AI agent development company
AI Agents That Can Reason, Use Tools, and Stay Under Control.
CoreRiqo Labs designs production AI agents and role-specific copilots that work with business knowledge, APIs, CRMs, support platforms, documents, and human approval workflows.
ORIGINAL CORERIQO LABS DEMONSTRATIONThe operating problem
A useful agent must do more than produce convincing text.
Business agents operate inside real systems. They need accurate context, limited permissions, reliable tool execution, structured outputs, escalation rules, and complete action traces. Without those controls, an impressive prototype can become an operational risk.
How CoreRiqo Labs engineers the solution
We define the agent's role and authority, build a bounded tool registry, connect approved knowledge and systems, validate inputs and outputs, add human approval for sensitive actions, and evaluate the complete workflow before production release.
Engineering scope
Capabilities built into the system.
Multimodal voice, chat, document, and image intake
RAG and business-context retrieval
Human approval and escalation
Structured outputs and policy enforcement
Agent evaluation and observability
Reference architecture
Designed beyond the model layer.
A production AI system needs data, interfaces, controls, evaluation, deployment, and ownership—not only a model API.
- 01User, event, or system trigger
- 02Identity, permissions, and context retrieval
- 03Model routing and agent planning
- 04Bounded API and tool execution
- 05Validation, guardrails, and approval
- 06Action logs, evaluation, and feedback
Where it creates value
Practical applications.
Customer operations agents
Resolve routine requests, update helpdesk systems, and hand complex cases to specialists with full context.
Sales and research copilots
Research accounts, prepare briefs, draft approved communication, and keep CRM records current.
Internal operations assistants
Search company knowledge, prepare reports, and complete approved tasks across internal systems.
Voice AI agents
Handle qualification, scheduling, support, and escalation through natural voice interactions.
Typical technology stack
OpenAI / Anthropic / Gemini · LangChain / LlamaIndex · FastAPI · n8n · PostgreSQL · pgvector · AWS · Docker
Service FAQ
Questions before a build begins.
How autonomous should an AI agent be?
Autonomy should match risk. We automate low-risk, reversible actions and use policy checks or human approval for sensitive operations.
Can an agent use our existing software?
Yes. We connect agents to CRMs, helpdesks, email, databases, internal APIs, cloud storage, telephony, and custom systems.
How do you test agent reliability?
We build representative task sets and evaluate answer quality, tool selection, execution accuracy, policy compliance, latency, and cost.
Start with the workflow
Bring us the business problem.
In a focused 30-minute consultation, we will discuss the users, workflow, data, integrations, risk, and the smallest useful next step.
Book your consultation