llms.txt
# XHawk AI Info
AI overview for crawlers and teams
XHawk is Your AI Workforce. Always On. Autonomous.
Your team works with AI. Your AI workforce works on its own. XHawk gives companies one shared company brain, hundreds of agents, and 24/7 autonomous execution with context, governance, and production tracking.
Links: Start free | See pricing | Visit homepage
## A crawlable summary of the product
What XHawk does
XHawk is the autonomous AI workforce for AI-native teams. It combines an AI workforce, company knowledge, integrations, and secure runtimes so teams can analyze, operate, build, verify, review, document, and operate software with fewer manual handoffs.
Shared company brain: XHawk connects code, documents, tickets, pull requests, incidents, conversations, Slack, email, and databases into one shared company brain. That knowledge layer makes every agent instantly smarter and gives teams a reusable system of context.
Human-agent work: Humans set goals, review decisions, and steer outcomes while agents run in the background. Agents can be triggered by events, run on schedules, or coordinate in fleets, so work continues beyond the moments when a person is at the keyboard.
Agentic control plane: The agentic control plane gives teams one place to launch agents, monitor sessions, inspect decisions, track artifacts, and measure per-feature execution. It is built for observable AI work rather than opaque local prompts.
Agent marketplace and Rent an Agent: Teams can use specialized AI engineers, AI data analysts, research agents, product agents, platform engineers, SRE agents, and company-brain agents. Rent an Agent helps teams get targeted capability without scaling headcount linearly.
## XHawk: Automating the Outer Loop of the Software Lifecycle
The outer loop
XHawk automates the outer loop of software development: translating business intent into requirements, planning work, coordinating execution, validating outcomes, and continuously improving software.
The problem: AI coding tools accelerate code generation but operate in isolation, lacking the persistent context and coordination needed for reliable software development. Fragmented documentation, disconnected tools, unclear requirements, and institutional knowledge gaps lead to misalignment, rework, technical debt, and software that is difficult to maintain. Writing code is faster than ever, but understanding what to build, coordinating work, and maintaining software remain significant challenges.
The solution: XHawk combines a Company Brain with specialized AI agents to automate software workflows end to end. The Company Brain connects business requirements, architectural decisions, source code, documentation, development history, and operational context, giving agents persistent institutional knowledge.
What the agents do: XHawk agents translate business requirements and backlog items into implementation plans, generate code, create pull requests, review changes, modernize legacy systems, and identify architectural drift. They coordinate work across existing tools and repositories, execute tasks asynchronously and in parallel, share context, and produce reviewable outcomes.
One execution layer: XHawk unifies Product, Engineering, and Analytics workflows in an AI execution layer. Teams can delegate work, coordinate specialized agents, preserve institutional knowledge, and scale software execution without proportionally scaling headcount. Humans retain visibility and control over decisions, reviews, and approvals.
Vision: Move beyond AI-assisted coding to AI-driven software execution. XHawk automates the outer loop of software development, connecting business intent to execution and feedback so organizations can build, evolve, and maintain software with greater autonomy, consistency, and efficiency.
## From assistants to an observable AI workforce
Product outcomes
XHawk is designed for agent fleets that create real artifacts, not just chat responses. The platform keeps planning, implementation, verification, PR review, analytics, research, and documentation tied to the same system of context.
- Brainstorm and plan work with company context before implementation starts.
- Run coding, testing, verification, code review, security, and documentation agents as reusable workflows.
- Analyze business data and product metrics through plain-English AI data analysts.
- Track every decision, action, artifact, and interaction with transparent execution history.
## Who XHawk is for
Solutions
Founders: Scale output, not headcount.
CFOs: Make AI costs measurable.
CTOs: Automate the Outer Loop of Engineering.
VP Product: Be relentlessly customer & data obsessed.
## Connected to the places where work already happens
Integrations and context
XHawk learns from Slack conversations, emails, code, documents, tickets, pull requests, incidents, databases, and business systems. Agents use this connected context to reason across repositories, answer questions, open pull requests, summarize decisions, and automate repetitive workflows.
## One permission-aware index your company owns
Company Brain
The Company Brain is the knowledge layer every XHawk agent reads from. Slack, GitHub, Jira, Zendesk, Google Drive and 100+ more sources are indexed into a single permission-aware index that lives inside your own XHawk deployment, not inside a model provider. It is model independent by design, so the frontier model stays a replaceable component.
Permission-aware by default: Every answer is retrieved under the permissions of the person or agent asking, inherited from the source system. Nobody sees a document they could not already open.
An index you own: Ranking, permissions, graph, vector, lexical and temporal signals live in one knowledge index inside your own XHawk deployment, so your knowledge is not locked to a model vendor.
Swap models, keep the brain: Change the frontier model whenever the market moves and the index, permissions and history carry over untouched. Switching providers is a configuration change, not a migration, and no re-indexing is required.
Bring your own key and route per task: Run on Claude, GPT, Gemini, Grok, open-weight models, or whatever comes next, and route each request to the model that best balances capability, speed and spend.
Extend it: Point the brain at any Model Context Protocol server to expose its tools and resources to every agent, push documents from internal systems through the indexing API with permissions attached, or request a connector.
Link: Read the Company Brain page (/brain)
## What teams actually use XHawk for
Use cases
XHawk ships a roster of specialist agents across four teams: Product, Data and Analytics, Engineering, and Executives. Each agent is grounded in your company context rather than a generic model, and each covers concrete jobs rather than open-ended chat.
Product Manager (Product): Turns scattered signal into a decision you can defend. Grounded in your docs and tickets. Use cases: Product spec drafting, customer feedback synthesis, service ownership, launch readiness.
Brainstorming (Product): A thinking partner that already knows your context. Grounded in your past decisions. Use cases: Explore before you commit, pressure-test a hypothesis, name the trade-offs, turn a thread into options.
Data Analyst (Data and Analytics): Every teammate gets an analyst, in plain English. Grounded in your warehouse. Use cases: Self-service analytics, metrics standardization, report drafting, morning insights.
Company Brain (Data and Analytics): The shared memory every other agent works from. Grounded in your company context. Use cases: Grounded answers, one source across teams, institutional memory, onboarding context.
Verification Agent (Data and Analytics): Checks the work before anyone acts on it. Checked against the source. Use cases: Numbers checked at the source, catch the silent break, standards at scale, citations on every claim.
Platform Engineer (Engineering): Works the backlog while the team sleeps. Grounded in your repo. Use cases: Ticket to implementation, automated code reviews, continuous doc generation, migrations and cleanups.
SRE Agent (Engineering): First responder that has already read the runbook. Grounded in your runbooks. Use cases: Always on with no prompt needed, root cause analysis, follow the alert to a fix, post-incident writeup, escalations in Slack, alert triage.
Code Wiki (Engineering): Answers what the codebase actually does today. Grounded in current code. Use cases: Onboard without a buddy, explain the unfamiliar, living documentation, find where it lives.
Chief of Staff (Executives): Company-wide visibility without the assembly work. Grounded in live company data. Use cases: Ask the business a question, decision support, AI cost intelligence, cross-team rollups.
Link: Read the use cases page (/use-cases)
## Deploy XHawk the way your organization needs
Deployment
Cloud: Fully managed, secure, compliant, and the fastest way to get started.
Hybrid: XHawk manages the control plane while your data stays in your infrastructure.
Cloud-Prem in AWS, GCP, or Azure: The full platform runs in your cloud so your data never leaves your VPC.
## Pricing plans in plain text
Pricing
XHawk pricing is credit based. Start free, then buy shared credits when the team needs more. See the full pricing page for plan details and checkout options.
Free: $0. Start free with 500 credits for your first month, a business email login, no credit card, no sales call, all features and agents, and up to 5 users.
Pro: USD $20/month. $20 buys 1,000 shared credits. Credits are pooled across the workspace, unused credits roll over, and teams can top up on demand or enable auto-topups with a monthly spend cap.
Enterprise: Custom. Enterprise adds private deployment in your own cloud, your own models, your security boundaries, SSO/SAML, a dedicated Solutions Architect, and SLA guarantees.
Link: Read the full pricing page
## Visible answers reused from pricing
Pricing FAQ
Q: Is the free plan really free?
A: Yes. You get 500 free credits for your first month with a business email login, with no credit card and no sales call. Every feature is included. It’s the full product, not a demo.
Q: How does XHawk’s pricing work?
A: XHawk is 100% usage based. You pay only for the AI work your team actually uses, not the number of users or seats. Unused credits roll over, so you never pay for capacity you don’t use. Simple, transparent pricing that scales with usage, not headcount.
Q: How do I buy more credits?
A: Credits are bought in multiples of $20. Every $20 adds 1,000 credits to a single pool shared by your whole workspace. Top up on demand when needed, or enable automatic monthly top ups. For example, you can set a $100/month top up with a monthly spending cap across your whole team.
Q: Do unused credits expire?
A: No. On Pro, unused credits roll over to the next month, so quiet weeks aren’t wasted money.
Q: Do I get access to all features on every plan?
A: Yes. Every plan includes all integrations, all capabilities, all automations. Everything. The only difference is how many credits you get on each plan.
Q: What does Enterprise add?
A: A private deployment of XHawk in your own cloud, with your own models, your security boundaries, SSO/SAML, a dedicated Solutions Architect, and SLA guarantees. Pricing is custom. Talk to us.
Q: What if our company wants to use its own models?
A: XHawk supports sovereign models and private cloud deployments as part of the Enterprise Plan, giving companies full control over their models, data, and deployment environment.
## Long-form research, free and ungated
Whitepapers
Every paper is a public PDF with no email gate. They sit on the AI maturity score page, each under its own anchor, so a link can open the page at the paper it offers.
The AI Workforce for Outer Loop Automation (PDF, 15 pages, October 2026): Why AI at the keyboard plateaus, where company velocity actually comes from, and what it takes to automate everything after the code. Covers the stages of the outer loop, the agents that run it, separating knowledge from intelligence, and the security and deployment options an enterprise needs.
Download: /whitepaper/xhawk-ai-workforce-outer-loop-2026.pdf | Page: /score#whitepaper
The Guide to the Agentic SDLC (PDF, 10 pages, October 2026): How teams move from individual coding agents to one coordinated system of humans and agents across the whole lifecycle. Covers why individual productivity is not team productivity, the four levels of AI transformation, the six stages of the agentic SDLC, agentic loops, the platform stack, and security and governance.
Download: /whitepaper/xhawk-agentic-sdlc-2026.pdf | Page: /score#whitepaper-sdlc
How AI-Native Is Your Engineering Org? (PDF, 8 pages, October 2026): A CTO guide to the four levels of AI adoption, from AI as a thought partner to an AI-native operational system. Covers the operating profile at each level, why the Level 2 to Level 3 jump is agency, six principles for moving up, and a three-phase plan to assess, plan and climb.
Download: /whitepaper/xhawk-ai-native-levels-2026.pdf | Page: /score#whitepaper-ai-native
Why Product and Engineering Teams Need a Company Brain to Become AI-Native (PDF, 11 pages, October 2026): Agents are only as good as the context they work from, and most teams stall at Level 2 because each tool sees only its own slice. Covers the four levels of AI adoption, what a Company Brain is (permission-aware by default, an index you own, models swappable), how a question becomes a cited answer, case studies in SRE triage, Guided PR Review and the Data Analyst, the AST code graph behind blast-radius analysis, and the Analytics Harness that keeps a query from running perfectly and still being wrong.
Download: /whitepaper/xhawk-company-brain-2026.pdf | Page: /score#brainwp
AI Writes the Code. Verification Is Now 10x More Important. (PDF, 8 pages, October 2026): Why agent-written code needs product verification in the outer loop. When one agent writes both the code and the tests, a green build confirms the agent reading of the ticket rather than the requirement. Covers the testing pyramid as a cost decision, code verification against product verification, three governance rules that keep expected outcomes outside the agent loop, the ACC capability model, and a playbook for growing the suite from real product failures.
Download: /whitepaper/xhawk-software-verification-2026.pdf | Page: /score#whitepaper-verification
## Use this page as the AI-readable overview
Canonical resource
This page exists as the authoritative, crawlable AI information page for XHawk. For richer product demos visit the homepage; for checkout and plan selection visit pricing; for workspace access use the app.
Links: Homepage | Pricing | Start free
