AI agent ecosystems engineered for performance and profit margins.
JMM Labs designs and ships production-grade AI systems at the intersection of technical performance and profitability. We turn prototypes into resilient platforms — optimizing latency and cost by intelligently routing between SOTA and efficient models, with security, observability, and compliance built in from day one.
- 12-Factor App
- System Prime Design
- Zero-Trust Security
- FinOps-First
What we build at JMM Labs
High-level technical consulting and end-to-end implementation for organizations transitioning from AI prototypes to robust, cost-aware production systems.
Production-Grade Agent Architecture
End-to-end designs based on Hexagonal Architecture: decoupled, testable, and vendor-agnostic AI systems engineered to move from prototype to resilient production.
Hybrid Search Engines
High-performance retrieval with Reciprocal Rank Fusion (RRF) on PostgreSQL (pgvector + TSVECTOR), tuned for relevance, latency, and cost.
Stability & Resilience Patterns
Distributed Circuit Breakers (Redis-backed) and Singleflight patterns to prevent cascading failures and cache stampedes under real production load.
Intelligent Semantic Routing
Dynamic routing between SOTA models (GPT, Claude) and efficient models (DeepSeek, Llama) based on prompt complexity and real-time budget controls.
Continuous Learning Loops
Nested learning pipelines that turn user corrections into dynamic few-shot examples, letting systems self-correct without expensive fine-tuning.
AI Security & Compliance
Custom middleware for real-time PII scrubbing (Spacy/NLP), deterministic guardrails against prompt injection, and SOC2/GDPR-aligned controls.
Explore our production-ready modules
Experience real-world implementations of our architecture patterns. These modules showcase our capabilities in building scalable, observable, and secure AI systems.
KERA
View Module →Knowledge Extraction & Retention
RAG-style document extraction with flashcard generation, model fallback, and storage controls. Built on DSPy and Semantic Caching.
Reduces LLM output tokens by 40% via Semantic Deduplication.
RAGSemantic CachingDSPyAsync WorkersSOC2VERA
View Module →Verified Expense & Receipt Parser
Deterministic receipt validation with human review for math and OCR edge cases. Optimized for edge compute with PII scrubbing.
Slashes VLM token consumption by 90% via Edge Compression.
Edge ComputeHITLDeterministic MathPII ScrubbingAURA
View Module →Audio Understanding & Retention
Async audio transcription, summarization, feedback capture, and observability. Features idempotency and nested learning loops.
100% compute savings on viral media via SHA-256 Idempotency.
IdempotencyNested Learning LoopRedis Debouncing
Have a system that needs to ship and scale?
Get in touch to discuss architecture, model strategy, or migration from prototype to production.