Cybrainic
AI agent architectures, built into production workflows
We design and implement production-grade AI agent systems for B2B teams. Each engagement starts with a focused pilot to validate impact in real workflows.
Pilot overview
6-7 weeks, one critical workflow
Scope, build, validate with measurable outcomes.
Delivery
A deployed AI agent system integrated into your existing stack
Measured results
Execution speed, accuracy, and real usage
Decision point
Scale the architecture - or stop
Typical pilot range
$5k-$15k
Timeline
6-7 weeks
Scope
One production workflow
Why most AI agents fail in production
Agent systems rarely fail because of model quality. They fail due to weak system design and poor integration.
Fragmented system context
Critical state and knowledge live across disconnected tools.
No operational ownership
Agents exist as demos, not production services.
Missing governance
Unclear data lineage, permissions, and execution boundaries.
Who this is for
B2B teams running complex internal systems
10-200 employees
Enough scale for system leverage, small enough to ship.
Process-dense environments
Operations, finance, analytics, customer operations, internal tooling.
Production intent
Teams prepared to run AI agents as part of their systems.
What we build
Production systems - not prototypes.
Agent architectures
Multi-step agents designed around system state, tools, and constraints.
Workflow integration
Agents embedded directly into existing software and operational tools.
Automation & orchestration
Controlled execution across services with defined boundaries.
Secure deployment
Cloud or private environments with logging, access control, and audit trails.
How the pilot is executed
Week 1
System scoping
Map the workflow, system boundaries, inputs, and outputs.
Weeks 2-5
Architecture & build
Design the agent architecture, integrate tools, deploy to production.
Weeks 6-7
Validation
Measure usage, reliability, and operational impact.
What success looks like
An AI system that runs inside your operations - reliably and measurably.
Faster execution
Reduced manual steps and decision latency.
System reliability
Traceable outputs with controlled behavior.
Sustained usage
Agents used daily as part of normal workflows.
Why Cybrainic
Architecture-first, production-driven
Systems engineering mindset
Designed for reliability, not demos.
Operational AI
Clear execution boundaries, permissions, and auditability.
Direct implementation
We build and ship inside your environment.
Operating principles
Architecture, constraints, and stop conditions are explicit.
One workflow
We design around a single production workflow.
Defined constraints
System limits and failure modes are explicit.
Real stop condition
If the workflow cannot be safely automated, the pilot ends.
Have a production workflow blocked by manual steps?
Describe the system and the bottleneck. We'll respond by email with next steps.
→ Send a request (email only)