The Engine for Agentic Work
The complete platform for deploying production agents in the enterprise. Durable execution, document ingestion, and multi-cloud compute — all built-in so you focus on your application, not infrastructure.
Deploy Durable Agents as APIs written in any framework
Process pay stubs, tax forms and bank statements. Verify income automatically.
Ingest claim documents, pull key fields, route to the right workflow. Handle thousands.
Read through agreements, flag key clauses, extract terms.
Document Ingestion enables building Agentic automation faster
Extract key insights, complex tables and charts from investor decks, huge spreadsheets, and SEC filings.
Handle low-quality faxes, handwritten notes, and complex medical forms with ease.
Parse complex intake files into labeled claims data—accelerating adjudication, triage, and system-ready handoff.
Everything you need for Agents in Enterprises
Understanding Documents is the biggest bottleneck for AI Applications. Document AI reads documents effortlessly
An application runtime to run serverless agents in any framework with built-in durable execution to help agents run longer and recover from crashes
Code Sandboxes enable your agents to run untrusted LLM generated code and solve a wide range of problems
Build large scale data ingestion and ETL workflows on CPUs and GPU machines with map-reduce primitives and built-in queues
Average request dispatch time
Number of concurrent requests
Function Input/Output payload size
Serverless Compute for your High Throughput Agents
Tensorlake’s compute platform lets you deploy agents on a serverless runtime to serve them to thousands of users instantly without touching infrastructure.
The platform combines many foundational infrastructure components that typically a platform team have to build, operate and maintain for you to start deploying agents reliably in production environments
State and function calls are automatically checkpointed to enable agents to resume from crashes
Pause functions to start after weeks or months
Every extracted field links back to its exact location in the source document—pixel-level coordinates for full auditability
PDFs, spreadsheets, presentations, Word docs, images, scanned forms—ingest anything your enterprise throws at it
Document Ingestion - OCR Infused with VLMs
We use VLMs with OCRs to read Documents the way humans do.
Layout and OCR Models - Break down documents into text, tables, figures while maintaining reading order
VLMs - Read images, complex tables, correct OCR mistakes and classify documents based on visual context
Tensorlake can spin up sandboxes in your cloud enabling lower cost on infrastructure and zero egress network bandwidth fees.
No limits on number of active sandboxes and 100 sandboxes/second
Use Tensorlake on our SAAS or deploy In-VPC or air-gapped environments
Agent and Workflow Runtimes are framework agnostic - can use with frameworks like Langchain or Pydantic
Get server-less runtime for agents and data ingestion
Tensorlake is the Agentic Compute Runtime the durable serverless platform that runs Agents at scale.
“With Tensorlake, we've been able to handle complex document parsing and data formats that many other providers don't support natively, at a throughput that significantly improves our application's UX. Beyond the technology, the team's responsiveness stands out, they quickly iterate on our feedback and continuously expand the model's capabilities.”
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"At SIXT, we're building AI-powered experiences for millions of customers while managing the complexity of enterprise-scale data. TensorLake gives us the foundation we need—reliable document ingestion that runs securely in our VPC to power our generative AI initiatives."
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“Tensorlake enabled us to avoid building and operating an in-house OCR pipeline by providing a robust, scalable OCR and document ingestion layer with excellent accuracy and feature coverage. Ongoing improvements to the platform, combined with strong technical support, make it a dependable foundation for our scientific document workflows.”
"For BindHQ customers, the integration with Tensorlake represents a shift from manual data handling to intelligent automation, helping insurance businesses operate with greater precision, and responsiveness across a variety of transactions"
“Tensorlake let us ship faster and stay reliable from day one. Complex stateful AI workloads that used to require serious infra engineering are now just long-running functions. As we scale, that means we can stay lean—building product, not managing infrastructure.”
The All-In-One Runtime for AI Data Workflows
Built-in 1TB persistence layer. Ingest massive datasets without managing Kafka or Redis sidecars
from tensorlake import function, File
@function()
def download_dataset(path: str) -> List[File]: images = load_images(path)
return [pre_process_image(image) for image in images]
@function(gpu="A10")
def detect_objects(image: PIL.Image) -> Detection:
detection = yolo(image)
return detection
@function()
def write_to_db(detection: Detection): psql.write(dection)
Achieve data-warehouse concurrency directly in Python. Use .map() to fan out execution across thousands of nodes instantly, replacing the need for SQL-based orchestration
Concurrent invocations per workflow. Zero cold-start throttling
Attach H100 GPUs to any step via a single decorator argument. Mix CPU and GPU workloads in the same workflow.
Full traces of every function and tool call — with logs, timing, and structured execution paths.
Tool calls run in isolated sandboxes, making them safe for LLM-generated code.
Each agent harness executes inside an isolated sandbox to keep sessions safe and independent.
Secure by default for PHI, PII, and sensitive documents.
Each project’s data lives in its own isolated bucket with full audit trails and strong RBAC controls.
