CRM and file system
Combine form context, pipeline fields, notes, and source files so the next action is easier to see.
AI system service
Custom AI around your tools
Use this when your own tools, files, data, and rules can make weekly growth work easier to run.
01
Map the work
02
Plan the build
03
Review the output
Direct answer
Custom AI connects the work your tools cannot handle alone.
Use Custom AI Systems when the workflow needs company data, CRM fields, files, forms, orders, content, or reporting to work together. This is the right service when an agent by itself is not enough because the useful answer lives across tools and the team needs a durable operating view.
Reporting system
Maintain KPI memory, weekly review notes, claim checks, and dashboards around the numbers the team trusts.
Operations workflow
Route requests, enrich records, prepare updates, trigger reviews, and keep exceptions visible.
When this helps
Use this when the work points here.
Best for
Teams with useful CRM, transaction, customer, product, content, or operations data that can improve sales, service, reporting, or prioritization.
Use when
The answer your team needs is spread across a CRM, a folder of files and a dashboard.
Possible next step
Strategy and agents stay in the plan when they are the simpler path to the same useful AI system.
What it fixes
A focused build the team can judge.
Fixes
- Data trapped across tools
- Manual decision workflows
- Weak account prioritization
- No useful weekly system view
Ships
- Custom workflow spec
- Data and integration plan
- Decision logic or model layer
- System dashboard
Planning checks
What we look at before wiring anything.
Custom work earns its cost when the answer lives across tools. The intake looks at where your data sits and who can grant access to it.
- Data sources
- CRM fields
- Example outputs
- Team workflow
- Review owner
- Operational gap
Inputs
What a custom system connects to.
A build is only as good as the records behind it. Field maps, example decisions and access rules come before any code.
- CRM and field map
- File and data sources
- Workflow rules
- Example decisions
- Human review moments
- Dashboard or report needs
Buyer questions
What buyers want settled first.
Teams weighing a build usually want these answered first.
When does a business need a custom AI system?
A custom AI system makes sense when the useful output depends on company-specific data, CRM fields, files, forms, orders, dashboards, rules, or integrations that a generic tool cannot see.
What does Custom AI Systems work usually include?
It usually includes workflow mapping, data and field review, source setup, integration planning, agent or model behavior, review gates, dashboards, and an operating view the team can inspect.
How is custom AI different from a normal automation?
Normal automation follows fixed steps. Custom AI can read messy context, prepare a draft or recommendation, explain its evidence, and route exceptions to people while still keeping deterministic rules where they belong.
Agent connection
The agent that runs on top.
A custom system usually feeds an agent. These pages show the work each one prepares once the plumbing is in place.
Report Agent
For KPI memory, weekly operating reviews, dashboards, and claim-safe reporting.
View agentSales Agent
For account context, lead qualification, pipeline review, and sales handoff prep.
View agentClient Agent
For client workspace memory, delivery updates, health checks, and proof packaging.
View agentNot for
When custom work is too early.
- Vague "build us something" requests
- Data-poor use cases
- Custom work before the workflow is understood
Next step
Map the system before building it.
Show us the tools the work touches, and the free plan comes back with the connections it needs and what the first version should do.
Build my AI system