Architecture Decision Records
Palantir-Informed Strategic Decisions for CODITECT
Document Type: ADR Collection
Version: 1.0
Date: February 2026
ADR-001: Ontology-First Architecture
Status
PROPOSED — Awaiting validation
Context
Palantir's success demonstrates that a semantic data layer (Ontology) is fundamental to enterprise AI adoption. Their $4.4B revenue and 127% Rule of 40 score validate this architecture.
CODITECT must decide whether to:
- Build a domain-specific ontology (healthcare/financial)
- Use generic data models with AI interpretation
- Adopt an existing ontology framework
Decision
Adopt an ontology-first architecture with domain-specific object types for regulated industries.
Rationale
| Factor | Ontology-First | Generic Data Model |
|---|---|---|
| AI context | Rich semantics | Requires inference |
| Compliance | Native audit trail | Bolt-on compliance |
| Developer UX | Business objects | SQL/tables |
| Extensibility | Add object types | Schema migrations |
| Palantir evidence | Core of $4.4B platform | N/A |
Consequences
Positive:
- AI agents understand business context natively
- Compliance artifacts generated automatically
- Developer productivity via business object APIs
- Differentiation from data-centric competitors
Negative:
- Higher initial development investment
- Requires domain expertise to model correctly
- Ontology versioning complexity
- Training cost for developers
Implementation Notes
Priority Ontology Objects (Healthcare):
├── Patient
├── Provider
├── Encounter
├── Claim
├── PriorAuthorization
├── CarePlan
├── Medication
├── Diagnosis
└── Procedure
Priority Ontology Objects (Financial Services):
├── Account
├── Transaction
├── Customer
├── Compliance Check
├── Risk Assessment
├── Document
├── Approval Workflow
└── Regulatory Filing
ADR-002: Agent Architecture Pattern
Status
PROPOSED — Requires technical spike
Context
Palantir's 2026 "Agentic AI Hives" represent the evolution from "decision-support" to "decision-execution." For regulated industries, autonomous agents must operate within compliance guardrails.
Decision
Implement guardrail-constrained autonomous agents with mandatory human checkpoints for regulatory decisions.
Rationale
| Approach | Pros | Cons |
|---|---|---|
| Full autonomy | Maximum efficiency | Compliance risk, liability |
| Human-in-loop everywhere | Maximum control | Defeats automation purpose |
| Guardrail-constrained | Balanced efficiency + control | Implementation complexity |
Agent Guardrail Categories
REGULATORY GUARDRAILS
├── FDA 21 CFR Part 11 constraints
├── HIPAA PHI handling rules
├── SOC 2 access controls
└── State-specific requirements
BUSINESS GUARDRAILS
├── Approval thresholds
├── Budget limits
├── SLA requirements
└── Business hours constraints
SECURITY GUARDRAILS
├── Data access policies
├── Network boundaries
├── Encryption requirements
└── Authentication rules
RESOURCE GUARDRAILS
├── Token budgets
├── API rate limits
├── Compute quotas
└── Storage limits
Consequences
Positive:
- Automation where safe, human oversight where required
- Audit trail for all agent decisions
- Regulatory defensibility
- Palantir-equivalent functionality
Negative:
- Complex guardrail definition process
- Checkpoint latency for regulated decisions
- Testing complexity
- Guardrail maintenance overhead
ADR-003: LLM Orchestration Strategy
Status
PROPOSED
Context
Palantir's AIP demonstrates that enterprise AI requires multi-model orchestration, not single-model dependency. Model selection should be dynamic based on task characteristics.
Decision
Implement tiered model routing with automatic selection based on task complexity, regulatory requirements, and cost constraints.
Model Routing Rules
ALWAYS OPUS:
├── Regulatory interpretation
├── Compliance validation
├── Security analysis
├── High-stakes decisions
└── Complex multi-step reasoning
SONNET (DEFAULT):
├── Standard workflows
├── Document processing
├── Code generation
├── Analysis and synthesis
└── User-facing responses
HAIKU (COST OPTIMIZATION):
├── Classification tasks
├── Simple extraction
├── Boilerplate generation
├── Routine validation
└── High-volume processing
Cost-Benefit Analysis
| Scenario | All-Opus | Tiered Routing | Savings |
|---|---|---|---|
| 1M tasks/month | $15,000 | $4,500 | 70% |
| Compliance only | $5,000 | $5,000 | 0% |
| Mixed workload | $10,000 | $3,000 | 70% |
Consequences
Positive:
- 40-70% token cost reduction
- Appropriate model for task
- Scalable economics
- Quality where it matters
Negative:
- Routing logic complexity
- Potential misrouting errors
- Multi-model integration testing
- Vendor dependency spread
ADR-004: Bootcamp GTM Model
Status
ACCEPTED — Implementation in progress
Context
Palantir's AIP Bootcamp model (5 days to production) has driven 204 deals >$1M and compressed sales cycles dramatically. This represents a paradigm shift from traditional enterprise sales.
Decision
Adopt a 2-day "Compliance Bootcamp" model for healthcare/financial services demonstrations.
Bootcamp Structure
DAY 1: CONNECT + MODEL
┌────────────────────────────────────────────┐
│ Morning: Data Connection │
│ ├── EHR/claims integration │
│ ├── Sample data extraction │
│ └── Quality assessment │
├────────────────────────────────────────────┤
│ Afternoon: Ontology Setup │
│ ├── Object type configuration │
│ ├── Link establishment │
│ └── Sample queries │
└────────────────────────────────────────────┘
DAY 2: AUTOMATE + PROVE
┌────────────────────────────────────────────┐
│ Morning: Agent Deployment │
│ ├── Compliance checking agent │
│ ├── Documentation automation │
│ └── Alert configuration │
├────────────────────────────────────────────┤
│ Afternoon: ROI Demonstration │
│ ├── Time savings calculation │
│ ├── Compliance improvement metrics │
│ └── Expansion roadmap │
└────────────────────────────────────────────┘
Success Metrics
| Metric | Target | Palantir Benchmark |
|---|---|---|
| Time to value | 2 days | 5 days |
| Bootcamp → LOI | >50% | ~60% |
| Deal size average | $100K | $1M+ |
| Customer satisfaction | >4.5/5 | Not disclosed |
Consequences
Positive:
- Compressed sales cycle
- Demonstrated value before contract
- Lower CAC
- Customer advocacy
Negative:
- High preparation cost per bootcamp
- Requires domain expertise
- Not scalable without automation
- Customer data handling complexity
ADR-005: Deployment Flexibility
Status
PROPOSED
Context
Palantir's Apollo enables deployment across cloud, on-premise, edge, and air-gapped environments. For regulated industries, deployment flexibility is often a requirement, not an option.
Decision
Build deployment abstraction layer supporting cloud, on-premise, and hybrid deployments from day one.
Deployment Matrix
| Environment | Priority | Use Case |
|---|---|---|
| Cloud (AWS/GCP/Azure) | P0 | Default, fastest adoption |
| On-premise | P1 | Healthcare systems, banks |
| Hybrid | P1 | Sensitive data on-prem, compute in cloud |
| Air-gapped | P2 | Future government expansion |
Architecture Approach
┌─────────────────────────────────────────────────┐
│ CODITECT DEPLOYMENT LAYER │
├─────────────────────────────────────────────────┤
│ │
│ Application Code │
│ │ │
│ ↓ │
│ ┌─────────────────────────────────────────┐ │
│ │ Deployment Abstraction Layer │ │
│ │ (Docker + Kubernetes + Helm) │ │
│ └─────────────────────────────────────────┘ │
│ │ │
│ ├──→ Cloud Provider (EKS/GKE/AKS) │
│ ├──→ On-Premise (k3s/Nomad) │
│ └──→ Air-Gapped (Offline Package) │
│ │
└─────────────────────────────────────────────────┘
Consequences
Positive:
- Address regulated industry requirements
- Competitive with Palantir's Apollo
- Larger addressable market
- Customer flexibility
Negative:
- Infrastructure complexity
- Testing across environments
- Support burden
- Slower feature velocity initially
ADR-006: Compliance-Native Design
Status
ACCEPTED
Context
Palantir retrofits compliance onto a general platform. CODITECT has the opportunity to build compliance as a first-class architectural concern.
Decision
Design regulatory compliance (FDA/HIPAA/SOC2) as foundational architecture, not overlay.
Compliance Architecture
EVERY OPERATION IN CODITECT:
Request → ┌──────────────┐ → Execution
│ Compliance │
│ Gate │
└──────────────┘
│
┌───────────────┼───────────────┐
↓ ↓ ↓
┌────────┐ ┌────────┐ ┌────────┐
│ Audit │ │ Access │ │ Data │
│ Trail │ │Control │ │ Guard │
└────────┘ └────────┘ └────────┘
Compliance Features
| Regulation | Required Capability | CODITECT Implementation |
|---|---|---|
| FDA 21 CFR Part 11 | Electronic signatures | Native e-sig on actions |
| FDA 21 CFR Part 11 | Audit trail | Hash-chain audit log |
| HIPAA | PHI access control | Object-level security |
| HIPAA | Encryption | At-rest + in-transit |
| SOC 2 | Change management | GitOps + approval workflows |
| SOC 2 | Access review | Periodic access reports |
Consequences
Positive:
- Differentiation from Palantir
- Shorter compliance audits
- Lower customer compliance burden
- Premium pricing justified
Negative:
- Higher initial complexity
- Potential over-engineering for non-regulated
- Compliance expertise required
- Ongoing regulatory monitoring
ADR-007: Open vs. Proprietary Architecture
Status
PROPOSED — Strategic discussion required
Context
Palantir uses a proprietary, closed architecture. Customers benefit from deep integration but face lock-in. An open architecture could be a differentiation point.
Options
| Approach | Pros | Cons |
|---|---|---|
| Proprietary (Palantir model) | Control, coherence | Lock-in concerns |
| Fully Open | No lock-in, community | Fragmentation, support |
| Open Core | Balance | Complexity |
Decision
Adopt "Open Core" model: open ontology format, open integration protocols, proprietary agent orchestration and compliance engine.
Open Components
OPEN (Standards-Based):
├── Ontology schema format (JSON-LD based)
├── Data connector protocol (OpenAPI)
├── Event format (CloudEvents)
├── Audit log format (SPDX-like)
└── Export formats (FHIR, HL7, X12)
PROPRIETARY:
├── Agent orchestration engine
├── Compliance validation engine
├── LLM routing logic
├── Bootcamp automation
└── Performance optimizations
Consequences
Positive:
- Reduced lock-in concerns
- Ecosystem potential
- Standards compliance
- Easier integrations
Negative:
- Less control over customer experience
- Potential commoditization
- Open components require maintenance
- Competitive visibility
ADR-008: Mid-Market Focus vs. Enterprise
Status
PROPOSED — Market validation required
Context
Palantir targets $5M+ enterprise deals. The mid-market ($50K-$500K deals) is underserved for AI automation in regulated industries.
Decision
Focus on mid-market initially, with enterprise expansion path.
Market Segmentation
| Segment | Deal Size | Palantir | CODITECT Target |
|---|---|---|---|
| Enterprise | $5M+ | Primary | Future expansion |
| Upper Mid-Market | $500K-$5M | Secondary | Growth path |
| Mid-Market | $50K-$500K | Underserved | Primary focus |
| SMB | <$50K | Not served | Out of scope |
Product Implications
MID-MARKET REQUIREMENTS:
├── Self-service onboarding
├── Transparent pricing
├── Short sales cycles
├── Minimal services required
├── Quick time-to-value
└── Lower support burden
ENTERPRISE REQUIREMENTS (FUTURE):
├── Custom integrations
├── Professional services
├── Long-term contracts
├── Executive relationships
└── Complex compliance needs
Consequences
Positive:
- Less direct Palantir competition
- Faster sales cycles
- Scalable go-to-market
- Prove model before enterprise
Negative:
- Lower deal sizes
- May miss enterprise market timing
- Different skill requirements
- Perception as "not enterprise-ready"
Decision Summary
| ADR | Decision | Status | Priority |
|---|---|---|---|
| ADR-001 | Ontology-first architecture | PROPOSED | P0 |
| ADR-002 | Guardrail-constrained agents | PROPOSED | P0 |
| ADR-003 | Tiered LLM routing | PROPOSED | P1 |
| ADR-004 | 2-day Bootcamp GTM | ACCEPTED | P0 |
| ADR-005 | Deployment flexibility | PROPOSED | P1 |
| ADR-006 | Compliance-native design | ACCEPTED | P0 |
| ADR-007 | Open core architecture | PROPOSED | P2 |
| ADR-008 | Mid-market focus | PROPOSED | P0 |
Architecture Decision Records v1.0 — February 2026