Built to make AI agents reliable in the real world

Sprig was created to help teams move beyond AI experiments and into real, dependable execution — with control, transparency, and long-term reliability.

Built to make AI agents reliable in the real world

Sprig was created to help teams move beyond AI experiments and into real, dependable execution — with control, transparency, and long-term reliability.

Built to make AI agents reliable in the real world

Sprig was created to help teams move beyond AI experiments and into real, dependable execution — with control, transparency, and long-term reliability.

How It Started

How It Started

We started Sprig after watching small teams struggle with tools that promised intelligence but delivered complexity.

We started Sprig after watching small teams struggle with tools that promised intelligence but delivered complexity.

Most AI products felt impressive in demos—but frustrating in daily use. They demanded too much setup, too much context, and too much trust from the very people they were supposed to help.

Most AI products felt impressive in demos—but frustrating in daily use. They demanded too much setup, too much context, and too much trust from the very people they were supposed to help.

So we decided to build something quieter: AI that works in the background, adapts over time, and helps teams think clearer—not harder.

So we decided to build something quieter: AI that works in the background, adapts over time, and helps teams think clearer—not harder.

Company History

Company History

Today

Building For Scale

Today, Sprig is focused on helping teams move AI agents from experiments into dependable, production-ready systems.

2025

Private Beta

We opened Sprig to a small group of teams to validate real-world use cases across sales, operations, and product workflows.

2024

First Production Use

Sprig was deployed in real internal workflows. This is where guardrails, approvals, and context persistence became non-negotiable.

2023

Early Prototypes

We built internal agent prototypes focused on execution, control, and visibility — not just conversation quality.

2022

The Problem Phase

We started by experimenting with AI tools internally and quickly ran into the same issue every team faced: models were impressive, but unreliable in real workflows.

How We Think About AI

We believe AI should be practical, predictable, and designed to support real work — not operate as a black box.

How We Think About AI

We believe AI should be practical, predictable, and designed to support real work — not operate as a black box.

How We Think About AI

We believe AI should be practical, predictable, and designed to support real work — not operate as a black box.

Human-first by design

Human-first by design

AI should assist people and workflows, with humans always in control.

AI should assist people and workflows, with humans always in control.

Control over autonomy

Control over autonomy

Automation works best when humans can guide, approve, and intervene when needed.

Automation works best when humans can guide, approve, and intervene when needed.

Reliability over novelty

Reliability over novelty

Consistent, dependable behavior matters more than impressive but fragile outputs.

Consistent, dependable behavior matters more than impressive but fragile outputs.

Built for real workflows

Built for real workflows

AI should fit into existing tools and processes, not force teams to change how they work.

AI should fit into existing tools and processes, not force teams to change how they work.

Meet the team

A small, focused team building reliable AI systems for real-world use.

Meet the team

A small, focused team building reliable AI systems for real-world use.

Meet the team

A small, focused team building reliable AI systems for real-world use.

Join Us

Join Us

We’re building a small, focused team around reliability, ownership, and thoughtful AI.
We’re building a small, focused team around reliability, ownership, and thoughtful AI.

Move from experiments to execution

Start using AI agents to run real workflows with control and reliability.

Move from experiments to execution

Start using AI agents to run real workflows with control and reliability.

Move from experiments to execution

Start using AI agents to run real workflows with control and reliability.

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