How Alphasense Uses Fast Inference To Make Agentic Research Interactive

How AlphaSense Uses Fast Inference for Agentic Research Skip to main content

General Compute Selects Cerebras to Bring Ultra-Fast Inference to Agentic Coding >>

Oct 01 2026 How AlphaSense Uses Fast Inference to Make Agentic Research Interactive Alec McLean Griffin Marge

Cerebras: Alec McLean and Griffin Marge AlphaSense: Aimee Hirsch, Chris Ackerson, Daniel Campos, Eldar Tinjic, James Chen, Matt Lapointe, Michelle Turner, Mohit Sharma, Pete Daly, Prashant Budania, and Yumo Luo The new bar for enterprise research through architectural trade-offs in low-latency GenAI Enterprise users no longer want a search box that returns a list of documents and leaves the synthesis to them. They want a research partner - a system that can understand a question, break it down, retrieve relevant evidence, and produce a clear, supported answer. Consider an equity analyst preparing for an earnings call. Instead of manually stitching together transcripts, filings, and news, they ask: “What are the key risks to margin expansion this quarter?” The system pulls relevant passages, weighs conflicting signals, and returns a structured answer with supporting evidence. What used to take hours becomes something they can iterate on in minutes. That is the ambition behind AlphaSense’s Generative Search, to make deep, evidence-based research practical in everyday workflows. But enterprise research products face a core trade-off. Higher-quality answers require more planning, retrieval, and evaluation, and each step adds latency. Users expect responses to feel interactive, even as the system does more work behind the scenes. To address this, AlphaSense partnered with Cerebras to power the parts of its platform that rely on many fast, repeated model calls. By reducing the latency of steps like routing, query generation, and evidence evaluation, Cerebras makes it possible to include more reasoning within the same response window. The result is a shift in architecture. Instead of collapsing everything into a single prompt or limiting the number of steps, Generative Search can compose multiple stages of planning, retrieval, and evaluation into a single responsive experience. Business challenge: research has to feel interactive AlphaSense’s users work in environments where research quality and speed both matter. A user may ask for a view on a company’s competitive position, a market trend, a regulatory development, or the implications of a management change. Answering accurately often requires several layered tasks: Identify the relevant companies, people, markets, products, and events Determine which sources are appropriate for the question Retrieve passages from structured and unstructured content Evaluate which snippets are relevant, redundant, stale, or contradictory Synthesize the strongest evidence into a clear answer

Traditional search architectures can return documents quickly, but they leave much of this reasoning to the user. Agentic research architectures can do more of the work, but they introduce a new bottleneck. Each additional reasoning, routing, or evaluation step usually requires another model call. That creates a product and architecture challenge. Users expect the default experience to feel fast, but the highest-quality answers often require more intermediate reasoning. The system has to decide how much work to do before the user notices the delay. Why latency becomes an architecture constraint As the Generative Search team expanded beyond traditional retrieval into planning, routing, evidence evaluation, and adaptive research modes, they encountered a new constraint. Each additional capability typically required another model call, and every model call consumed part of the user's response-time budget. This is where inference latency becomes more than a performance metric. In agentic applications, it becomes an architectural design constraint. The slower each model call is, the fewer model-mediated decisions an application can afford before the experience no longer feels interactive. For systems like Generative Search, that often forces difficult architectural trade-offs: Collapse multiple responsibilities into one prompt. Rather than using specialized models for routing, retrieval planning, evidence evaluation, and synthesis, these tasks are combined into a single prompt to minimize the number of model calls. Replace model-driven decisions with heuristics. Intent classification, tool selection, and workflow routing are handled with static rules or regex because another LLM call introduces too much latency. Evaluate less evidence. Although retrieval may return hundreds of candidate passages, only a small subset can be inspected before synthesis, increasing the risk that relevant or conflicting information is overlooked. Serialize work that could otherwise run in parallel. Independent tasks are executed sequentially to reduce the number of concurrent model calls and simplify latency management. Limit workflow adaptability. Every request follows a similar execution path because dynamically escalating from a lightweight answer to a deeper research workflow introduces unpredictable delays.

For AlphaSense, reducing inference latency changed these trade-offs. Instead of collapsing the workflow into fewer, larger prompts, Generative Search could decompose research into smaller, specialized model calls for planning, retrieval, and evidence evaluation while still delivering an interactive experience. More broadly, this illustrates an important principle for agentic AI systems - Fast inference doesn't simply make an existing workflow faster, it expands the architectural design space. As the cost of individual model calls falls, developers gain the freedom to compose richer workflows, evaluate more evidence, and adapt the execution path to each user's question without exceeding the application's response-time budget. Solution overview Rather than treating every inquiry the same, AlphaSense redesigned Generative Search around a planned, adaptive research workflow. Instead of relying on a single prompt to perform retrieval, reasoning, and synthesis, the system decomposes research into a series of specialized decisions that can be adjusted based on the complexity of the user's request. The first decision is determining how much research the question actually requires. A straightforward question may only need a lightweight retrieval and synthesis path, while a more complex request...