01 · Ingest & Connect
Bring every modality into view.
Connect documents, images, charts, diagrams, audio, video, applications, sensors, and structured enterprise data.
INSPECTION_REPORT.PDF — READY
ASSEMBLY_VIDEO.MP4 — READY
SENSOR_DATA.CSV — READY
Multimodal AI Platform
Move Beyond Text-Based AI With Multimodal Understanding
Automatan gives enterprise AI agents the ability to perceive, connect, and reason across documents, images, diagrams, charts, audio, video, and structured data—creating intelligence from the complete information environment.
Cross-modal perception · Vision-language reasoning · Evidence-grounded decisions
1M+Multimodal data elements analyzed
100K+Documents, images, and visual assets processed
50+Enterprise data formats supported
<10sMedian multimodal analysis time
Platform thesis
Enterprise intelligence does not exist in text alone.
01
What Multimodal AI is
An AI perception layer that understands and combines multiple information modalities within one reasoning environment.
02
Where traditional automation stops
OCR extracts text. Vision systems classify images. Transcription converts speech. These systems process formats independently without understanding the relationships between them.
03
How Automatan perceives differently
Automatan connects language, visual structure, spatial signals, spoken information, and enterprise data—allowing agents to reason across the complete business context.
How Automatan works
Five stages. One perception-to-decision chain.
Each stage converts fragmented, multimodal information into connected context, grounded understanding, and actionable intelligence.
02 · Multimodal Perception
Understand each format natively.
Vision-language models and specialized perception systems identify entities, objects, speech, layouts, tables, sequences, spatial relationships, and visual signals.
OBJECTS — 17
DOCUMENT REGIONS — 26
SPOKEN EVENTS — 8
03 · Cross-Modal Alignment
Connect signals across formats.
Map the same entities, events, products, locations, and conditions across written records, visual evidence, recordings, and structured systems.
04 · Multimodal Reasoning
Interpret the complete evidence.
Agents compare signals, identify inconsistencies, discover patterns, resolve ambiguity, and reason across information that no single modality can explain independently.
VISUAL ANOMALY — CONFIRMED
DOCUMENT CONFLICT — DETECTED
CONFIDENCE — 0.93
05 · Decision Support
Turn perception into action.
Produce grounded findings, recommendations, evidence packages, and workflow triggers based on the complete multimodal context.
ESCALATE QUALITY REVIEW
EVIDENCE SOURCES — 5
PRIORITY — HIGH
Platform capabilities
Perception built around the decision.
Six capabilities combine into one multimodal intelligence system.
Document Understanding
Interpret document structure, text, tables, forms, diagrams, annotations, references, and relationships across native and scanned files.
A
B
C
! Risk
Visual Analysis
Identify characteristics, patterns, anomalies, conditions, and relationships within product images, screenshots, technical visuals, and real-world scenes.
IP indemnityHigh
TerminationMedium
InsuranceLow
Audio Understanding
Interpret customer calls, meetings, interviews, voice notes, and operational recordings for topics, sentiment, decisions, commitments, and actions.
Renegotiate cap
Add insurance layer
Define cure period
Video Intelligence
Understand events, behaviors, demonstrations, inspections, and operational sequences across time—not simply individual frames.
Cross-Modal Reasoning
Connect written, visual, spoken, spatial, and structured evidence to answer questions that cannot be resolved through one modality alone.
◎Method-driven reasoning
▤Evidence-backed conclusions
↗Calibrated confidence
Multimodal Workflow Intelligence
Use perceived information to initiate reviews, support decisions, generate evidence packages, and trigger controlled enterprise workflows.
Clause 8.2
Clause 8.3
Downstream Risk
Perceptual rigor
Every conclusion remains connected to what the agent perceived.
Automatan preserves the relationship between source information, cross-modal interpretation, and business conclusions so reviewers can understand not only the finding, but the evidence across formats that produced it.
Source Grounding
Trace findings back to the document region, image area, video segment, audio timestamp, or structured record supporting them.
Cross-Modal Validation
Confirm signals from one modality against related information in other formats and enterprise systems.
Confidence Calibration
Communicate how strongly the available multimodal evidence supports each interpretation and conclusion.
Contradiction Detection
Surface conflicts between written records, visual evidence, spoken statements, structured data, and observed conditions.
Human Review
Route uncertain, sensitive, or high-impact interpretations to domain experts with the supporting evidence already assembled.
Example multimodal insight
Not extraction. A cross-modal analytical object.
Finding, visual evidence, documentary context, operational data, and action—connected in one review surface.
● Quality Signal · Medical Device Inspection
Visual evidence indicates a component deviation not recorded in the inspection report.
Supporting Evidence
The inspection image shows an assembly orientation inconsistent with the approved engineering drawing. The written inspection report records the component as compliant.
Cross-Modal Validation
The timestamped production record confirms the photographed unit belongs to the inspected batch. Sensor readings show an abnormal alignment measurement during the same assembly stage.
Risk Classification
High priority · Product quality exposure · Documentation inconsistency.
Recommended Action
Quarantine the affected batch, initiate a quality investigation, and reconcile the inspection report with the visual and sensor evidence.
Where Automatan is used
Built for information-rich enterprise work.
Products and AI Agents
- Multimodal Intelligence API
- Vision-Language Agents
- Document Understanding Agents
- Real-World AI Copilots
Solutions
- Product Quality Analysis
- Inspection Intelligence
- Customer Interaction Analysis
- Multimodal Compliance Review
Teams
- Product and Engineering
- Quality and Compliance
- Customer Operations
- AI and Innovation
Industries
- Healthcare and Life Sciences
- Manufacturing and Engineering
- Ecommerce and Retail
- Financial and Professional Services
Featured AI transformations
Start with fragmented signals. End with unified intelligence.
Scanned Documents + Tables→Structured Evidence Map
Explore →Product Images + Specifications→Visual Compliance Analysis
Explore →Inspection Video + Sensor Data→Operational Anomaly Report
Explore →Customer Calls + Account History→Resolution Intelligence
Explore →Engineering Drawing + Site Images→Deviation Assessment
Explore →Presentation + Financial Data→Decision-Ready Brief
Explore →Trust and enterprise readiness
Enterprise-grade from the perception layer up.
Security
TLS 1.3, AES-256, RBAC, SSO/SAML.
Reliability
Multimodal evaluation, confidence signals, source grounding, and human-review controls.
Data Handling
Zero-retention default and no training on customer data.
Deployment
SaaS, VPC, on-prem, APIs, and enterprise SDKs.
Frequently asked questions
Common questions
Is Multimodal AI the same as OCR or document processing?
No. OCR extracts characters from an image or document. Multimodal AI understands how text, layout, tables, diagrams, visual evidence, audio, video, and structured data relate within a broader business context.
Why do enterprises need multimodal AI?
Enterprise decisions rarely depend on text alone. Product quality, customer behavior, operational performance, compliance evidence, and real-world events are represented across multiple formats. Multimodal AI allows agents to reason from the complete evidence rather than an incomplete textual representation.
How does Automatan support trustworthy multimodal reasoning?
Automatan connects conclusions to their supporting source regions, frames, timestamps, records, and documents. Confidence signals, cross-modal validation, Eval Agents, and human-review workflows provide additional assurance for sensitive decisions.
Does Multimodal AI replace our existing enterprise systems?
No. Automatan adds a perception and reasoning layer across existing content repositories, applications, data platforms, media systems, and enterprise workflows.