Platform

An operating system, not a chatbot.

ClearOS is a modular runtime: connectors in, agents in the middle, audited decisions out. Built to deploy inside your boundary, on your iron, under your governance.

Layers

Six layers, well-defined contracts.

L1

Connectors

SFTP, fax, email, EDI, JDBC, REST, mainframe screen-scrape, S3.

L2

Document AI

OCR, layout, vision-LLM extraction, signature/seal detection.

L3

Knowledge

Policy manuals, statutes, regs, internal precedent — versioned RAG.

L4

Agent runtime

Deterministic plan-and-execute with full traces; pluggable LLMs.

L5

Workflow

State machines, SLAs, escalation, appeals, queueing.

L6

Audit & export

Signed, immutable logs to your bucket. FOIA & IG views.

Agent runtime

Predictable, observable, replayable.

Most "AI agents" are a clever prompt and a fingers-crossed loop. That's not acceptable for public-sector decisions. ClearOS agents are explicit state machines. Every step has a name, a contract, a budget, and a fallback.

  • Deterministic planner with bounded retries
  • Tool calls capped, sandboxed, signed
  • Replay any trace bit-for-bit
  • Diff two replays to study override patterns
  • Hot-swap models without changing logic

Trace · case 18342

▸ ingest.fax(2pp) ✓ 1.2s ▸ ocr.layout ✓ 2.8s ▸ extract.SSA-3368 ✓ 3.1s ▸ knowledge.lookup(listing 1.04) ✓ 0.4s ▸ reasoning.draft ✓ 6.7s ▸ policy.lint ✓ 0.2s ▸ queue.review(J. Ramirez) ✓ total 14.4s · cost $0.083

Policy engine

Statute as source. Manual as code.

Versioned

Policy lives in Git-backed bundles. Diffs review like pull requests.

Cited

Every model answer points to the section it relied on.

Tested

Counsel writes assertions; the system runs them on every change.

Forkable

Pilot a new policy in shadow mode without affecting live cases.

Diffable

See exactly which case classes a regulation change will touch.

Rollback-ready

Bad rollout? One command, full revert, all decisions tagged.

Models

Bring your own. Or ours. Or none.

Frontier hosted

OpenAI, Anthropic, Google — behind FedRAMP-Mod boundaries.

Open weights

Llama, Mistral, Qwen — runnable air-gapped.

Specialty

Document-AI models we fine-tune for messy gov forms.

None

Pure rules & OCR mode for the most sensitive workloads.

Deployment topology

Three patterns, one product.

SaaS multi-tenant

Lightest lift. Counties, small cities, design partners.

Single-tenant cloud

State agencies. AWS GovCloud or Azure Gov. Customer-managed keys.

Air-gapped on-prem

Federal IGs, DoD adjacent. Open-weight models, full disconnection.

Observability

Run it like a production system.

Latency & cost

Per workflow, per model, per minute.

Quality

Human override rates, escalation rates, appeal-reversal rates.

Drift

Policy mismatch flags before legal hears about it.

Fairness

Outcomes by protected class, automatic reports.

SLA

Real-time queue health, breach prediction.

Capacity

Forecast next quarter's volume; right-size model spend.

Developer-friendly

An API for the parts that should be programmable.

State CTO offices want to build, not just buy. ClearOS exposes a typed REST & gRPC API for ingestion, classification, drafting, and audit retrieval. Webhooks for state changes. SDKs in Python, TypeScript, and Go.

  • OpenAPI 3.1 spec, generated docs
  • Stable v1 contract since 2025
  • Sandbox environment included
  • Local dev kit for offline testing

POST /v1/cases

{ "workflow": "ddi-initial", "documents": ["s3://.../app.pdf"], "applicant": { "id": "X-19283" }, "priority": "standard" } → 201 { "case_id": "C-2026-018342" }

Performance

Numbers that matter to your CIO.

99.95%
platform availability SLA
<15s
median draft latency
10M+
documents/day per tenant
$0.06
avg model cost per case
FAQ

For the engineering team.

Can we run this air-gapped?

Yes. We ship an offline bundle with open-weight models, vector DB, and policy engine. No outbound calls.

What's the upgrade story?

Quarterly minor releases, annual majors. Backward-compatible API for two majors. Customers control upgrade windows.

Do you support our SIEM?

Splunk, Elastic, Sentinel, Sumo, plain syslog. Standard OCSF schema.

Can we self-host the model?

Yes. Bring your own inference cluster; we provide the routing, caching, and observability.

Bring your architects.

We'll do a 60-minute deep dive — runtime, policy, audit, deployment.

ScheduleSecurity →