VectorStackAI tunes your GenAI stack, agents and search alike, end-to-end against your accuracy, latency, and cost tradeoffs, and finds the Pareto frontier of operating points for your KPI.
Founded by Shreyas Saxena: PhD INRIA · ex-Apple ML · ex-Cerebras Principal Research Scientist.
“Most of the GenAI market competes within a layer. We integrate across them.”
Every vendor optimizes its own layer (best embedding model, best reranker, best vector DB, best agent runtime) and benchmarks itself against others in that same layer. None of them is moving the number the enterprise actually cares about: the product-level metric that cuts vertically across the whole stack. Single-component gaps that look decisive on a leaderboard collapse once the stack is tuned together, so picking your embedding vendor from a benchmark is the wrong place to start.
And what you take away isn’t a tuned configuration of someone else’s APIs: it’s a proprietary stack. Fine-tuned embeddings, learned weights, distilled rerankers, custom indices: assets your competitors can’t replicate by paying the same API bills. The optimization compounds into IP you own, not a recipe anyone can copy.
Dense, sparse, fusion, and reranking tuned across a heterogeneous corpus, so the production stack hits the accuracy target without inheriting the all-premium cost curve.
Textual gradients on a DAG of your agent runtime, used to optimize prompts, tool definitions, and few-shot examples against your KPI, all end-to-end.
For legal AI teams where quality depends on both finding the right documents and calling the right search surface with the right filters.
For finance teams shipping document QA where a wrong answer is worse than no answer, and the hallucination rate is the product metric.
The anchor essay: why assembled stacks stall, and the end-to-end loop that fixes them.
Why single-component leaderboards are the wrong place to choose an embedding model, and what closes the gap once the stack is tuned together.
The library behind our agent-optimization engagements, out of stealth: a PyTorch-shaped training loop for agent runtimes.
Send the KPI you're stuck against: accuracy, latency, cost, hallucination rate. We'll tell you where the frontier is.