Our agentic AI development services
RAG-based knowledge agents
We build agents that answer questions using your product documentation, knowledge bases, and content hubs. Precise retrieval via hybrid search combines semantic, BM25, and re-ranking approaches. Complete solutions include ingestion pipelines, retrieval architecture configuration, and iterative quality evaluation.
RAG-based knowledge agents
We build agents that answer questions using your product documentation, knowledge bases, and content hubs. Precise retrieval via hybrid search combines semantic, BM25, and re-ranking approaches. Complete solutions include ingestion pipelines, retrieval architecture configuration, and iterative quality evaluation.
MCP integration for agentic systems
The Model Context Protocol serves as our preferred standard for connecting agents to business systems: CRM, ERP, CMS, and BI platforms. Adding new data sources requires MCP server implementation rather than custom project development, creating architecture built for extension.
Observability and continuous evaluation
Production agentic AI systems require comprehensive observability to avoid black-box operations. We implement Langfuse (OpenTelemetry) as integral components of every agent system, making every retrieval step, agent decision, and generated answer traceable and evaluable. Batch evaluations identify regressions early.
Multi-year agent platforms
Successful initial pilots represent beginnings rather than the endpoint. We help develop roadmaps for architectural scaling to new channels, brands, and markets, identifying required MCP connections for subsequent pilots and planning evolution from single agents to multi-agent ecosystems.
Let's discuss your agentic AI needs
Our approach to agentic AI development
Most AI experiments function as demonstrations, running in isolation, handling only ideal scenarios, and failing to reach production when edge cases emerge. Production-ready agentic AI presents different engineering challenges: agents that reason through multi-step tasks, recover from errors, collaborate with other agents, and operate continuously with controlled supervision.
Architecture that scales
Architectural choices in Pilot 1 determine whether Pilot 2 becomes feasible. We design with future requirements in mind: MCP as the integration standard enabling easy addition of new data sources, A2A as the protocol for multi-agent collaboration in later phases, and Infrastructure as Code via Terraform for reliable deployments. This approach builds platforms that grow rather than requiring refactoring after every pilot.
Observability as production requirement
We implement Langfuse from day one as an integral system component, not an optional later addition. Every retrieval step, agent decision, and generated answer becomes fully traceable. Batch evaluations systematically test for regressions. Domain experts participate in early evaluation sessions, applying lessons learned from production projects.
Governance first, then technology
Technology selection for agentic AI represents the straightforward aspect of implementation. The complex challenge involves governance: determining which decisions agents can make autonomously, when escalation to humans occurs, and how every subsequent action is audited. We begin with governance frameworks, then select platforms that align.
Why work with iO for agentic AI development?
Complete journey from PoC to production
Many agencies create convincing demonstrations. We build comprehensive solutions including production setup, evaluation cycles, observability layers, and architecture supporting multiple pilots. The distinction lies in post-presentation implementation capabilities.
Programmatic approach to multi-year agent platforms
Nimo Beeren leads agent programmes as programme coordinator rather than standalone pilot project manager. This ensures architectural connectivity across pilots, systematic stakeholder alignment, and internal AI ownership development.
Proprietary tooling: Claws framework
We've developed Claws, a proprietary framework for building agentic AI systems. Components include zeroclaw (zero-shot agents), openclaw (open-source building blocks), and nemoclaw (agent orchestration), providing head starts for production-grade agent development.
Guardrails for sensitive domains
B2B agentic AI inevitably encounters sensitive topics. We have experience building guardrails that safely route off-topic questions without compromising agent utility, demonstrating that safety and usability complement rather than oppose each other.
Governance-first advisory approach
Governance challenges exceed technology selection complexity. We begin with governance frameworks, then select tools based on specific contexts and requirements.
