DataUnit
A reusable processing unit: agent, tool, connector, code function, rule, approval gate, webhook, API call, or human review step.
UnitLine Pro
Turn natural language into production-ready AI workflows
Describe a business process. UnitLine compiles it into DataUnits, DataLines, DSP contracts, connector requirements, approval gates, and execution traces — ready to review, test, and deploy.
Describe the workflow you want to build
Company chatbotDatabase auditTrend detectionStartup agents
Generated draftDraft — review before publishing
Website Trend Detection Workflow
DataUnits assembled9
DataLine branches4
DSP checks passed18
Setup items missing3
Prompt
DataUnits
DataLines
DSP validation
Draft
Source config
Scraper
Extractor
Classifier
Clusterer
Approval
Slack alert
Contractspassed
DataLine trustpending review
Side effectsapproval required
Connectormissing secret
One execution model across no-code, low-code, and developer surfaces
StudioSDKAPICLIDataUnit RegistryConnector CatalogModel GatewayAudit Viewer
DataUnit + DataLine + DSP
The primitives that make AI workflows production-ready
UnitLine is built around a protocol-native model: composable components, traceable execution packets, and contracts that make governance and observability part of the system from day one.
DataLine
A traceable execution packet carrying payload, trust, policy context, lineage, artifacts, errors, and compaction history.
DSP
The protocol that makes every DataUnit composable, governable, observable, versioned, and usable from no-code and developer environments.
DataUnits make workflows composable. DataLines make execution traceable. DSP makes the platform governable.
What UnitLine generates
From prompt to executable workflow architecture
UnitLine extracts intent, assembles protocol-compliant components, creates traceable execution paths, and validates before deploy — all from a single natural-language description.
01
Understand intent
UnitLine extracts the goal, inputs, outputs, risks, and required systems from your natural-language description.
02
Assemble DataUnits
Agents, tools, connectors, code blocks, rules, approvals, and joins become protocol-compliant workflow components.
03
Create DataLines
Each execution path receives a traceable DataLine with payload, trust, policy context, lineage, and artifacts.
04
Validate before deploy
DSP contracts, connectors, model capabilities, governance rules, and missing setup are checked before execution.
What UnitLine generates
From prompt to executable workflow architecture
UnitLine extracts intent, assembles protocol-compliant components, creates traceable execution paths, and validates before deploy — all from a single natural-language description.
NLP-to-workflow compiler
Turn a business request into a draft workflow template with DataUnits, DataLines, contracts, connectors, and governance metadata.
DataUnit registry
Browse, version, and reuse every agent, tool, connector, rule, code block, approval, and API call as a protocol-compliant component.
DataLine lineage
Track payload, trust, policy context, lineage, artifacts, errors, compaction, and side effects across every execution path.
DSP validation
Validate contracts, schemas, capabilities, side effects, and governance rules before any DataUnit executes.
Model gateway
Route DataUnits by capability requirements instead of hardcoding model IDs, supporting local, cloud, and enterprise models.
Approval gates
Route risky steps — external notifications, data mutations, elevated permissions — through human approval before execution.
Connector governance
Manage secrets, rate limits, scopes, and audit trails for every external system your workflows connect to.
Replay and audit
Replay any past execution from a DataLine checkpoint, inspect every lineage event, and export audit-ready reports.
Not another agent builder
A production protocol for AI workflows
Prototype tools treat agents as experiments. UnitLine treats every workflow as a governed, observable, auditable production system from day one.
Prototype toolsUnitLine Pro
Agents glued together manuallyProtocol-compliant DataUnits
Logs added after deploymentDataLine lineage by default
Validation handled by developersDSP contracts before execution
Governance added laterPolicies, approvals, and side-effect control built in
No-code and code paths divergeUI, SDK, API, and CLI use the same manifest
Use case templates
Start from a template. Customize with natural language.
The same DataUnit and DataLine primitives can power hosted chatbots, database audits, scraping workflows, startup agents, and deterministic low-inference business logic.
Hosted company chatbots
Knowledge ingestion, product cards, appointments, CTAs, public URLs, and tenant-safe configuration.
SaaSRAGPublic URL
Database Q&A chatbot
Natural-language questions over PostgreSQL or MongoDB with exact database-backed evidence.
PostgresMongoDBCitations
External database audit agent
Read-only schema inspection, duplicate detection, null-rate analysis, suspicious values, and audit reports.
Read-onlyAuditPolicy
Scraping and trend detection
Parallel DataLines for crawling, extraction, classification, clustering, trend scoring, and alerts.
ParallelClustersAlerts
Startup agent suite
Product, marketing, finance, research, and fundraising agents coordinated around shared startup memory.
Multi-agentMemoryTasks
Low-inference recommendation workflows
Formula DataUnits, rules, product lookup, ranking, and optional LLM explanations for lower cost.
RulesFormulasLow cost
Workflow lifecycle
A visual story for the full agent lifecycle
UnitLine does not stop at generation. Every workflow moves through review, validation, test runs, approval gates, deployment, observability, and replay.
Step 01
Describe the workflow
Start from natural language or a template. Define the process, expected outputs, systems involved, and risk level without opening a blank graph builder.
- Intent extraction
- Template suggestions
- Missing setup detection
Workflow promptparsed
Scrape competitor websites daily, classify product changes, detect pricing trends, summarize risks, and send approved alerts to Slack.
scrapeclassifyclusteralert
Step 02
Review generated DataUnits
UnitLine proposes agents, tools, connectors, rules, approvals, joins, and deterministic code blocks as protocol-compliant workflow components.
- Reusable catalog
- DSP contracts
- Capability requirements
dsp manifest
dsp_version: 1.0unit_id: unit.trends.classifiercontracts: strictcapabilities:llm: structured_outputgovernance:approval: conditionalobservability:lineage: required
Step 03
Inspect DataLines
See how data moves through branches, what trust level each step carries, which policy applies, and where side effects require control.
- Lineage chain
- Trust propagation
- Parallel branches
dataline envelope
{"type": "trend.cluster","trust": "pending_review","policy": "requires_audit","lineage_events": 6,"side_effects": "external_notification","status": "waiting_approval"}
Step 04
Validate, test, and publish
Run DSP checks, configure missing connectors, test sample inputs, route risky actions to approval, and publish when the workflow is ready.
- Validation report
- Approval gates
- Replayable execution
Validation reportdraft ready
Input/output schemaspassed
Connector secretsmissing
Side-effect policyapproval
Replay checkpointsenabled
Not another agent builder. A production protocol.
Give your team a workflow compiler, not another blank canvas
Start from natural language, review the generated architecture, connect your systems, test safely, and publish when the workflow is ready.
Describe
Write the workflow in plain language, from a simple task to a multi-system enterprise process.
Compile
Generate DataUnits, DataLines, contracts, connector requirements, and governance metadata.
Test
Run sample inputs, inspect lineage, validate contracts, and review risky branches before publishing.
Operate
Deploy with traces, approvals, replay, model routing, cost visibility, and audit-ready execution history.