AI automation systems for the work your team still does by hand.
xAGI Labs helps growth, operations, support, and product teams turn repeated manual work into production AI agents, copilots, and integrations that fit the systems they already use.
Best for teams with a real workflow to automate: research, routing, extraction, follow-up, reporting, or product AI.
What happens after you book
Pick a time that works for you
Bring one workflow your team still handles manually
Get a practical automation plan from our team
Workflow design
Start from the job, edge cases, owners, and handoffs before choosing the model.
System integration
Connect data, docs, APIs, CRM, help desk, and internal tools without creating a shadow stack.
Production monitoring
Review outputs, trace decisions, and expand scope only after the workflow holds up.
Automation console
Workflow in production
Review mode
Human
Approval gates stay in place where judgment matters.
Workflow type
GTM ops
Account research, enrichment, CRM updates, and drafted follow-up.
Active workflow
Lead research to CRMFinds company context, fit signals, recent activity, and the reason the account is worth pursuing.
Updates CRM fields, queues a suggested next step, and routes unclear accounts for human review.
Logs sources, output quality, and approval state so the team can inspect what happened later.
Workflow automation
Turn repetitive research, data entry, routing, reporting, and follow-up into reliable AI-assisted workflows.
Internal copilots
Give teams agents that search company knowledge, draft decisions, update systems, and keep context attached.
GTM automation
Automate lead research, account scoring, outreach preparation, CRM hygiene, and handoffs between growth tools.
AI product features
Embed assistants, extraction, generation, recommendations, and decision support inside the products you already ship.
How it works
A practical rollout path for ops teams.
Start with a narrow workflow, connect the systems you already use, then scale once outputs, reviews, and business outcomes are stable.
01
Map the workflow
Find the repeated work, decision points, review rules, success metrics, and fields that need to move between systems.
02
Connect knowledge and systems
Integrate docs, databases, CRM, help desk, spreadsheets, APIs, and the tools your team already works inside.
03
Launch with monitoring
Start with a narrow production path, review outputs and edge cases, then expand once the automation proves useful.
Who this is for
Founders, operators, and growth teams with repeated work across multiple tools
Teams that need agents, copilots, extraction, routing, or GTM automation in production
Product teams adding AI features to real customer workflows rather than demos
Not the best fit
Teams looking for a generic chatbot skin with no workflow behind it
Use cases without a clear owner, input, decision path, or success metric
Projects that only need research slides rather than a working production rollout
Reliability
Designed for workflows that cannot quietly drift or break.
The platform combines grounded AI, tool execution, operational control, and traceable outcomes so your team can trust it in front of customers and internal stakeholders.
What the system handles
- Research, summarization, extraction, and classification
- Knowledge-grounded outputs and tool-triggered workflows
- Structured summaries, action items, and state logging
- Escalation to humans with context preserved
Agent architecture
Purpose-built agents with scoped tools, memory, evals, and review paths instead of one giant chatbot prompt.
Knowledge and data layer
Ground outputs in your SOPs, docs, tables, product data, and business rules with traceable source context.
Workflow orchestration
Trigger actions across CRM, support, email, spreadsheets, internal tools, and custom APIs with clear state handling.
Guardrails and review
Use confidence checks, approval gates, audit logs, and escalation rules for work that needs human judgment.
Use cases
Built for the automations most teams keep postponing.
Lead research and outbound ops
Research accounts, qualify fit, draft contextual outreach, update CRM fields, and keep sales follow-up moving.
Support and back-office triage
Classify requests, summarize history, draft replies, route exceptions, and keep internal teams aligned.
Document and data extraction
Extract structured fields from PDFs, emails, forms, spreadsheets, and messy operational records.
Reporting and decision support
Turn scattered data into weekly summaries, risk flags, next actions, and dashboards your team can trust.
Product copilots and AI features
Add assistants, retrieval, generation, recommendations, and workflow actions directly inside your product.
Enterprise trust
Practical deployment and governance, not hand-wavy enterprise language.
The rollout is designed around operational controls, integration depth, and implementation planning that procurement and ops teams can actually evaluate.
Security, privacy, and deployment constraints are scoped during discovery and pilot planning so the production setup matches the workflow and environment requirements you already operate under.
Cloud-native deployment with a path for stricter environment and data handling requirements
API-first automations for internal workflows, customer-facing features, and scheduled operations
Integrations across CRM, help desk, data stores, messaging, docs, spreadsheets, and automation tools
Structured logs, human review points, evals, and workflow-level observability for production use
Common integrations
Ready to launch
Put one AI automation into production with a cleaner path to rollout.
Start with one operational workflow, validate quality and outcomes, and expand once the system is doing real work for your team.