Engineering the
Digital Genome.
We rebuild intelligent systems from the ground up — precision agent engineering, multi-modal transcription pipelines, and self-adapting AI orchestration matrices.
Rebuilding the Engineering Genome of Intelligent Systems
The Sequence
Three hierarchical layers — from molecular knowledge architecture to distributed agent expression. Each layer a precision-engineered genome of your AI stack.
Base Pairs
Foundation Protocol
The atomic building block of every agent. We engineer your system's core prompt architecture, knowledge indexing, and vector memory at the molecular level — eliminating noise from the base up.
- ▸Context window optimization
- ▸Knowledge base curation
- ▸Prompt DNA engineering
- ▸Vector index precision
Transcription
Data Pipeline Protocol
Real-time multi-modal data sensing, routing, and retrieval augmentation. Our transcription engine dynamically maps information streams into structured, queryable intelligence with sub-20ms latency.
- ▸Multi-modal RAG pipeline
- ▸Dynamic context routing
- ▸Real-time indexing
- ▸Semantic deduplication
Expression
Orchestration Protocol
Distributed AI agent matrices that express intelligent behavior autonomously. Multi-agent coordination, task decomposition, and adaptive self-correction — running continuously without human scaffolding.
- ▸Agent mesh orchestration
- ▸Adaptive task planning
- ▸Fault-tolerant execution
- ▸Cost-optimized routing
The Werkes
Real-time performance telemetry from our production agent mesh. No marketing claims — only live instrumented data from systems operating at scale.
TELEMETRY_SOURCE: production_cluster_01 / DATA_REFRESH: 1s interval / ALL_METRICS: verified
Published Findings
Our research output is the proof-of-work for every system we deploy. Open science, applied engineering.
Genomic Prompt Architecture: A Hierarchical Framework for Multi-Agent LLM Systems
We introduce a novel hierarchical prompt structuring methodology that mirrors biological gene expression — enabling modular, reusable, and evolvable prompt DNA across distributed agent networks.
Low-Latency RAG Transcription: Sub-20ms Retrieval via Adaptive Index Sharding
A production-grade retrieval-augmented generation architecture achieving P95 latency of 14ms at scale through dynamic index partitioning and speculative retrieval prefetching.
Expression Matrices: Self-Correcting Agent Orchestration Under Adversarial Conditions
We present a fault-tolerant multi-agent orchestration protocol inspired by cellular redundancy mechanisms, demonstrating 97.3% task completion across 10,000 adversarial test scenarios.
dnawerkes.ai is a 2026 seed-stage startup engineering the deep infrastructure layer of intelligent systems. We believe most AI deployments fail at the engineering layer — not the model layer.
Our team comes from ML infrastructure, systems programming, and computational biology. We apply the precision of genetic engineering to AI architecture.
- 01 / Precision over scaleEvery component engineered to molecular tolerance — not bolted on.
- 02 / Measurable outcomesNo marketing claims without instrumented production data.
- 03 / Open researchCore findings published. We build the field, not just a product.
- 04 / Minimal footprintMaximum intelligence from minimum token expenditure.