We build production AI features inside real products. RAG systems, agents, evals, LLM workflows, and the surrounding UX that makes them usable. Shipped by engineers who have done it before, embedded in your team via The Density Method.
Most AI pilots fail at the integration layer, not the model layer. The hard work is wiring an LLM into a permissioned codebase, instrumenting evals that survive contact with real data, and shipping a UX that customers actually adopt.
Embedding pipelines, vector stores, hybrid search, chunking strategies, evals against your actual corpus. Wired into the auth and permissions model your customers already trust.
Tool use, function calling, planner-executor patterns, eval harnesses, and the boring observability work that turns a flashy demo into a system you can roll out to real users.
Streaming UIs, prompt scaffolds, error states, fallback paths, and the design work that makes generative features feel like part of the product instead of an experiment bolted on top.
Every engagement runs through the same four phase framework. Two week paid diagnostic. Seven to ten day match from a 2% acceptance pool. Structured 30/60/90 embed with weekly tech lead 1:1s. Multi year retention.
100% success rate over the last six years. Zero forced replacements. 120 day replacement guarantee on every placement.
Read how the Embedded Method works →We have shipped these systems in healthcare, fintech, real estate, and customer support. Patterns and playbooks come into your codebase, not into a slide deck.
The same patterns ship into your stack. Same engineers. Same Density Method.
Prevetted is a search and discovery product for tech buyers. We designed and shipped it with a small team running the same playbook we use on client engagements. Live, in production, paying users.
Visit prevetted.ai →Book a 30 minute call. We will tell you honestly whether the work is real and whether we are the right fit.
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