Agentic AI Consulting & Infrastructure

Turn AI experiments into systems that perform inside your business.

Createting designs, integrates and operates agentic AI systems across models, company data, tools and workflows—without locking your business into another isolated AI tool.

Enterprise integrationsEU-ready infrastructureHuman oversightModel-independent architecture
The real problem

Most AI initiatives create more tools—not more operational leverage.

Teams test copilots, automations and models, but the systems remain disconnected from processes, permissions, data and measurable outcomes.

01

Fragmented stack

Separate chatbots, voice tools, automations and model providers create duplicated cost and inconsistent governance.

02

No reliable execution

AI can generate answers, but cannot safely complete work across CRM, ERP, support, browser and internal systems.

03

No path to production

Proofs of concept stall because ownership, deployment, monitoring, security and continuous improvement remain unresolved.

One implementation partner

Strategy, platform and infrastructure—designed as one operating system.

The engagement starts with the business process, not a predetermined model or vendor. Createting maps the highest-value workflows, builds the required agent system and operates the underlying stack.

Explore the governed enterprise AI agent platform →
Discover

Find the workflows worth automating

Process analysis, opportunity scoring, feasibility, risk, data access and a prioritized implementation roadmap.

Design

Define the agent architecture

Agents, tools, knowledge, permissions, human approvals, model routing, integrations and measurable success criteria.

Deploy

Integrate with the existing business

Connection to CRM, ERP, support, telephony, databases, browser workflows and internal systems.

Operate

Improve performance continuously

Monitoring, governance, model optimization, workflow updates, support and expansion into additional processes.

Where value appears

Build systems around revenue, cost and operational risk.

Revenue

Convert demand faster

Respond to leads, qualify opportunities, schedule appointments and support sales teams across channels.

Efficiency

Remove repetitive operations

Handle support, research, documentation, data entry, browser work and cross-system coordination.

Retention

Act before customers leave

Detect risk signals, trigger personalized outreach and coordinate the right next action automatically.

Control

Govern autonomous execution

Define permissions, approval thresholds, audit trails and human escalation for every agent action.

Why Createting

Not another automation agency. Not another closed AI tool.

Typical implementation

  • Vendor-first recommendations
  • Disconnected point solutions
  • Limited control over models and infrastructure
  • Project ends after deployment
  • Opaque automation without governance
A controlled path to value

Start with one measurable workflow. Scale from evidence.

  1. 1

    Opportunity session

    Identify the process, baseline, systems, constraints and economic upside.

  2. 2

    Solution blueprint

    Receive a clear architecture, implementation scope, timeline and success criteria.

  3. 3

    Production pilot

    Deploy one real workflow with monitoring, safeguards and measurable outcomes.

  4. 4

    Operational rollout

    Optimize the system and expand into adjacent workflows and departments.

Your next AI system

Find the workflow with the strongest business case.

Share the process, systems and current bottleneck. Createting will assess where an agentic system can create measurable leverage.

Request an opportunity session

AI STRATEGY · ARCHITECTURE · IMPLEMENTATION

Build the right AI system before paying to scale the wrong one.

Createting evaluates where AI creates measurable leverage, which models and infrastructure fit the workload, and how agents should connect to existing systems. Consulting defines the business case and architecture. Infrastructure and hosting are implemented separately when they are the right answer.

Model-independentBusiness-case firstEU-ready architectureFrom pilot to operations

THE DECISION LAYER

The expensive mistake is not choosing the wrong tool. It is designing the wrong operating model.

Token consumption grows as context windows, reasoning depth, agent loops and multimodal workloads expand. At the same time, premium API pricing, plan limits and vendor dependencies can turn a successful pilot into an unpredictable operating cost.

But private hosting is not automatically cheaper. The answer depends on utilization, latency, model size, data sensitivity, availability targets and the value of each workflow. Createting models these trade-offs before architecture is locked in.

WHAT CONSULTING DECIDES

01

Where AI should act

Prioritize workflows by revenue impact, cost reduction, risk and implementation difficulty—not by novelty.

02

Which capability is actually required

Agentic execution, coding, retrieval, voice, browser control, document intelligence or a simpler deterministic workflow.

03

Which model and routing strategy fits

Frontier APIs, lower-cost APIs, open-weight models, specialist models or a routed combination based on task complexity.

04

Which infrastructure is justified

Managed API, dedicated endpoint, private cloud or self-hosted deployment—with the right GPU, observability and failover design.

AI ECONOMICS

As usage compounds, architecture becomes a financial decision.

A single request can trigger retrieval, planning, tool calls, verification and multiple model passes. Cost must therefore be measured per completed business outcome—not merely per million tokens.

See how private and hybrid infrastructure can reduce long-term AI cost →
Simple internal retrievalAPI or small hosted model
High-volume supportRouting + dedicated inference
Agentic operationsHybrid model stack
Sensitive proprietary dataPrivate infrastructure

The right answer is workload-specific. Consulting establishes the break-even model; Infrastructure implements it.

A CONTROLLED PATH TO VALUE

Start with one measurable workflow. Expand only after evidence.

01

Opportunity review

Business process, data, systems, constraints and success metric.

02

Architecture blueprint

Agent design, model routing, integrations, governance and cost model.

03

Production pilot

Real workflow, real users, measurable outcome and operational safeguards.

04

Scale or stop

Expand proven systems, optimize economics or reject weak business cases.

WHEN PRIVATE INFRASTRUCTURE IS THE ANSWER

Createting can also deploy and operate the model layer.

Fine-tuning, dedicated inference, private hosting, model routing and observability are separately scoped Createting Consulting and Infrastructure services—not features that are automatically included with ordinary platform access.

View Infrastructure & Hosting
FROM STRATEGY TO OPERATING MODEL

A concrete example: governed AI customer service agents.

See how business intent, evidence, system tools, autonomy policy and measurable pilot criteria become one implementation architecture.

Inspect the customer service architecture →

YOUR NEXT DECISION

Find the AI initiative with the strongest business case.

The first conversation is designed to identify whether the opportunity deserves a pilot, a different architecture or no AI investment at all.

Request an opportunity review