doing meaningful attribution in consumer-facing AI products? Is the honest answer just "you can't do causal attribution so track correlation with appropriate confidence intervals and be transparent about it"? Or is there a smarter framing I'm missing?

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**4. Graph vs. vector for long-term trend trajectory tracking**

For tracking how trends move over time — whether a topic is rising, peaking, or declining — I'm using a knowledge graph structure (nodes for topics/niches/formats, edges representing temporal relationships and cross-niche correlations).

The question is whether this graph structure actually adds value over a well-indexed time-series table with vector similarity on top. The graph gives me things like "this trend in fashion is 3 weeks ahead of the same trend in fitness" (cross-niche lag relationships). But maintaining graph consistency as data volumes grow is non-trivial.

For time-series trend intelligence specifically — has anyone found graph databases or graph structures in Postgres genuinely outperforming simpler approaches, or is it usually over-engineering?

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**5. How do you handle context window bloat in longitudinal memory systems?**

As user history grows, the temptation is to inject more and more historical context into the LLM context window. We've budgeted for this but I'm already seeing the system prompt get heavy at 6+ months of user history.

The architecture I'm considering: don't dump memory into the system prompt. Instead pre-load everything into graph state at the entry node, and have each downstream node pull only the memory slice relevant to its function (scripting node gets voice fingerprint, strategy node gets growth trajectory, diagnosis node gets anomaly history).

Has anyone done selective memory injection per node in LangGraph or similar? Does this hold up in practice or does the LLM still need broader context to reason well even when a node is "specialised"?

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**What I'm NOT asking:**

- Whether to use RAG (already using it, works fine for trend retrieval)

- General "what LLM should I use" questions

- Whether this is a good business idea

**What would genuinely help:**

Real production experience with any of the above. Especially failure modes — what seemed reasonable in design but broke under real usage. I'll read every reply.

Thanks in advance.