Private AI Systems for
Regulated Enterprises.
DataSpeckle designs and deploys private AI systems for organizations that cannot send operational data to public APIs. Connect LLMs, document intelligence, and predictive models directly to your existing systems—while keeping sensitive data inside your controlled environment.
The Public Cloud Compromise is Dead.
For years, enterprises were forced into a false choice: leverage powerful AI models by exposing proprietary data to third-party APIs, or maintain security by doing nothing.
Bare-Metal Intelligence.
By deploying highly-specialized automation engines directly into your existing infrastructure, we bring the compute to your data—not the other way around.
Scale Without Exposure.
Break through the historical bottlenecks of manual data processing and compliance audits, achieving exponential operational scale while tightening your security posture.
Bare-Metal Engineering.
Sovereign Control.
We manage the heavy lifting—ETL pipelines, vector embeddings, and bare-metal orchestration—so your enterprise can focus strictly on operational output.
Private Data Ingestion
Connect native ERPs, SQL databases, and unstructured file stores. No application data leaves the configured private environment during parsing, OCR, or vector embedding.
Sovereign RAG Architecture
Query millions of internal documents using semantic vector search. Your proprietary knowledge base is indexed locally, never exposing IP to public API endpoints.
Traceable Provenance
Provide citation-grounded responses with configurable validation. Generated insights include source-linked output where supporting evidence is available.
Air-Gapped Agentic Action
Deploy autonomous agents that securely update internal CRMs, file tickets, or execute SQL commands strictly inside your firewall sandbox.
Native Identity & RBAC
Seamlessly map to Okta, Azure AD, and PingIdentity. If an employee lacks file permissions in the source system, the model automatically redacts it.
Cryptographic Audit Trails
Log every token, prompt, and system execution. Designed to support HIPAA/PIPEDA-aligned deployments with comprehensive internal telemetry.
Private Data Ingestion
Connect native ERPs, SQL databases, and unstructured file stores. No application data leaves the configured private environment during parsing, OCR, or vector embedding.
Sovereign Deployment Matrix.
We decouple machine learning from public cloud APIs. Manage proprietary models natively within your perimeter for fixed-cost, secure scalability.
Customer-Controlled Deployment
No application data leaves the configured private environment. We manage the architecture, but you retain sole custody of the encryption keys, network gateways, and storage buckets.
Fixed Infrastructure Pricing
Eliminate unpredictable token markups. By utilizing bare-metal hardware, you pay exclusively for raw compute, enabling ROI at scale.
Regulatory Compliance
Engineered strictly to support defense, financial, and healthcare data enclaves directly out of the box.
Zero-Egress Fine-Tuning
Adapt open-source foundational models into highly specialized domain experts using your internal documentation—without leaking intellectual property.
Industry Solutions Architecture.
Deploy private AI workflows specifically engineered around your sector's data gravity, operational latency, and strict InfoSec constraints.
The 4-Phase Execution Engine.
From initial audit to production deployment. Engineered for rapid integration without disrupting your current operations.
Diagnostic Assessment
Evaluate your existing workflow bottlenecks, data readiness, and baseline operational costs in a targeted 60-minute session.
Architectural Design
Map the exact integration points for your ERPs (SAP, Oracle) and define the hardware topology required for your target ROI.
Secure Deployment
Deploy the custom automation engine directly into your existing infrastructure. Zero downtime, zero external data leakage.
Autonomous Scaling
Activate real-time monitoring agents to track precision metrics, system accuracy, and ongoing financial impact.
Reported Pilot Outcomes
Based on genuinely documented internal scoping
Deployments
Scope & customer type clarified upon request
Data Privacy Maintained
No application data leaves the private environment
Stop experimenting. Start executing.
Data Readiness Audit
Timeline: 2 Weeks
Best For
Teams assessing if their infrastructure can support a sovereign AI initiative.
Deliverable
Comprehensive architecture gap analysis and 90-day deployment roadmap.
Private LLM Sprint
Timeline: 4 Weeks
Best For
Companies needing to safely test Generative AI on proprietary data.
Deliverable
A functioning, air-gapped LLM prototype deployed in your sandbox.
Targeted AI Pilot
Timeline: 8-12 Weeks
Best For
Operations leaders ready to solve a specific forecasting or automation bottleneck.
Deliverable
A deployed, measurable pilot program running on your live data.
Production & MLOps
Timeline: Ongoing
Best For
Teams needing enterprise-grade scaling for existing models.
Deliverable
Ongoing bare-metal engineering, model monitoring, and SLA-backed operations available under applicable service agreements.
Map Your AI Deployment Strategy.
Evaluate your current infrastructure and workflows to identify viable paths for private AI deployment. Complete the assessment to receive a preliminary opportunity map.