>_ AI EMPLOYEE INFRASTRUCTURE

We didn't build a chatbot.
We created an AI Employee.

Every Boosteam employee has a goal, memory, feedback loop, clean business context, and a self-healing orchestration layer that can analyze, monitor, and prepare actions across your stack.

Goal-driven
Learns your business
Runs 24/7
Human-approved actions
Product Tour

See the platform in action

Watch Product Tour

0:00
0:00
Persistent memory
Self-healing workflows
Step 1: The Context Layer

Every AI employee starts with a clean understanding of your business.

Most AI products reason over fragmented live APIs and raw tables. Boosteam first builds a trusted data foundation, so the employee reasons on normalized business context instead of noise.

Why this matters

Before an employee thinks, Boosteam ingests, cleans, and joins data from ads, CRM, commerce, analytics, and internal systems into a single source of truth.

Context Window DisciplineEmployees reason over curated, structured context instead of raw API noise.
Cross-Channel Identity StitchingWe reconcile conversions, spend, and downstream outcomes across systems before the employee makes a call.
HubSpot
Sheets
API
LinkedIn
Shopify
Snowflake
Unified
Truth
Meta Ads
Google Ads
Salesforce
Amazon
TikTok
Analytics
Employee-Grade Context
Step 2: The Employee Layer

One platform. A workforce of specialized AI employees.

You are not chatting with a generic model. You are assigning work to employees that already know your business, remember feedback, and operate inside a managed system.

Analyst Employee

On Demand

Ask a question. Get a reasoned answer with memory.

When you need an explanation, the employee pulls business context, historical performance, and prior feedback to answer like someone already embedded in your team.

User: “Why did our CPA on LinkedIn spike yesterday vs last week?”

The employee traces spend, auction pressure, funnel quality, and historical context before answering.

Operator Employee

Always On

Recurring work runs without waiting for a prompt.

Employees monitor channels, investigate anomalies, prepare recommendations, and assemble action packages while your team sleeps.

Assigned Goal: “Monitor paid social and search daily. Surface wasted spend, explain the cause, and prepare a budget shift plan for approval.”

The Analyst

Finds anomalies, patterns, and revenue leaks across your full marketing stack.

The Strategist

Builds forward-looking plans across channels, budget moves, audiences, and timing.

The Executor

Turns recommendations into precise, review-ready action packages with zero manual formatting.

The Communicator

Ships updates, briefings, and dashboards in your format, on your cadence, in your voice.

This is the part most AI products do not have.
Inside every employee is a managed orchestration stack.

Boosteam employees are not a single model with a cute UI. They are domain systems built from many models, layered instructions, memory, validation, routing, and recovery logic.

Clean context first

Boosteam ingests, cleans, and normalizes your data before any model reasons on top of it.

One employee, many systems

Each employee is composed of specialized models, private instructions, tools, and checks tuned for a domain.

Supervisor-orchestrated swarms

Workers investigate, validators verify, and supervisors recover from failures so the job keeps moving.

Human-controlled execution

The AI does the reasoning. Boosteam APIs handle the platform access. Your team still approves the move.

The 50% Workflow
One error = Total Crash
The 99.99% Team
Collaborative Swarms

Reliability comes from team structure, not hope

Fragile automation: One prompt fails, one API response is malformed, or one model drifts, and the workflow falls apart.

Boosteam employees: Specialist workers, validators, and supervisor agents collaborate. If one path breaks, the system retries, re-routes, or corrects itself before the task reaches you.

Employees investigate until the problem is actually solved

Fixed-logic tools: You define the exact steps in advance, so the system only goes as deep as the script you wrote.

Boosteam's way: Employees determine their own depth. If attribution is messy, they keep branching, checking, and synthesizing until the explanation is defensible, not just convenient.

Squad Architecture
3 Agents Active
Supervisor
Employee Orchestrator
Performance Worker
Tracing spend shifts...
Validation Worker
Checking evidence...
Supervisor Recovery Active
Dynamic Investigation Depth
ALL
Models
Zero
Downtime

The stack compounds over time

  • Model Routing: Math, analysis, writing, classification, and planning are routed to the right models for the job.
  • Memory + Feedback: Employees retain preferences, absorb corrections, and come back sharper on the next run.
  • Fallbacks + Control: If a provider fails or confidence drops, Boosteam falls back gracefully and keeps humans in the approval loop.

From account connection to first AI employee in minutes.

Minute 0

Connect Your Stack

Ads, CRM, commerce, analytics, and internal data sources plug into one operating layer.

Minute 1

Build Trusted Context

Boosteam cleans, joins, and normalizes the business context your employee will use.

Minute 3

Assign the Goal

Define what the employee owns in plain language, from monitoring to strategy to reporting.

Minute 5

Deploy the Employee

The employee begins investigating, monitoring, and packaging outputs in your format.

Minute 6

Review and Compound

Approve the recommendation, give feedback once, and make the next run better.

Ready to meet your AI employees?

Replace brittle prompts, dashboards, and manual busywork with a workforce that learns your business and gets sharper every run.