Unstructured data holds your most sensitive information — and it's your biggest blind spot. ROAD scans, classifies, and governs file-based content at scale, across every environment it already lives in.
Documents, contracts, HR files, and financial records spread across cloud storage, shared drives, and legacy file servers — often without any visibility or control.
PII, financial records, and regulated content live in places no one is watching — Microsoft OneDrive, SharePoint, cloud storage, NAS drives.
Without governed access controls, AI systems can reach data they should never see — creating GDPR, CCPA, and HIPAA exposure.
Files accumulate for years with no retention policy. The footprint grows. So does the risk.
Incident response requires knowing what data was at risk. Most organizations can't answer that question in hours — or at all.
What is that doing there?
ROAD's unstructured scanning engine runs in-place against your existing file storage — on-premise or cloud — without moving data out of its source environment for analysis. Index it, classify it, govern it.
How It Works
Three concerns, one platform: analyze your content with AI, identify sensitive PII, and archive with governance built in.
AI-powered, in-place analysis. Index, classify, and search your unstructured content exactly where it lives — no data movement, no disruption to existing workflows.
Discover PII and regulated content across files with predefined and custom scanners. Pinpoint exact locations inside documents so you can act on real risk, not guesswork.
Classify, summarize, and organize content as it moves from source to target. Retention and governance built into the migration — not bolted on afterward.
Cost Governance
ROAD gives organizations direct control over AI token costs before they become a problem.
ROAD monitors AI token consumption in real time, enforces daily spending limits per LLM, and automatically suspends access before costs run away — keeping AI operations governed and accountable.
For each LLM in use, administrators configure the exact cost per token. ROAD tracks consumption per execution in real time, so spend is visible at the transaction level — not discovered after the fact.
When a limit is reached, ROAD suspends access to that LLM automatically. The entire process runs without manual intervention and routes notifications to the appropriate administrators.
The result: AI usage stays within budget, accountability is maintained at the execution level, and no single agent or workflow can exhaust resources undetected.
Cost controls can be set at multiple layers:
A hard cap on total token spend per LLM per day.
Alerts when usage approaches the daily limit.
A defined buffer that permits additional spend before the LLM is suspended.
An optional additional allocation that can be added on top of the base daily limit.
Platform Capabilities
ROAD applies LLM-powered classification, OCR, and automated metadata indexing — without requiring custom model training.
Every processed file is indexed across seven standard fields — file type, size, path, creation date, access date, modified date, and owner — searchable from day one, no configuration required.
Classify documents with an LLM in plain language, or use deterministic, programmable policies you define. No model training required — ROAD passes instructions to the LLM at runtime, and policies give you auditable, rule-based control.
Apache Tika with OCR handles scanned PDFs, image-only documents, and embedded text in PNG/JPEG/SVG files — open source, no per-document licensing cost.
High-volume scanning can flood teams with noise. ROAD uses confidence scoring, multi-signal validation, and tunable thresholds to suppress false positives — so you focus on real risks, not alert fatigue.
Plain-language summaries generated per document during analysis or archiving. Sentence count is configurable per job. Use the same LLM for summarization and classification to reduce licensing and infrastructure costs.
Define inclusion and exclusion patterns before any job runs. Limit analysis to specific file types or directories. Multi-threaded execution with configurable concurrency, and distributed across multiple ROAD nodes for high-volume environments.
Archive jobs support configurable retention rules — by creation date, modified date, access date, or custom Groovy expression. Evaluate expiration monthly, yearly, or on full extract.
Scale & Sovereignty
ROAD's distributed architecture runs scans across multiple nodes and locations in parallel. Data stays in its source environment, so you keep sovereignty while scaling without limits. By running the LLM on your own infrastructure, you are guaranteed data confidentiality.
Deploy multiple ROAD nodes across regions or business units. Jobs split across workers, sources, and environments for elastic throughput.
Multi-threaded, configurable concurrency lets you match throughput to your infrastructure. Add nodes to handle growing volumes without re-architecting.
Analysis runs in place. Data never leaves its source environment unless you explicitly choose to move it. Ideal for GDPR, national data-residency, and air-gapped requirements.
Risk Reduction
ROAD's Discovery module scans file content for sensitive data using predefined and custom scanners. When a flagged term is found, its exact location in the document is highlighted.
Predefined Scanner Categories
SSNs, driver's licenses (state-level), passports
Bank accounts, credit card numbers
Phone, email, address, postal codes
Multi-lingual medical term libraries; additional languages added on request
City, country, region fields
User-defined via regex, dictionary, or custom logic
Search Capabilities
Three search modes against the full indexed document corpus. Results downloadable as a ZIP file containing all matching documents.
Keyword search across document content and metadata — including LLM-generated summaries. Returns all matching documents across the full corpus.
Criteria-based search across file class, extension, size, path, content, modified date, owner, and directory. Supports exact match, starts with, and contains operators.
Free-text queries interpreted by the configured LLM. Query in any language the LLM supports. Typo-tolerant by default.
“Find all documents created in the last 2 years containing Social Security numbers”
“Show me every contract referencing Oracle EBS”
Connectivity
ROAD connects to on-premise and cloud file storage — no data replication required for analysis. Archive to the targets your organization already uses.
Archive Targets
LLM Flexibility
A single LLM can power both classification and summarization to reduce licensing and infrastructure costs. Multiple LLMs can still be configured when your architecture requires it. Fully self-hosted options available for air-gapped environments.
Green dot = fully self-hosted; no external API calls required. Suitable for air-gapped or data-sensitive environments.
Most organizations don't know what sensitive data they're holding — or where it is. ROAD finds it, classifies it, and gives you control over it.
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