The agent is the cheap part. The architecture underneath is the product.
Orbio is built so the model can always be swapped for a better one, while everything that actually compounds — your context, your rules, your outcomes — stays exactly where it is.
Model-independent by design
Each model is used where it performs best, consumed through enterprise clouds with EU data residency. Claude via AWS Bedrock for complex reasoning and transcript analysis, GPT via Microsoft Azure for conversational messaging, Mistral for document OCR and extraction. No dependency on any single provider, with fallbacks for continuity — and every model change passes comparative testing before production.
The context layer
Underneath the agents sits one persistent record of your workforce: identities resolved across ATS, HRIS and payroll, every fact carrying its source and date. Agents write down into it after every conversation and read up from it before every decision. Models get replaced; this layer never does.
Real memory, not a context window
Working memory holds the task in flight. Episodic memory records what happened and how it turned out, append-only and time-stamped. Semantic memory holds what things mean in your operation. Procedural memory encodes how work is actually done, tuned from experience rather than hard-coded.
Orchestration over automation
Agents do not run a fixed script. An orchestrator builds a plan from the goal, the context available and the permissions of the person involved — then executes it across channels and systems, escalating to a human at the gates you define.
Engineering for work that cannot go wrong quietly
Hiring decisions affect people's livelihoods, so the platform is built to be inspected: every conversation traceable, every change validated, every output reviewed.
Voice and messaging at scale
Natural voice with sub-second turn-taking, plus WhatsApp, SMS, LINE, email and web — one conversation that can move between channels and switch language mid-interview across 35+ languages.
Evaluation on 100% of output
An independent model reviews every analysis in production for bias, hallucination and omission. Flagged cases go to human review, and the results feed a weekly technical review.
Simulation bench before deploy
Representative scenarios — first contact, disinterest, questions about the AI, edge cases — run against every agent change. 6,000+ automated tests must pass before a change can merge.
Transparent versioning and rollback
Every operation records the exact system version it ran on. Prompt changes are logged with date, author and previous version, and any degradation can be rolled back immediately.
Agentic observability
Real-time tracing of agent decisions and actions for debugging and audit, with anomaly detection, automated alerting and bottleneck prediction ahead of peak load.
Built for volume
Millions of applications a year across 17 countries, with per-client isolation enforced at the database-engine level and capacity that absorbs seasonal spikes without added coordination.
What gets rebuilt, and what never does
The clearest way to see where the value sits is to ask how long each layer would take to recreate from zero.
The model
Replaced three times this year. Nobody noticed.
4 days
The integrations
Rewritten twice. Painful, finite, done.
2 weeks
The context layer
Never rebuilt. Cannot be prompted into existence.
18 months and counting
Want the technical detail? We will go as deep as you like.
Architecture reviews, model and sub-processor lists, pen-test reports and the AI system documentation are all available to your engineering and security teams.