Shaped

Why guess with your rankings? 🤔 With ShapedQL, you can explicitly weight Keyword vs. Semantic search using simple SQL. 10% BM25 + 90% Vector? Done in one line. ⚡️ Watch the demo 👇

Netflix doesn't just personalize which movies you see. It personalizes which ROWS you see. We wrote the playbook on how to build it. shaped.ai/blog/how-to-bu…

discover weekly = hybrid filtering collaborative: fails for new releases content-based: too obvious hybrid: ELSA + AI enrichment + adaptive scoring shaped.ai/blog/how-to-bu…

"the new model feels better" that's not a metric offline eval: Recall@10: 0.45 → 0.50 (+11%) NDCG@10: 0.54 → 0.61 (+13%) online A/B test: CTR: +15% Conversion: +19% now you have proof shaped.ai/blog/ab-testin…

your agent's config: • ranking formula in .env • filters hardcoded • vector DB settings in UI • features scattered when it breaks: 🤷 with GitOps: • everything in Git • PR review before deploy • one command to rollback • clear audit trail shaped.ai/blog/gitops-fo…

your agent: 5 seconds to respond your LLM: 3 seconds where are the other 2 seconds? retrieval. vector DB: 220ms filtering: 50ms scoring: 120ms reordering: 60ms network hops: 4x = 450ms+ per query Shaped's fast_tier: 30-100ms for all 4 stages unified shaped.ai/blog/sub-100ms…

The scaling law for agents isn't model size. It's what goes into the context window. Attention is quadratic. 2x tokens = 4x cost. 10 ranked results > 200 stuffed chunks. Every time. At 10x lower cost. We wrote the math: shaped.ai/blog/context-w…

your agent: "order has shipping tier 4 and status 7" what the customer needed: "ships in 2 days via FedEx, in transit" AI Views fix this—enrich at write time, not read time shaped.ai/blog/the-statu…

most "AI hallucinations" are just stale retrieval user: "is this in stock?" agent's index: last updated 2am product: sold out at 9am agent: "yes" the model is fine. your data pipeline is broken. how to fix it 👇 shaped.ai/blog/why-your-…

We built a complete technical guide: ✓ Working code ✓ Architecture diagrams ✓ Dynamic weight tuning patterns ✓ Real-world use cases (e-commerce, travel, content) ✓ When NOT to use this approach Read it here: shaped.ai/blog/building-…

Real production benchmarks: Traditional stack: → 250-500ms (3-4 network hops) → Hours to update weights → 42% precision@5 Value models: → 50-100ms (single query) → Seconds to update weights → 68% precision@5 Same data. Better architecture.

Your agent just cost you $40,000. It recommended wireless headphones to a customer who bought the exact same pair yesterday. The problem? It optimized for semantic similarity. It ignored business logic. Here's how to fix it 🧵

Your agent: ❌ Recommends items twice ❌ Ignores user preferences ❌ Treats everyone the same The problem? Stateless RAG. New guide: How to build agents that actually remember users shaped.ai/blog/building-…

Stop putting business rules in prompts. LLMs are probabilistic. SQL is deterministic. Filter data at the database layer, not in the LLM context. Your agent can't recommend out-of-stock items if it never sees them. The architecture guide: shaped.ai/blog/how-to-bu…

Stop feeding your AI agent 50 documents when it only needs 5. Positional bias means LLMs ignore anything in the middle of their context window. Pre-ranking solves this, here's how production teams are doing it: shaped.ai/blog/why-pre-r…

Is your AI agent just a "stochastic parrot"? 🦜 If your retrieval layer is weak, your LLM is just guessing. To build agents that actually drive ROI, you need high-signal data, behavioral context, and real-time filtering. Read our latest deep dive: shaped.ai/blog/the-stoch…

We finished as the #5 Product of the Day on @ProductHunt! 😸 The most common feedback we heard yesterday? "Finally, I can stop writing glue code for my RAG stack." Try the free playground: playground.shaped.ai