New Embedding Model: Faster Search, Lower AI Costs

Most SaaS teams in the U.S. aren’t losing deals because their product is missing features. They’re losing deals because users can’t find what they need—support answers, the right workflow, the right document, the right product recommendation—fast enough.

That’s why the announcement of a new and improved embedding model (more capable, more cost effective, and simpler to use) matters more than it sounds. Embeddings are the quiet engine behind modern AI search, recommendations, and retrieval-augmented generation (RAG). When the embedding layer improves, everything above it—chatbots, knowledge bases, customer self-service, internal tools—gets better and cheaper.

This post is part of our series, “How AI Is Powering Technology and Digital Services in the United States.” The theme is practical: what changes in AI actually move the needle for U.S. startups and digital service teams trying to scale without lighting their budgets on fire. Embeddings are one of those changes.

What a “better embedding model” actually changes

A better embedding model improves the quality of similarity. That’s the core. If two pieces of content “mean” the same thing, their vectors should land closer together—even if they don’t share keywords.

In practical terms, improvements show up as:

• Higher search relevance in product docs, support portals, and internal wikis
• More accurate RAG (your AI assistant cites the right passages more often)
• Better deduplication and clustering for content ops and analytics
• Stronger recommendations (“people who viewed this also need…”) without brittle rules

Here’s a snippet-worthy way to think about it:

Embeddings turn meaning into math. If the math gets better, every AI feature that depends on “what’s similar” gets better too.

Why this matters in 2025 U.S. SaaS and digital services

U.S. customers are less patient than product teams think. If a user has to read three irrelevant help articles before finding the right one, you pay for it—through churn, support tickets, and lower expansion.

Meanwhile, buyers increasingly expect “AI search” and “AI assistants” to work out of the box. The fastest path to that isn’t always a bigger language model. Often, it’s better retrieval, which starts with embeddings.

The real win: cost-effective embeddings that scale with you

The RSS announcement highlights three points: more capable, cost effective, simpler to use. The cost piece is not just a nice-to-have. It changes what you can afford to build.

When embeddings are expensive, teams cut corners:

• They embed only a subset of documents
• They avoid re-embedding when content changes
• They skip multilingual support
• They don’t experiment with hybrid retrieval or reranking

When embeddings get cheaper, you can do the things that make systems reliably useful.

A budgeting model that doesn’t fall apart

In most U.S. SaaS companies, “AI features” start as a pilot. Then one of two things happens:

1. The pilot succeeds and usage grows, and suddenly costs are under a microscope.
2. The pilot is inconsistent, and leadership decides “AI isn’t ready.”

Cheaper embeddings help both cases:

• If you scale usage, you have more headroom before margins get squeezed.
• If you’re still tuning quality, you can afford iteration—re-embed, test chunking strategies, compare retrieval settings—without turning every experiment into a finance review.

A concrete example: support deflection math

Say your help center and internal KB contain 50,000 chunks of text (a common number once you split docs into retrieval-sized pieces). If you re-embed monthly to keep up with product changes, cost adds up quickly. Lower-cost embeddings make freshness realistic.

Fresh embeddings are underrated. Stale embeddings are one of the top reasons RAG answers drift away from what your product does today.

Simpler to use means faster shipping (and fewer fragile pipelines)

A “simpler” embedding model sounds like marketing until you’ve inherited an embedding pipeline built in a hurry. I’ve seen teams wire together:

• A chunker that isn’t deterministic (same doc chunks differently on each run)
• An embedding job that silently fails on edge cases
• A vector database filled with multiple versions of the same content
• No traceability from an answer back to the exact chunk revision

When an embedding model is easier to use—and typically better documented and more consistent—teams spend less time babysitting infrastructure and more time improving the experience.

What simplicity should look like in your stack

If you’re building AI-powered search or a RAG assistant for a U.S.-based SaaS product, your embedding workflow should be:

1. Deterministic chunking (same input → same chunk boundaries)
2. Clear identifiers (doc_id, chunk_id, revision)
3. A re-embedding plan (on publish, nightly, weekly—pick one)
4. Evaluation baked in (a small test set you run every release)

If the new embedding model reduces the operational friction around any of these, that’s a genuine product win.

Where better embeddings show up in real U.S. products

A stronger embedding model isn’t a feature by itself. It’s an enabling layer. Here are the most common “where it pays off” areas I’d prioritize for lead-gen-focused teams in 2025.

1) AI-powered site search that actually understands intent

Keyword search fails when users describe problems differently than your docs do. Embedding-based semantic search closes that gap.

What improves with a more capable embedding model:

• Better matches for paraphrases (“SSO error” vs “login via Okta failing”)
• Stronger handling of acronyms and product-specific language
• More reliable results for long, messy queries copied from error logs

If you sell to enterprise in the U.S., semantic search isn’t fluff. It’s a sales enabler—procurement teams and admins live in documentation.

2) RAG assistants that cite the right source more often

RAG quality is often blamed on the language model, but retrieval is usually the bottleneck. Better embeddings:

• Reduce “near miss” retrieval (top results are close but not actionable)
• Improve answer grounding (citations match the response)
• Cut hallucinations caused by missing context

A sentence you can share with your team:

If your RAG assistant feels unreliable, upgrade retrieval before you upgrade the model.

3) Personalization and recommendations without creepy tracking

Embeddings can represent users, items, or sessions in a privacy-conscious way: “people who used these features tend to need X next.” With the right design, you can reduce reliance on invasive tracking.

Better embeddings help especially with cold start and sparse data—common for startups and new products.

4) Content ops: deduplication, clustering, and “what should we write next?”

Marketing and documentation teams can use embeddings to:

• Identify duplicate pages that cannibalize SEO n- Cluster tickets and feedback into themes
• Spot missing articles based on repeated queries that don’t retrieve good matches

This ties directly into AI-driven content creation and automation: you’re not just generating content—you’re generating the right content.

How to adopt a new embedding model without breaking production

Switching embedding models can boost quality, but it’s not a copy-paste change. Different models produce different vector spaces; you can’t reliably compare vectors created by model A with vectors created by model B.

Here’s the approach that avoids downtime and protects relevance.

Step 1: Run a dual-index migration

Create a second vector index for the new embedding model. Keep the old index live while you test.

• Index A: current embeddings
• Index B: new embeddings

Route a portion of traffic (or internal users) to Index B.

Step 2: Evaluate with a small, real test set

You don’t need a massive benchmark. You need a truthful one.

Build 50–150 queries from:

• Real support searches
• Sales-engineer questions
• Onboarding friction points
• “No result found” searches

For each query, define what “good” means:

• The correct doc appears in the top 3
• The correct chunk appears in the top 5
• The assistant cites the right policy/feature page

Track metrics like Recall@K and a simple human rating (“correct / close / wrong”).

Step 3: Revisit chunking before blaming the model

Better embeddings help, but chunking still matters. Two practical rules:

• Keep chunks focused (one concept per chunk when possible)
• Preserve structure (headings and bullet lists often carry meaning)

If your docs are heavy on tables, consider storing table rows as separate chunks.

Step 4: Add a reranker if relevance is business-critical

Embeddings retrieve candidates; rerankers reorder them with deeper reasoning. If search relevance ties directly to revenue (enterprise support, compliance, medical workflows), a reranker often pays for itself.

People also ask: Embeddings edition

Are embeddings only for chatbots?

No. Chatbots are the visible application, but embeddings are more broadly useful for semantic search, recommendations, clustering, and content intelligence.

Will a better embedding model fix hallucinations?

It reduces them when hallucinations are caused by missing or irrelevant retrieved context. If the assistant is hallucinating despite correct sources, you also need better prompting, citation rules, and output constraints.

Do we need to re-embed everything to switch models?

Yes. Because vectors aren’t comparable across models, you re-embed your corpus and rebuild the index. The safe way is dual indexing and a staged rollout.

What to do next if you want AI search that drives leads

For U.S. SaaS and digital service companies, embeddings are one of the highest-ROI improvements you can make to AI-powered experiences. A more capable, cost-effective, simpler embedding model gives you three advantages at once: better relevance, room to iterate, and faster time to production.

If you’re using AI to drive growth—support deflection, product-led onboarding, content discovery, or an assistant that helps buyers evaluate your platform—start by auditing retrieval:

1. Pick 50 real queries your users actually type
2. Measure how often your current system finds the right source
3. Pilot the new embedding model in a parallel index
4. Ship the upgrade with a staged rollout

The broader theme of this series is straightforward: AI in the United States is scaling digital services by making software more responsive and more personal without adding headcount. Embeddings are one of the simplest places to feel that impact quickly.

What would change in your business if customers could find the right answer—or the right next step—in under 10 seconds, every time?