Custom AI systems
Automate the work your software can't.
We build custom AI systems and workflow automations that handle complex operations, integrate with your existing stack, and move work from input to outcome.
Built around your existing stack
No rip-and-replace required.
01 / Received
RUN / 00842
- Attachment
- request_8421.pdf
Your software handles records.Your team still handles the gaps.
CRM, ERP, email, documents, databases, internal tools — most businesses already have the software they need. The difficult work happens between those systems.
People collect context, apply rules, move information, chase approvals, handle exceptions, and decide what should happen next.
That's the layer we automate.
The expensive work is usually between the systems.
The process may look automated from the outside. Inside the business, people are still stitching it together manually.
Request received
A customer or internal request arrives by email. Someone needs to identify what it is and where it belongs.
Manual triage
Context missing
The request needs information from three different systems before anyone can act.
Manual lookup
Decision waiting
A team member needs to interpret policy and decide the next step.
Human bottleneck
Approval stalled
The workflow is waiting in someone's inbox. Nobody has followed up.
Process delayed
Systems out of sync
The action was completed in one platform but two others still need updating.
Manual reconciliation
Caldris turns these handoffs into a system.
What we automate.
Operational patterns we build across industries — starting with the workflow that creates the most friction.
Process incoming work
Turn unstructured inputs into structured action.
Read emails, forms, documents, messages, and other incoming requests. Extract the relevant information, validate it, classify the work, and route it into the correct process.
Extraction · Classification · Validation · Routing
Systems we build.
Capabilities behind the workflows — detailed on our Services page.
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Custom AI Systems
Purpose-built AI applications designed around a specific operational problem.
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Workflow Automation Systems
End-to-end processes spanning people, data, decisions, and software.
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AI Agents
Agents that reason, use tools, retrieve context, and perform clearly defined work.
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AI Integration & Infrastructure
Secure integration between AI systems and the data, APIs, applications, and infrastructure they depend on.
Built around the stack you already have.
We don't start by replacing your systems. We connect the workflow across them.
Business systems
- CRM
- ERP
- Operations software
- Internal tools
Communication
- Messaging
- Notifications
Knowledge
- Documents
- Policies
- Knowledge bases
- Files
Data
- Databases
- Warehouses
- APIs
One workflow. Every handoff coordinated.
Illustrative operational sequence — not client performance data
Request received
Email and supporting document detected
Request classified
Category identified · Priority assigned
Context retrieved
Customer record found · Historical data loaded · Relevant policy retrieved
Required fields validated
One discrepancy detected
Exception resolved
Supporting record checked · Correct value confirmed
Action prepared
System update ready
Approval requested
Assigned to Taylor Morgan
Approved
Workflow resumed automatically
Systems updated
Three records synchronized
Confirmation sent
Stakeholders notified
Workflow completed
Audit trail recorded
Automation is useless if we can't measure what changed.
We define the baseline before we automate the workflow.
Throughput
- Processing time
- Queue time
- Completion rate
Quality
- Error rate
- Exception rate
- Rework rate
Operations
- Manual touches
- Approval time
- Escalation volume
Business
- Cost per process
- Time-to-outcome
- Capacity recovered
Metric categories we establish per engagement — not fabricated results.
Good automation starts with the right problem.
We do not automate something simply because AI can touch it.
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High repetition
The workflow happens often enough that friction compounds.
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Multiple systems
People spend time gathering or moving information between tools.
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Decision points
The process requires rules, judgment, context, or interpretation.
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Exceptions
The workflow has edge cases that simple automation cannot handle well.
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Clear outcome
There is an objective definition of what successful completion looks like.
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Measurable cost
Delay, manual effort, error, or lost capacity can be quantified.
Start where the work gets stuck.
We don't begin by asking where you want AI.
We map where work waits, where people manually coordinate systems, and where decisions slow the process down.
- 01 / Find
Map the bottleneck
Understand the workflow, systems, people, rules, exceptions, and measurable cost.
- 02 / Prove
Automate one measurable workflow
Build the smallest useful system that demonstrates whether the approach works.
- 03 / Harden
Make it production-ready
Handle edge cases, permissions, observability, failure modes, security, and human escalation.
- 04 / Expand
Connect adjacent workflows
Extend the system only after the initial automation proves valuable.
Bring us the workflow nobody wants to own.
The process spread across inboxes, spreadsheets, software, approvals, documents, and someone's memory is usually where we start.