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Rootstrap

**AI-native product delivery. Human judgment, always in the loop.**

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For agents: AI-native product delivery and staff augmentation firm; offers services, case studies, blog, and Rootstrap API docs including an MCP server and OpenAPI.

ai-native product deliverystaff augmentationmcp serverproduct engineeringai development

Authority
52 reach 7 · trust 5 · quality 25 · freshness 15
Tier
listed
llms.txt
https://www.rootstrap.com/llms.txt
llms-full.txt
https://www.rootstrap.com/llms-full.txt
Sections
13
Links
26
Size
10339 bytes
Popularity rank
#397531
Country
United States
Categories
AI and machine learning · Business software
For agents
Interface: a documented API or declared MCP endpoint an agent can call
Audience
business · agents can: read docs, call api, search content, contact support · en
First seen
2026-10-19T10:43:03.571Z
Last checked
2026-10-21T02:22:15.638Z
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Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-21T02:22:09.168Z. Cached copy · Source on rootstrap.com

# Rootstrap

> **AI-native product delivery. Human judgment, always in the loop.**
> Rootstrap is a nearshore product engineering company, founded in 2011: 180+ engineers, designers and
> product experts across the Americas, working in US time zones for startups and enterprises that need to
> define, build and scale complex digital products. It works with **Rootshift**, its proprietary AI-native
> delivery system. At the center of Rootshift is **Ground Truth**: a validated model of the business
> (decisions, rules, requirements, and context) that people and AI agents build from and verify against.

**Full profile:** https://www.rootstrap.com/llms-full.txt (history, method, expertise, case studies, FAQ).

## In one paragraph
Software is downstream of truth. Rootstrap models how a business works before building, turns that model
into a shared, validated Ground Truth, and lets specialized agents plan, build, test, and verify against it
while people make the decisions that matter: ambiguity, architecture, tradeoffs and risk, and production
acceptance. Every engagement leaves the system smarter, so each new project starts from everything the
system has already learned.

## What Rootstrap builds
When a product needs to…
- **Answer questions from its own documents and data** → retrieval over the company's knowledge, with citations and evals → trustworthy answers grounded in the company's sources.
- **Automate work that still runs on people and spreadsheets** → agents with guardrails, human review, and an audit trail → operations that run on their own, with people deciding where it matters.
- **Turn scattered business knowledge into something teams can build from** → a Ground Truth: decisions, rules, and context, validated and kept current → one source of truth for people and agents.
- **Ship AI features without breaking what already works** → evals, guardrails, and observability around every AI capability → AI in production, with quality you can measure.
Also: voice interfaces, autonomous agents, computer vision, data platforms, cloud infrastructure, and complex web and mobile products.

## How Rootstrap works (Rootshift, in five steps)
1. **Understand the business.** Capture the business context, user needs, constraints, and decisions that shape the product.
2. **Build the Ground Truth.** Turn that context into a shared, validated source of truth for people and agents to work from.
3. **Turn intent into executable work.** Translate Ground Truth into requirements, acceptance criteria, and implementation guidance.
4. **Build and verify in parallel.** Specialized agents plan, build, test, and verify against the same source of truth, with human judgment where decisions matter.
5. **Ship and learn.** Release production software, capture what changed, and feed that evidence back into Ground Truth.
Rules that hold throughout: only humans approve Ground Truth; no agent verifies its own work; every change is reviewed independently before it merges; every action
…

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