How Your Data Stays Private

Every step of the pipeline runs inside your network. Nothing leaves.

Document Ingestion

Internal files uploaded securely

Streaming Ingestion

Indexed and prepared for retrieval

Layered Retrieval

Vector, keyword, and graph search

Merge & Rank

Top passages selected by relevance

LLM Inference

Accurate response generated

The platform ingests your company's internal files, indexes them through streaming ingestion, and stores them for layered retrieval - combining vector, keyword, and graph search. When a user asks a question, the system merges and ranks the most relevant passages across all three search methods, passes them to the LLM, and generates an accurate, context-aware response. The entire process runs inside your corporate boundaries. No data exits your network, and users get fast, natural-language access to your institutional knowledge.

Key Capabilities

Natural Language Document Search

Query internal documents using conversational language - no SQL required.

Layered Retrieval Accuracy

Pull relevant context from your proprietary data using vector, keyword, and graph search for accurate, grounded responses.

Enterprise Integration

Connectors for SharePoint, NetSuite, Salesforce, Google Drive, and more.

Familiar Output Formats

Generate reports in Word, PowerPoint, and formats your teams already know.

Enterprise Security

SSO integration, role-based access control, and comprehensive audit logging.

No Vendor Lock-In

Open-source foundation means fine-tuned models become your intellectual property.

Taking Your AI & Data Private

Cognetryx delivers secure, customized AI solutions that let companies harness AI without sending sensitive data to the cloud.

By moving processing inside your network, you completely eliminate the privacy risks of public APIs while giving your teams instant access to the exact procedural knowledge they need to do their jobs without retraining or workflow disruption.

Enterprise-Grade Technology Stack

Built on proven open-source technologies for performance, security, and long-term flexibility.

Our stack is designed from the ground up to integrate cleanly with your existing IT operations, giving you cloud-native agility within the absolute security of your own data center.

Key Features

Model Serving

vLLM + NVIDIA GPUs with open-weight LLMs for optimized private inference.

Layered Retrieval

Vector, keyword, and graph search with contextual ranking for precise, zero-hallucination answers

Orchestration

LangChain for agentic reasoning, query routing, and multi-step workflows.

Data Layer

NVMe SSDs and object storage compatible with existing enterprise data lakes.

APIs & Deployment

Docker/Kubernetes containerization with FastAPI for CI/CD readiness.

Security & Observability

SSO (Okta/Azure AD), RBAC, and granular Prometheus monitoring.

Flexible Infrastructure Choices

Choose the deployment model that fits your organization's requirements and risk profile.

Private Cloud / Hybrid

For organizations with existing private cloud infrastructure, Cognetryx solutions can be deployed within your secure environment with cloud-like agility.

  • Cloud-like agility and scalability
  • Maintains full data sovereignty
  • Compliance requirements met by architecture
  • Integrates with existing cloud investments
  • AWS/Azure isolated VPC options available

Total Cost of Ownership Benchmark

Compare the true Year-1 TCO across the enterprise AI landscape. Regulated industries are moving away from unpredictable cloud meters and heavy DIY burdens in favor of fixed-cost, locally-hosted infrastructure.

Solution Approach
Software License
Implementation
Infrastructure
Est. Year 1 TCO
Strategic Risk & Impact
Palantir AIP (locally-hosted)
$300K – $1M+
$200K – $500K
Customer-supplied
$600K – $1.5M+
High-touch enterprise deployment; premium pricing model tailored primarily for federal defense applications.
Scale AI / Donovan
$500K+
$300K+
Gov-supplied
$1M+
Primarily US Fed/DoD focused; not commercially available. Creates significant barrier to entry for standard enterprise deployment.
IBM watsonx (locally-hosted)
$150K – $400K
$150K – $400K
$100K – $250K
$500K – $1M
Legacy ecosystem integration; requires substantial ongoing professional services reliance to extract value.
DataRobot (locally-hosted MLOps)
$120K – $300K
$80K – $200K
$80K – $200K
$300K – $700K
Strong legacy in predictive MLOps but lacks native architectural focus on generative AI and agentic workflows required for modern unstructured data.
Open-source DIY (vLLM, FAISS)
$0
$150K – $400K (SI)
$185K – $400K
$350K – $800K
High hidden costs; diverts internal engineering focus and carries significant support SLA and compliance risks.
Note: TCO figures are Mid-Market estimates based on publicly available information and market intelligence. Actual pricing varies by deal structure, infrastructure requirements, and negotiation.

Why Open Source Matters

The entire stack is designed with open-source licensing (Apache 2.0, LLaMA 4 Community License) to eliminate vendor dependencies and unpredictable licensing costs.

Open models now rival proprietary systems in enterprise use cases while providing deployment flexibility that closed vendors cannot match. If you fine-tune your model, you will always own that IP - no asterisks.

Key Benefits

Your Fine-Tuned Models Are Your IP

Unlike cloud AI, customizations belong to you permanently.

Escape Vendor Lock-In

No dependency on a single vendor's pricing or roadmap decisions.

True Cost Predictability

Fixed infrastructure costs, unlimited queries, no per-token surprises.

Freedom to Innovate

Customize, extend, and evolve without asking permission.

Future-Proof Your Investment

Migrate between hardware, upgrade models, scale on your terms.

Ready to stop renting AI?

Calculate your specific ROI and see how a fixed-cost, locally-hosted infrastructure transforms your balance sheet and secures your proprietary data.