# autobound
Source: https://speakeasy.com/customers/autobound


import { Card } from "@/mdx/components";

In the world of sales & marketing, personalization is king. [Autobound.ai](https://www.autobound.ai) enables teams to create hyper-targeted content at scale by combining 350+ data sources.

However, leveraging mountains of data presents it's own problems. Problems that Autobound solved with an MCP server built on [Gram](/docs/gram/introduction).

## The limits of determinism

For every prospect, Autobound's system has anywhere from 20 to 100 relevant insights available, from SEC filings to podcast appearances to LinkedIn activity. The challenge is figuring out which insights to draw on for each unique sales conversation, dynamically fetching additional context based on how the conversation progresses.

That's a problem that required moving beyond deterministic algorithms into truly agentic decisioning. The algorithm could pick from available insights, but it couldn't reason about them. For that, Autobound needed MCP.

<Card
  variant="testimonial"
  className="my-10"
  quote="There were thousands of possible API request patterns across our Insights API, building deterministic logic for every scenario was impractical. We needed a reasoning layer that could intelligently navigate our API and make decisions about which insights to fetch. MCP was exactly what we needed."
  author={{
    name: "Daniel Wiener, CEO, Autobound.ai",
  }}
/>

## MCP in days not weeks

What Autobound wanted was straightforward: expose the core of their Insights API through an MCP server so that their [Vellum-based agents](https://www.vellum.ai/products/orchestration) could dynamically fetch insights based on context.

Daniel Wiener, CEO of Autobound.ai, suspected that building it wouldn't be quite so straightforward.

<Card
  variant="testimonial"
  className="my-10"
  quote="I didn't want to block our engineering team with the creation or maintenance of the MCP server. It's not just learning the protocol. Authorization, error-handling, infrastructure, scaling, all of that is a distraction from moving our core product forward."
  author={{
    name: "Daniel Wiener, CEO, Autobound.ai",
  }}
/>

Gram changed the equation entirely. Autobound was able to easily transform their Insights API into a set of tools and deploy them as an MCP server.

<Card
  variant="testimonial"
  className="my-10"
  quote="Gram made it incredibly easy to build and deploy an MCP server. We went from concept to a working MCP server in days."
  author={{
    name: "Daniel Wiener, CEO, Autobound.ai",
  }}
/>

## Pushing personalization forward with MCP

After integrating Vellum's agents with their Gram-powered MCP server, something fundamentally different became possible. Now the agent determines how to structure tool calls based on context. It analyzes the returned insights and reasons about what additional data might improve content quality. It makes subsequent calls dynamically based on its reasoning, and Gram's self-healing properties ensure that tool calls have a high success rate.

<Card
  variant="testimonial"
  className="my-10"
  quote="MCP used within the agent orchestration is interesting because the system is determining how to structure the API requests and it's super dynamic. That's a layer of intelligence that did not exist before."
  author={{
    name: "Daniel Wiener, CEO, Autobound.ai",
  }}
/>

For a company whose core value proposition is having "the best insights to write the best content," this shift from deterministic selection to intelligent orchestration represents a fundamental evolution in how they deliver on that promise.

## The reality of production scale

Getting an MCP server working in development is one thing. Scaling it to production volumes is another challenge entirely.

Every email generated requires 5-10 tool calls through the MCP server to fetch and orchestrate insights. At full scale, that translates to millions of API interactions daily, all needing to happen fast enough to feel instant to sales teams working through their outreach sequences.

This kind of scale exposes issues that never surface in testing. Connection pooling becomes critical. Error handling needs to be bulletproof, a failed tool call can't break an entire email generation flow. Latency matters when you're chaining multiple API requests together. And all of this needs to work reliably while Autobound's agents are dynamically deciding which subsequent requests to make based on previous results.

The partnership with Speakeasy became essential here. Autobound stayed focused on tool & prompt design while Speakeasy handles the infrastructure to ensure the MCP server scales appropriately.

## Going live and looking ahead

Autobound's first version went live within days of starting with Gram. The full rollout is planned over the coming weeks as they continue to refine their insight selection logic and scale up volume.

The focus now is on monitoring the results. Does intelligent orchestration actually deliver better insights? Does better insight selection lead to better content? The directional indicators suggest yes, but Autobound is taking the time to measure properly.

But the fundamental transformation is already clear. Autobound moved from a static algorithm to an intelligent reasoning system without blocking their engineering team or breaking their economics at scale. They went from exploring MCP through failed ChatGPT suggestions to production deployment in weeks, not months.

Learn more about building production MCP servers:

- [Gram Documentation](/docs/gram/introduction)