Systems Architecture · AI Engineering · Freelance

I build systems that think clearly

AI architecture, agentic systems, and neuro-symbolic design. I help teams ship reliable, auditable AI — where the model is powerful and the harness makes it trustworthy.

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What I Build

Services

From architecture to deployment — hybrid systems designed for reliability, not demos.

01

AI Systems Architecture

Design the full pipeline: LLM orchestration, tool routing, state management, validators, and scoring loops. The harness that makes models production-ready.

Agent Harness Router Design Tool Contracts State Machines
02

Neuro-Symbolic Integration

Bridge neural inference with explicit logic: knowledge graphs, constraint validators, symbolic postprocessing. Reduce hallucinations, increase auditability.

Knowledge Graphs GraphRAG Constraint Systems Verification
03

Research & Technical Writing

Translate complex AI concepts into clear, structured documentation. Research briefs, architecture specs, glossaries, and technical position papers.

Research Briefs Architecture Docs Position Papers
04

Full-Stack Development

Production frontends and backends. Interactive data visualizations, dashboards, APIs, and tooling. Clean code, tested, deployed.

TypeScript React Node.js Python D3.js
61
NeSy Glossary Terms
6
Architecture Layers
<2%
Target Hallucination
CS
Comp-Sci Foundation

How I Work

The Approach

I treat every AI system as a hybrid pipeline with clearly separated concerns. The model handles interpretation and generation. Everything else — routing, validation, state, memory, scoring — is explicit, deterministic, and auditable.

This isn't theoretical. It's the same architecture pattern behind every production agent that actually works, from Claude Code's leaked orchestration kernel to enterprise composite AI systems.

Logic adds a level of confidence that inference alone will never achieve.

I write in implementable components first: state model, router loop, tool contracts, validators, memory graph, eval hooks. Then optionally map the above to "neurosymbolic." Precision ships.

λ

LLM as Interpreter

Hypothesis generation, intent understanding, natural language glue — not source of truth.

Deterministic Router

Same inputs + same state = same plan. Tool selection and sequencing with transactional semantics.

G

Memory Graph

Entities, relationships, prior runs. Structured retrieval that goes beyond vector similarity.

Hard Validators

Constraints, type checks, safety bounds. Independent of the model — pass or block.

Scoring & Rollback

Evaluate, revise, retry, backtrack. Iterative loops with transactional guarantees.


Let's Build

Ready to ship something reliable?

I'm available for freelance architecture work, consulting engagements, and research collaborations.

daverobertson9353@gmail.com →