The multimodal pipeline under
every TerraEdge deployment.
A proprietary, enterprise-owned framework built for industry-specific and cross-functional deployments. Documents, voice, video, and streams enter. Structured intelligence comes out.
Four reasons enterprise buyers pick TerraEdge.
100% enterprise-owned IP
No open-source model weights in your pipeline. You own the models, the training data, the deployment, and the derivative work.
Zero open-source risk
Clean licensing. No contamination. Passes procurement, legal, and compliance on the first review.
ML Ops native
Drift detection, retraining, lifecycle management, and performance monitoring built into the framework — not bolted on.
Deploys where you need
Cloud, on-prem, hybrid, or edge. TPU, GPU, CPU, or edge device. The framework adapts to your infrastructure — not the other way around.
Multimodal by design. Unified pipeline.
Most enterprise data isn't rows in a database. It's documents, calls, frames, and live streams. The framework ingests all of it through one interface.
Invoices, legal documents, product sheets, engineering drawings, scanned tables, email attachments.
Voice recordings, telephonic conversations, recorded calls, WAV / MIDI audio clips.
Social media feeds, security surveillance video, CCTV footage, drone feeds, satellite imagery.
Sensor streams, real-time feeds, live transactional data.
Seven modules. Modular, composable, auditable.
Each module is a swappable component — configurable per use case, traceable in production, and retrainable without re-platforming.
Pre-processes raw input — resolution, noise, clarity.
Classifies form type, layout, and routing path.
OCR / ICR over printed and handwritten content.
Structured extraction from visual elements, tables, diagrams.
NLP and sequence reasoning over transcribed content.
Identifies relationships, entities, and semantic links.
Domain logic enforcement — Drools-based, configurable.
A configurable rule engine sits between the ML output and the structured outcome. Domain logic — compliance checks, validation rules, escalation paths — is encoded declaratively, not in model weights. That's how an extraction at 95% accuracy becomes an auditable 99% business outcome.
Structured, queryable, system-ready.
Six canonical output formats — designed to plug directly into the systems you already run.
Structured data for system integration.
Analyst-ready tabular output.
Per-speaker audio with diarization.
2D → 3D conversions, machining features.
Vector embeddings, searchable indexes.
Insights, aggregations, forecasts.
Deploys where you need.
Scales where it matters.
- Distributed Hadoop
- Apache Spark
- Kubernetes-ready
- NVIDIA GPU (CUDA parallel)
- TPU
- CPU clusters
- Edge devices
- Cloud (AWS / Azure / GCP)
- On-premises
- Hybrid
- Air-gapped
Not ready for AI yet? Want to chat about possibilities?
No pitch, no pressure. Just a conversation about where AI could fit, or where it can wait. Bring your skepticism — we'll bring ours.
Map the framework to your use case.
Bring one workflow and one data constraint. A CoE architect will sketch the ingestion → processing → output flow for your specific problem in under an hour.