Applied AI Infrastructure

Turn raw operational data into decisions your team can trust.

AuraForge is the layer between the data you already have and the decisions you need to make. Production-grade LLM pipelines, grounded analytics, and automation — running on AWS, governed end to end.

10×
Faster time-to-insight
99.9%
Pipeline uptime target
SOC-2
Ready architecture
AWS Amazon Bedrock Python PostgreSQL / RDS Vector DBs Kubernetes / EKS S3
The platform

One layer between your data and every decision

AuraForge unifies ingestion, reasoning, and delivery so teams stop stitching tools together and start shipping intelligent products.

01

Data Ingestion Engine

Connect databases, warehouses, and APIs. We normalize messy operational data into clean, query-ready structures automatically.

02

LLM Reasoning Pipelines

Retrieval-augmented generation with guardrails, evaluation, and caching — answers stay grounded, fast, and cost-controlled.

03

Insight & Analytics

Natural-language querying over your own data. Ask a question, get a chart, a number, and the SQL that produced it.

04

Intelligent Automation

Trigger workflows from model outputs — alerts, reports, and actions that run without a human in the loop.

05

Governance & Security

Row-level access, audit logs, and PII redaction baked in. Your data never leaves boundaries you control.

06

Observability

Track latency, cost, and quality of every model call. Know exactly what your AI is doing in production.

How it works

From connection to insight in three steps

01 · Connect

Connect

Securely link your databases, warehouses, and APIs. Read-only by default, encrypted end to end.

02 · Configure

Configure

Define the metrics, models, and guardrails that matter. We handle the pipelines, retries, and scaling.

03 · Decide

Decide

Query in natural language, receive grounded answers, and automate the actions that follow.

Built on AWS

Cloud-native from day one

AuraForge runs entirely on AWS. We use managed inference, storage, and analytics services so the platform scales cleanly without operational overhead — and so every customer deployment stays inside a boundary they control.

Inference

Amazon Bedrock for managed model access with per-call cost and latency tracking; SageMaker for evaluation workloads.

Data

RDS (PostgreSQL) and S3 for storage, with a vector store for retrieval. Read-only connectors keep source systems untouched.

Compute

EKS for pipeline orchestration and autoscaling, with Lambda for event-driven automation triggers.

Early access

Let's build your AI layer together

Tell us about your data and what you want to decide faster. We reply within two business days.

Request access →