TECHNOLOGY OVERVIEW · FOR TECHNICAL EVALUATORS
The tech behind
the intelligence
layer.
Built for technical evaluators who need to understand what MSIL is running on before they commit. Cloud infrastructure, AI model selection, data architecture, security posture, and API design — documented clearly, without marketing language.
MSIL TECHNOLOGY STACK
Cloud InfrastructureAWS / Multi-cloud
AI Model LayerOpenAI + Anthropic + Custom
Data LayerPostgreSQL + Redis + Vectors
Event BusKafka + Custom Orchestrator
API LayerREST + GraphQL + WebSockets
FrontendReact 19 + TypeScript + Vite
ObservabilityOpenTelemetry + Datadog
SecuritySOC 2 Type II + Encryption at rest
LAYER BY LAYER
ARCHITECTURE LAYERS
What each layer
does and why.
01
Cloud Infrastructure
MSIL runs on AWS with multi-region failover. All compute is containerized via ECS, with auto-scaling configured to handle intelligence event spikes without performance degradation. Data residency options available for regulated industries.
AWS ECSAuto-scalingMulti-region99.9% SLA
02
AI Model Layer
We run a hybrid model architecture — not locked to a single provider. OpenAI and Anthropic models handle reasoning and language tasks. Custom fine-tuned models run classification, anomaly detection, and domain-specific intelligence. Model selection per task is automatic.
OpenAI GPT-4Claude 3.5Custom fine-tunesModel routing
03
Data & Memory Layer
Persistent memory is the foundation of MSIL. PostgreSQL handles structured operational data. Redis provides sub-millisecond access to hot context. pgvector and Pinecone handle semantic search across the intelligence graph. All data encrypted at rest and in transit.
PostgreSQLRedispgvectorPinecone
04
Event & Agent Architecture
MSIL runs on an event-driven architecture. Kafka handles the event bus — every data change, system event, and external signal flows through as an event stream. The agent orchestrator consumes events, routes them to the appropriate agent, and manages execution with boundary enforcement.
Apache KafkaAgent OrchestratorBoundary EngineAudit Logger
05
API Layer
REST APIs for standard integrations. GraphQL for complex queries from portal frontends. WebSockets for real-time intelligence event streaming. Every API endpoint is rate-limited, authenticated via JWT + OAuth 2.0, and fully documented with OpenAPI spec.
RESTGraphQLWebSocketsOpenAPI 3.1
06
Security Architecture
SOC 2 Type II compliant. All data encrypted at rest (AES-256) and in transit (TLS 1.3). Zero-trust network architecture. Role-based access control at every layer. Agent boundaries enforced at the infrastructure level, not just application level. Full audit trail for every operation.
SOC 2 Type IIAES-256TLS 1.3Zero-trust
WHY THESE CHOICES
Technology decisions
with reasoning.
We made deliberate technology choices for MSIL. Each one is driven by the requirements of persistent, always-on enterprise intelligence — not by what's trendy or what we already knew.
We document our reasoning because technical evaluators deserve to understand the decision-making, not just the outcome. Every choice below has a clear rationale and a clear alternative we considered and rejected.
Kafka over SQS for the event bus
MSIL processes millions of events. Kafka's throughput, replay capability, and consumer group architecture handle enterprise-scale event streams that SQS cannot. Replay is essential for backfill and debugging.
Multi-model AI routing
No single model excels at every intelligence task. We route each task type to the model with the best performance profile for it — reasoning to frontier models, classification to fine-tuned models, cost managed automatically.
Boundary enforcement at infrastructure level
Agent boundaries enforced only at the application layer can be bypassed. We enforce boundaries at the infrastructure level — network policies, IAM roles, and execution contexts that agents cannot override regardless of their instructions.
pgvector over dedicated vector DB
For most enterprise deployments, pgvector within PostgreSQL provides sufficient vector search performance while eliminating an additional infrastructure dependency. We move to Pinecone only when semantic search volume justifies the dedicated infrastructure.
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