The Real-Time AI Bottleneck Everyone's Fighting

Your users expect chat-level responsiveness. Sub-100ms responses for copilots, instant search results, real-time fraud detection. But every time you try to deliver, you hit the same wall: even the fastest models take a few seconds for meaningful responses. Add retrieval, tool calls, or complex reasoning? 

You're looking at multi-second delays.

Most teams throw hardware at the problem. More GPUs, custom silicon, edge deployment. The bills skyrocket, but tail latency still kills the experience. There's a better way.

The Preprocessing Shortcut: Compute Before They Ask

Instead of waiting for each query to trigger full LLM reasoning, flip the problem: do the heavy compute before users ask.

Think of it as the difference between cooking a meal to order versus having prepared ingredients ready to assemble. When someone wants insights about their security incidents, don't generate the analysis live; have it ready and waiting.

Here's the core insight: Most "real-time" AI requests follow predictable patterns. Users ask similar questions about the same entities (customers, incidents, products). You can batch-process these patterns ahead of time and serve pre-computed results in milliseconds.

How Preprocessing Works in Practice

Before the request:

  • Batch or streaming jobs continuously process your data
  • Generate summaries, run analyses, extract insights
  • Store "artifacts" (insight cards, answer templates, root cause summaries) in fast storage
  • Index by likely query patterns (user_id, incident_id, product_category, etc.)

At request time:

  • System fetches the relevant precomputed artifact (~10-20ms)
  • Optionally fills in live variables or light personalization (~20-30ms)
  • Returns complete response in under 50ms

Your latency is now dominated by cache reads and network, not LLM compute.

Real-World Applications

Cybersecurity Incident Response

Instead of generating incident insights when analysts click, continuously analyze incoming security events. Pre-build "incident cards" with threat summaries, affected assets, and recommended actions. When an SOC analyst investigates? Instant context, with optional real-time updates for the last few minutes.

Root Cause Analysis

Run nightly jobs analyzing application logs, correlating errors across services, and synthesizing failure narratives. Store by service, time window, and error signature. During an outage, serve the pre-built RCA summary immediately, append recent events if needed.

Support & Product Q&A

Precompute answers for common questions by intent, product area, and user segment. "How do I configure SSO for enterprise accounts?" already has a tailored response waiting. Serve from cache, personalize with account-specific details.

Personalized Recommendations

Instead of computing recommendations live, precompute likely suggestions and explanations for user cohorts nightly. At request time, just rerank a small candidate set based on recent activity.

The Trade-offs You Need to Know

Compute Cost: Preprocessing everything is wasteful. A typical SaaS might have 10M possible user-intent combinations, but only 100K get asked regularly. Focus preprocessing on high-value, high-frequency patterns.

Freshness: Precomputed answers lag behind real-time data. Critical for use cases where "5 minutes ago" matters, but fine for most analytical queries.

Storage Complexity: Managing millions of artifacts efficiently requires thoughtful indexing, compression, and cleanup strategies.

Making It Work: Best Practices

Apply the Pareto Principle

Start by analyzing your production logs. Identify the 10-20% of query patterns that cover 80-90% of requests. Precompute only these high-impact scenarios.

Partial Preprocessing

You don't need to precompute entire responses. Generate the expensive intermediate results (embeddings, retrieved context, base analysis) and do light finishing touches at request time.

Smart Invalidation

Use TTLs appropriate to your domain. Security insights might refresh every 15 minutes, while customer analytics could be daily. Hook into your event streams to invalidate stale artifacts.

Entity-Based Filtering

Only precompute for active entities. Don't generate insights for inactive users or deprecated products. Use recency signals to prioritize preprocessing work.

Optimize Storage

Design compact artifact schemas. Use typed storage and efficient lookup keys. Consider compression for large text summaries.

When NOT to Precompute

Skip preprocessing for:

  • Extremely personalized queries with unique contexts every time
  • Use cases demanding up-to-the-second freshness (active trading, emergency response)
  • Domains where stable patterns don't emerge from historical data

Getting Started: Your First Two Weeks

Week 1: Analyze query logs to identify top patterns. Design your artifact schema. Build the preprocessing pipeline for one high-value use case.

Week 2: Implement fast lookup API. A/B test against your current approach. Measure latency improvements and iterate.

Start small- pick one workflow where users consistently ask similar questions about predictable entities.

The Real Speed Hack

Ultra-low latency GenAI isn't about faster models or bigger GPUs. It's about moving compute off the critical path.

Users don't care if their insights were generated 30 seconds ago or 30 minutes ago- they care that clicking "analyze" feels instant. Preprocessing makes your AI feel real-time, even when the heavy lifting happened hours earlier.

The best user experiences aren't built on the fastest hardware. They're built on the smartest architecture.