We Built a Mission-Critical AI Application in 48 Hours. Here's How.
TEMPs (Testing and Evaluation Master Plans) take up to two years to complete in defense organizations. A TEMP is the authoritative plan that defines how a weapons system will be tested, what success looks like, what measures matter, and how a program proves operational readiness. The timeline isn't bureaucracy, it's complexity. TEMPs require deep coordination across stakeholders, careful alignment to policy and test standards, and constant reconciliation of prior test results, legacy documents, and program-specific constraints. The knowledge needed lives across hundreds of artifacts: prior TEMPs, test plans, operational test reports, requirements documents, standards, wargame outputs, program memos, lessons learned, etc. Much of it is distributed across locations, stored in different formats, segmented by security levels.
Recently at AFOTEC in Albuquerque, Air Force and Space Force leaders described the bottleneck. GenAI.mil helps with drafting and summarization, but without secure integration to these distributed sources, a chatbot can only help at the margins. It cannot reliably build a complete, defensible TEMP grounded in precedent and evidence.
This was an opportunity to prove what the Kamiwaza platform enables. We captured requirements in the room, turned notes into a problem statement, and in 48 hours built Tempo, a working proof-of-concept application that was live and ready for demonstration.
The speed wasn't about working faster. It was about platform architecture that enables speed by eliminating the bottlenecks that normally consume months or years.
How the Platform Eliminates Data Engineering Time
Most AI Projects Start With 6-18 Months of Data Work
Traditional schools of thought say that AI demands centralization. Extract data from source systems, transform it to common formats, load it into a data lake, build pipelines, establish governance, manage access controls. Organizations spend months or years and millions of dollars on data engineering before they can start building applications. By the time the infrastructure is ready, requirements have changed or stakeholders have moved on, if the migration was even successful.
This data engineering tax isn't a technical problem, it's an architectural one. The assumption that AI requires centralized data creates the bottleneck and is wrong.
Kamiwaza's Distributed Data Engine (DDE) eliminates this entirely through locality-aware data operations. The DDE deploys lightweight nodes wherever data lives: cloud VPCs, on-premise servers, edge locations, across security boundaries. Each node processes information in place without requiring extraction or centralization.
The architecture operates through distributed nodes at each data location, a command and control layer that orchestrates across the environment, and local processors that execute operations within security boundaries. When Tempo needs data, the platform routes requests to the appropriate nodes, processes locally, and returns only results, never raw data.
For Tempo, this meant we connected to the disparate data sources, such as prior TEMPs, test reports, standards libraries, and all across different security levels. The DDE indexed content wherever it lived. No extraction phase, no transformation pipeline, no loading process, no centralized repository to build. We pointed the platform at the distributed sources on Monday and started building immediately.
Time saved: 6-18 months of data engineering eliminated.
How the Inference Mesh Coordinates AI Across Distributed Environments
AI Needs to Reason Across All Available Information
Once data is accessible, AI workloads need to run against it. In centralized architectures, this is straightforward: everything is in one place. In distributed environments, this becomes the next bottleneck. How do you coordinate inference operations across multiple locations, security domains, and data sources without centralizing first?
The Inference Mesh solves this by orchestrating AI workloads. It operates as a distributed inference layer that routes requests to appropriate nodes based on data locality and security context, manages model deployment across distributed infrastructure, and aggregates results from multiple nodes while maintaining security boundaries.
For Tempo, the Inference Mesh enabled AI to pull insights from the complete body of relevant material across security domains. A query about test objectives might require reasoning across requirements in one system, prior test results in another, and standards documentation in a third. The Inference Mesh coordinated the operations across these sources. It processed data locally for security compliance and synthesized results without ever consolidating the underlying data.
The platform handled what would traditionally require weeks of custom integration code: routing requests to correct data locations, managing distributed inference execution, aggregating results across security boundaries, maintaining audit trails of what was accessed and processed.
Time saved: weeks of building custom integration logic eliminated.
How Ontology Automation Replaces Manual Knowledge Engineering
Understanding Relationships Requires Structure
Generic search and retrieval isn't enough for complex domains and workflows. Explicit structure is necessary to understand the connections between requirements and test objectives, how those objectives map to measures, and how the measures relate to previous test results. In traditional approaches, knowledge engineers spend months manually building ontologies, defining entities, mapping relationships, creating taxonomies.
The platform builds knowledge graphs through automatic entity extraction based on defined rules and domain patterns. The ontology service processes documents as they're ingested, identifying domain entities like programs, systems, requirements, test objectives, measures, threats, constraints, stakeholders, and evidence. It discovers relationships automatically, finding connections between entities across documents and systems.
The extraction uses both pattern matching and semantic understanding to identify entities and normalize them across variations. "Authentication API v2.1" and "Auth API version 2.1" are recognized as the same entity. The platform builds a graph where nodes are entities and edges are relationships: "requires," "validates," "depends on," "provides evidence for."
This graph updates continuously as new documents arrive. When a new test report is ingested, entities are extracted and linked to existing knowledge automatically.
For Tempo, this meant the platform extracted entities from hundreds of documents, built the knowledge graph without manual mapping, and enabled reasoning about connections that would take knowledge engineers weeks to define. We provided the extraction rules for the TEMP domain, and the Kamiwaza platform did the work.
Tempo doesn't retrieve documents, it reasons about connections. Navigate from objectives to evidence, from measures to historical usage, from requirements to validation data. Every output includes citations pointing to source documents and confidence scores showing quality, coverage, and consistency across sources.
Time saved: months of manual ontology construction eliminated.
How Inherited Security Removes Approval Cycles
Security Reviews Normally Block Technology Deployment
Even when an application works technically, deploying it in defense or regulated environments requires security reviews, authority to operate processes, compliance validation, access control audits. Each new application triggers a new security project. These approval cycles add weeks or months to technology delivery timelines.
Relationship-Based Access Control (ReBAC) enforces context-aware permissions at the platform level. ReBAC goes beyond role-based access control by understanding organizational relationships and using them to determine authorization. Access decisions consider not just who you are, but what you're working on, who you're working with, and what organizational relationships are relevant.
The platform maintains a relationship graph that captures organizational structure, project assignments, clearance levels, and delegation chains. When a user queries the knowledge base, ReBAC filters results based on their position in the relationship graph. This filtering happens at the graph level, hiding not just documents but entities and relationships in the knowledge graph itself, which also means that information cannot be inferred, preventing the accidental disclosure of classified information.
For Tempo, ReBAC meant users only saw test documentation, requirements, and evidence they were authorized to access. A program manager saw their program's full context. A test engineer saw relevant technical details across programs. A compliance reviewer saw what they needed for oversight. The same application, the same knowledge graph, filtered dynamically based on relationship context.
Applications built on Kamiwaza inherit the platform's security posture automatically. Tempo didn't implement access controls, it inherited ReBAC. For defense deployments, this means IL4 through IL7 alignment, authority to operate inheritance where applicable, and compliance with mission environment requirements. Applications don't create new security projects because they operate within the platform's existing security boundary.
Time saved: months of security review cycles eliminated.
How the Platform Handles the Heavy Lifting
What Tempo Actually Does
Tempo proves the Kamiwaza platform works. Users explore the knowledge graph and discover relationships with citations. The chat interface is backed by the graph and ontology, targeted questions get answers with direct citations and confidence scoring.
Tempo constructs TEMPs using rules, mappings, and structured data. It doesn't generate text, it assembles from the graph. The platform queries the graph for relevant entities and relationships, populates sections with structured information, ensures traceability by maintaining links to source documents, and maintains coherence by enforcing domain constraints. Output is structurally correct, grounded in precedent, ready for expert refinement.
The workflow view surfaces gaps explicitly: missing inputs where required entities aren't in the graph, sections needing SME validation where confidence scores are low, unresolved conflicts where sources contradict, required approvals tracked through the relationship graph, items blocked by access issues flagged by ReBAC. This makes the last 20-30% of human work visible and trackable.
The audit trail is built in by design: what was generated, what sources were used, what decisions were made, how changes propagated. This supports formal reviews, compliance, and governance.
All of this functionality was delivered in 48 hours because the Kamiwaza AI Orchestration platform provides the foundation. We didn't build data pipelines, we used the DDE. We didn't write integration logic, we used the Inference Mesh. We didn't manually construct ontologies, the platform generated context graphs. We didn't implement access controls, we delivered ReBAC. We wrote application logic that orchestrated platform capabilities.
The Pattern Works Beyond Defense
This isn't the enterprise AI problem for defense alone. Any enterprise organization with distributed knowledge, strict access controls, and mission-critical processes faces the same barriers to creating digital assistants: data engineering time, integration complexity, knowledge structure requirements, security approval cycles.
Insurance: Carriers have policy documentation, claims history, actuarial models, and regulatory requirements scattered across legacy platforms spanning decades. The same platform approach eliminates months of data centralization work, automatically extracts policy entities and risk relationships, and maintains privacy boundaries through ReBAC. Digital underwriting assistants that might take IT 12-18 months to build traditionally can be deployed in weeks.
Healthcare: Clinical trial protocol development requires synthesizing prior trials, regulatory guidance, investigator capabilities, and safety records distributed across research sites, CROs, and regulatory bodies, all governed by HIPAA and GCP. The platform connects distributed data sources without centralization, builds knowledge graphs of trial relationships automatically, and enforces access controls that keep protected health information within appropriate boundaries. Protocol assistants deliver in weeks, not quarters.
Financial Services: Regulatory compliance and audit response require pulling evidence from trading documentation, compliance files, and policy libraries spanning business units, jurisdictions, and legacy systems. The platform eliminates the data consolidation project, automatically structures compliance relationships, and maintains access boundaries for sensitive trading data. Compliance assistants that normally require 6-12 months of data work can be built in weeks.
What This Means for Enterprise AI
The barrier to useful enterprise AI isn't AI models, those are commoditized. The barrier is the time required to prepare distributed data, build integration infrastructure, create knowledge structures, and navigate security approvals.
Traditional AI projects fail because they require:
- 6-18 months of data migration that takes longer than the business problem's lifespan
- Custom integration logic for each distributed environment
- Weeks or months of manual ontology construction
- New security projects for each application
- Point solutions that become technical debt
Kamiwaza eliminates these bottlenecks by treating the platform as the product. The Distributed Data Engine, Inference Mesh, automatic ontology generation, and ReBAC aren't application features. They're the foundation that makes rapid, secure, repeatable delivery possible.
Tempo proves the platform works. Kamiwaza delivered in just 48 hours what traditionally takes 12-18 months of manual processes. This speed was possible because Kamiwaza eliminates the common bottlenecks that delay traditional approaches.
We built Tempo to prove what's possible with the right architecture. The real question isn't "can you build a demo fast," it's "can you deliver production capability at business speed without the data engineering tax, without custom integration projects, without manual knowledge engineering, without new security reviews for each application?"
The answer is yes, if you build on a platform designed for distributed data reality. Distributed knowledge isn't a problem to solve by centralizing. It's how organizations operate. The right platform turns that complexity into advantage by eliminating the bottlenecks that make traditional AI deployment slow.
If you're interested in how we might do this for your organization, reach out or learn more at kamiwaza.ai.
