AI enablementAgents for business. Agents for people.

Every business will run on agents.
We build yours.

AnovaGrowth helps teams design AI-assisted workflows for lead follow-up, customer replies, scheduling, and CRM preparation in the systems they already use. Each workflow starts with source context, a clear action boundary, and a human review path.

Book an AI Operations Audit
See agents at work

The audit maps the workflow, source context, decision points, and the first release worth evaluating before you commit to anything.

01Find the high-value work02Connect the tools03Run it with human oversight
Source
Context stays attached
Requests begin with visible inputs
Prepare
Actions stay reviewable
Draft before anything changes
Owner
Approval remains explicit
A person decides the next move
Trace
The workflow stays inspectable
Decision paths are designed up front
Operating friction

The work slows down when context gets lost.

Most operations problems look ordinary at first. A request waits, an update is copied, or the team cannot see the decision that led to the next step. Start by making that path clear.

Response path

Requests wait when the next owner is unclear.

A useful operating path captures the request, prepares the next step, and makes the owner visible before the context goes cold.

Repeated work

Routine context gets copied between tools by hand.

Map the inputs once, define the repeatable action, and preserve review where judgment is still needed.

Connected context

Customer context fragments across the tools the team uses.

The goal is not another dashboard. It is a clear path that carries the right source context to the right decision.

The first useful system is the one that makes a real handoff easier to understand, review, and own.

Working Demo

Watch the handoff, then try the agent.

This interface is live. Ask the agent to inspect, prepare, or explain a task. The workflow keeps its context visible and preserves human approval before action.

AnovaGrowth agent
Live · no scripts, real model
Online
Hey, I’m the AnovaGrowth agent. Ask me how I’d run your lead follow-up, replies, or scheduling. Real model, real answers, and I always stop for a human before anything actually goes out.
An

6 live demo messages remaining · no access to real systems · humans approve everything real

Live API response. Six-message preview. No action leaves this demo.

What We Run

One operating layer, run by us

From lead response to customer operations to the websites that feed them, we build the agents, wire them into your systems, and run them, so your operations move faster without adding headcount.

Managed agents

AI agents that prepare, then ask.

Customer support, lead qualification, and next-step work with source context and an explicit human approval boundary.

Lead responseCustomer supportApproval gates

Custom software

The operating tool you cannot buy.

Focused portals, dashboards, and internal systems shaped around the work your team repeats every day.

Client portalsInternal toolsConnected APIs

Web systems

A website that starts the work.

Search, proof, conversion, and intake designed as one visitor path, then connected to the follow-up system.

Search structureConversion pathsLead intake

Workflow automation

Handoffs without copy-paste.

Systems stay in sync, the right person sees the right context, and routine work moves with a visible record.

CRM handoffsSource recordsReviewable actions

Agent Skills Marketplace

Verified, install-ready capabilities any AI agent can use, built and tested by us.

Browse skills
Designed around the handoff

One operating path, clear from request to review.

Your site, operations tools, and managed AI can work from the same context, with a defined owner at every decision point.

One system

Keep the request, the next action, and the review together.

A useful system does not make the team guess where a request came from or who owns it next. The workflow preserves that context and makes approval explicit.

Operating scenarioPreview only
Request context attached
Source and visitor intent visible
Next action prepared
Draft stays reviewable before sending
Owner review defined
No booking, notification, or CRM write in this preview
Connected operating path

A workflow that stays inside your operating tools.

Choose a stage to inspect the local scenario. The path keeps source context visible and stops for human review before any external action.

See how managed AI works
Local scenario outputSource and request context are visible.

This preview does not read from, write to, or notify any external tool.

Start With An Audit

Find the workflow worth evaluating first

The AI Operations Audit maps the workflow, source context, decision points, and the review boundary for a useful first release. Start with a short conversation to see whether it fits.

A written first-release plan, not a generic pitch
Source, handoff, and review notes
No obligation to continue into implementation
AI agents, custom software & automation
Fixed-price quotes, no hidden fees
30-day post-launch support
The ROI Math

What reclaimed time is actually worth

Automation buys back hours every week. Move the sliders to see what those hours add up to in a year.

10hrs
$100/hr

That's 520 hours a year back, at $100/hr, worth $1,000/week.

Reclaimed per year
$52,000

Saving 10 hours a week reclaims $52,000 a year.

See what we can automate
How It Works

Four Steps. No Drama.

Every project follows the same playbook, so you always know what comes next.

Discovery Call

30 min

30-minute call to understand your bottlenecks, tech stack, and what success looks like. No pitch, no pressure. We'll tell you straight if we're a fit.

Business goals & pain points
Current tech audit
Budget & timeline alignment

Strategy & Scope

3-5 days

A detailed proposal with architecture, timeline, and one fixed price. No hourly billing, no surprise invoices. You approve before any code gets written.

Solution architecture
One fixed price
Clear deliverables

Build & Iterate

4-8 weeks

7-day sprints with a working demo every Friday. You review, give feedback, and we adjust. We handle infra, deploys, and technical decisions.

Weekly working demos
Your feedback shapes each sprint
We carry the technical load

Launch & Optimize

Ongoing

Full production deployment plus 30 days of active support. DNS, SSL, integrations, training, then we stick around to optimize on real usage.

Production deploy handled
30 days of support included
Optimization from real data
From the Blog

Insights on AI & Automation

Practical guides from our team on the AI tools, models, and strategies actually working for businesses right now.

A founder working late at a small desk on a starry hillside as a rocket launches in the distance, symbolizing steady focused work paying off
Your Move

Ready to put AI to work, not just chat with it?

Start with an AI Operations Audit. We map the workflow, source context, decision points, and review boundary for a useful first release before you commit to a build.

Book an AI Operations Audit
Review

Each audit produces a practical first-release plan and a defined decision path.

Decision artifact: Scope before the build
FAQ

Things People Ask Us

Real questions from real conversations before, during, and after projects.

AI automation can prepare repetitive work such as qualification, routing, data capture, scheduling, and draft responses. The useful starting point is a specific workflow, its source information, and the review boundary before any action is connected.
It depends on the workflow, data access, integrations, review controls, and release plan. A scoped conversation establishes the useful first release and the written estimate for that engagement.
Rule-based bots follow defined paths and conditions. AI-assisted chat can interpret a broader range of language and prepare a response from approved context, but it still needs source controls, escalation rules, and evaluation before customer-facing use.
Timing depends on the work, source access, review requirements, and the release boundary. The scope defines the delivery cadence and review points before implementation begins.
We are most useful when a team has a real operating bottleneck, a decision owner, and enough context to define a safer first release. Company size matters less than the clarity of the workflow and the willingness to review how it changes.
The stack is selected around the workflow, source systems, security needs, operating ownership, and long-term maintenance. Common options include modern web frameworks, managed data services, automation platforms, and scoped integrations where they fit.
Many CRMs, helpdesks, and collaboration tools can be evaluated for integration. Each connection depends on the available access, data model, permissions, and review rules, so the useful path is to map the specific handoff before promising an automation.
Post-launch support is defined around the release, handoff, and operating model in the written engagement. Monitoring, adjustments, and ongoing evaluation are scoped when they are useful for the workflow.
A useful measurement plan identifies the baseline, decision metric, source of truth, and review cadence before a release. Metrics can include response time, handoff completeness, workload reduction, or conversion, but only when they can be measured credibly.
We focus on the operating details around a useful release: the source information, action boundary, human review, ownership, and measurement plan. The work stays grounded in a written scope instead of a generic automation promise.