Doubleword AI provides an optimized inference stack for high-performance model deployment across batch and real-time use cases. Their control layer offers centralized governance, security, and observability for all AI usage, including private and cloud APIs. The platform enables organizations to run large-scale language models efficiently on private infrastructure with production-grade, scalable APIs.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises struggle to run large language models and other AI workloads at scale because inference requires specialized hardware, complex orchestration, and strict governance. Batch and asynchronous jobs often incur high GPU costs, while cloud‑only solutions expose sensitive data and limit compliance. Managing deployments across on‑premise, cloud, and hybrid environments adds operational overhead and reduces agility.
Solution
Doubleword AI delivers an end‑to‑end inference platform that abstracts hardware, scaling, and security concerns while keeping models inside the customer’s trusted environment. The Batch Inference service provides cost‑optimized, high‑throughput processing with strict 1‑hour and 24‑hour SLAs, reducing token costs to market‑lowest levels. A unified Control Layer offers centralized authentication, role‑based access control, usage metering, and audit‑ready logging for all model endpoints, whether they run on‑prem, in a private cloud, or via public APIs such as OpenAI or Bedrock. The Inference Stack packages open‑source and custom language models as production‑grade, OpenAI‑compatible APIs, leveraging infrastructure‑as‑code, GPU‑aware autoscaling, and self‑healing mechanisms to maximize performance and minimize idle resources. Together, these components let teams deploy, monitor, and govern AI services securely across any deployment topology without building bespoke infrastructure.
Features
- Batch Inference engine tuned for large‑scale async workloads, offering 1‑hour and 24‑hour SLAs and the lowest token pricing on the market.
- Control Layer with built‑in authentication, RBAC, fine‑grained usage metering, and immutable audit logs for compliance‑heavy environments.
- Inference Stack that auto‑generates OpenAI‑compatible endpoints for any HuggingFace or custom model, deployed via Terraform IaC for repeatable rollouts.
- GPU‑aware autoscaling and cost‑aware scheduling that dynamically adjusts GPU allocation to maintain high throughput while minimizing idle capacity.
- High‑availability architecture with health probes, self‑healing APIs, and failover routing to ensure continuous service.
- Hybrid routing engine that intelligently directs requests to on‑prem, private‑cloud, or public‑cloud endpoints based on data classification and policy rules.
- Support for advanced inference techniques such as speculative decoding and serverless LoRA serving to accelerate token generation and fine‑tuned model serving.
- Open‑source core components (Control Layer, Inference Stack) enabling extensibility and integration with existing CI/CD, monitoring, and security tooling.