Ordos — Products

Now available — Early Access

🐻‍❄️ Opos

The general-purpose Agent. Works across every domain from day one.

Opos is an autonomous AI Agent Installed on your machines. Discovers every tool in your environment. Selects the right models. Presents the full plan before running anything. You approve. Then it runs.

// opos — pre-execution plan

task: "screen 50k compounds"

status: "awaiting approval"

pipeline:

01 BioNeMo → local

02 Claude-3 → reasoning

03 report_gen → output

data_handling:

pii_filter: local ✓

audit_trail: enabled ✓

proprietary_data: stays local ✓

// awaiting human approval

approve? [ yes ] / no

For

Built for teams where expertise is the product.

Opos is designed for domain experts — scientists, analysts, engineers — who need AI that works inside their actual environment, speaks their domain's language, and earns trust before it acts. Not assistants that generate text. Agents that execute tasks, with full visibility.

Research teams running multi-step computational pipelines

Labs handling sensitive data that cannot leave the premises

Organizations that need a full audit trail before any regulator asks for one

In Practice

Without Opos

→ Every new workflow requires a developer. Scientists wait weeks for pipelines that should take hours.

→ Output is wrong. You spend two days diagnosing which model, which step, which API call failed — with no logs and no context.

→ Most biotech and pharma firms lack in-house AI capabilities entirely — so they pay for consulting, system integration, and maintenance just to get started.

→ Compliance reporting that should take minutes takes weeks. When an audit hits, you're scrambling.

With Opos

→ Describe the task. Opos selects BioNeMo locally, Claude-3 for reasoning, report_gen for output.

→ Opos shows exactly which data moves where. PII filtered. Proprietary data stays local.

→ Full plan displayed before execution. You approve every step.

→ Every action logged. Failures surfaced immediately with context.

→ Audit log generated automatically. Exportable on demand.

Handled

What Opos handles so you don't have to.

🧠

Model Selection

Evaluates available models against task requirements, local constraints, and data sensitivity. Picks the right one. Shows its reasoning.

🔍

Tool Discovery

Scans your installed environment. Finds the tools that exist, understands what they do, and builds workflows using your actual stack — not a generic one.

🔒

Data Security

Traces every data flow before execution. Flags external calls. Enforces local processing for sensitive inputs. You see it all before approval.

📋

Audit Trail

Logs every action, model call, tool invocation, and decision point. Structured. Timestamped. Exportable. Ready before any regulator asks for it.

⚠️

Failure Handling

When a step fails, Opos surfaces exactly what happened, why, and what it tried. No silent failures. No black-box errors. Full context for every issue.

Transformation

BEFORE

→ You spend hours selecting tools and models for every new task type.

→ Sensitive data flows through external APIs you didn't fully vet.

→ When something fails, you have no record of what ran or in what order.

→ Compliance is a scramble. Audit prep takes days. Trust is low.

AFTER

→ Describe the task. Opos handles model and tool selection automatically.

→ Every data flow is declared and approved before it runs. Nothing moves silently.

→ Every action is logged, structured, and searchable. Failures surface with full context.

→ Audit trail is always ready. Trust is built into the execution model.

How Opos Works

Describe your task

Tell Opos what you need in plain language. "Screen 50k compounds against this target." "Generate a pipeline report from last week's assays." No prompting. No model selection.

Opos builds the plan

Opos selects the right AI models, discovers required tools in your environment, builds the complete workflow, and determines how data will flow through the pipeline.

You see everything

Before anything runs, Opos shows you the full justification: what it's about to do, why each model was chosen, how data is handled, what the risks are. The box is open.

Approve and execute

You approve or reject. If approved, Opos executes autonomously, logging every action for a complete audit trail. If rejected, it replans based on your feedback.

Architecture

Built on OpenClaw. Deep-engineered beyond it.

Opos is OpenClaw deep-engineered — not a wrapper. OpenClaw handles autonomous execution and tool finding. Deep engineering adds everything else: multi-model orchestration, security hardening, human-in-the-loop approval, and dead-simple installation. OpenClaw alone cannot orchestrate multiple LLMs. Opos engineers orchestration around it carefully.

→

Multi-model orchestration

Routes the right LLM to each task, BioNeMo for docking, Claude for reasoning, GPT-4 for synthesis. Not one model for everything.

→

Local data filtering

PII and proprietary data filtered locally before anything leaves your environment. None of it touches external APIs unprotected.

→

Automatic tool discovery

Opos scans your environment and integrates with every existing tool. If a required tool doesn't exist, Opos finds it, downloads it, integrates it. No manual configuration.

→

Full audit trail

Every action logged. Every model choice documented. Every data path recorded. Ready for investors, regulators, and your own peace of mind.

What Opos is not.

Opos is not a discovery tool. It doesn't understand the science — scientific judgment comes entirely from the specialized models it orchestrates. Opos is the operating room — it makes surgery possible, it doesn't perform surgery. Your startup is the surgeon.

Tools that open the box before running.

Built for teams where expertise is the product.

What Opos handles so you don't have to.

Infrastructure that opens the box.