Enterprise Intelligence Engineering
Bring AI In-House.
ConservaTech Networks designs, deploys, and operates private AI infrastructure and governed intelligence systems that give organizations control over their data, models, knowledge, costs, and operations.
Open-source across the stack. Customer-controlled by design. External models only when needed.
- Private AI Infrastructure
- Knowledge Network
- ConservOS
Designed, deployed, and operated by ConservaTech Networks
Enterprise AI is becoming another fragmented software layer.
Organizations are adding AI subscriptions, assistants, models, and agents without gaining control of the underlying infrastructure or knowledge. Data remains fragmented, costs are difficult to predict, and critical context stays trapped across disconnected systems.
Fragmented knowledge
Important information is spread across documents, databases, applications, and individual employees.
Uncontrolled AI use
Teams send sensitive work into disconnected tools with inconsistent policies and oversight.
Vendor dependence
Models, infrastructure, data access, and operating costs are controlled by outside platforms.
No operational memory
Answers and decisions disappear instead of becoming durable organizational knowledge.
The foundation for customer-controlled enterprise AI.
Private AI Infrastructure
Customer-controlled compute, model hosting, retrieval, storage, networking, identity, and security.
ConservaTech Knowledge Network
Governed enterprise knowledge, evidence, relationships, historical state, computed facts, and digital twins.
ConservOS
Governance for models, agents, tools, workflows, permissions, approvals, actions, and organizational memory.
Managed Operations
Continuous operation of infrastructure, models, retrieval, integrations, capacity, security, and governance.
Built on this foundation
Run privately by default
Reach outside only when justified.
Workload
A user, application, agent, or ConservOS workflow requests intelligence.
Policy and Routing Gateway
Applies data boundaries, model policies, cost limits, capability requirements, and operational authority.
In-House AI
Sensitive, routine, persistent, and predictable workloads.
Private Cloud
Customer-controlled elastic capacity and temporary expansion.
Approved External AI
Specialized models, frontier reasoning, multimodal tasks, or bounded capacity needs.
Governed Return
Validate the result, obtain required approval, record the audit trail, and preserve the approved outcome.
Use the lowest-total-cost approved route that satisfies the required data boundary, quality, latency, capability, reliability, and operational authority.
Open by Design
Open-source across the stack—not just the model.
ConservaTech builds private AI environments using open technologies across model hosting, retrieval, orchestration, integrations, knowledge systems, observability, and infrastructure operations.
Own the infrastructure
Deploy on customer hardware, private cloud, dedicated hosted infrastructure, or a governed hybrid environment.
Choose the models
Host approved open models privately and use outside providers selectively instead of committing every workload to one vendor.
Preserve portability
Keep enterprise knowledge, integrations, policies, and operational memory under customer control.
Most enterprise AI platforms ask organizations to move intelligence into a proprietary environment. ConservaTech builds the intelligence environment around the organization.
Public Proof
Public proof of the architecture.
Hammer Tavern
A public intelligence environment showing how fragmented environmental, infrastructure, historical, and public data can become governed, evidence-backed intelligence.
Explore Hammer Tavern
Mammoth Central
A working sports-intelligence experience showing how identities, teams, players, games, historical state, evidence, and analysis can operate through the same ConservOS architecture.
Explore Mammoth CentralThe same architecture can be deployed privately around an organization’s own systems, knowledge, and operations.
Start with the infrastructure. Expand into intelligence.
Assess
Define workloads, data boundaries, systems, risks, costs, and requirements.
Deploy
Establish customer-controlled compute, models, retrieval, identity, and monitoring.
Connect
Integrate documents, databases, applications, APIs, repositories, and tools.
Govern
Add policy, approvals, evidence, authority, workflow control, and memory.
Operate
Monitor infrastructure, models, integrations, capacity, cost, and intelligence quality.