AI Architecture
You Can Actually Operate.
Agent Leverage
Without the Guesswork.
Cloud Gatherer Labs is a consulting practice for AI architecture and agent harness design. We help teams clarify the problem, design the right system, prototype the risky parts, and hand off a plan their builders can execute with confidence. That work draws on experience shipping federal-scale ML, hardening Apple endpoints, building field-ready software, and keeping live services up under load.
We help teams with real operational constraints architect AI systems stakeholders can trust, and design the harnesses that turn coding agents into disciplined contributors. Our role is to assess, architect, prototype, and hand off a path your team can execute.
An ML system designed for audit, not retrofitted for it.
Most AI consultants hand over a prototype and leave the hard questions for later: where the data flows, what must be logged, what has to stay local, who approves access, how the system is evaluated, and what happens when it drifts. We start there.
Sometimes the constraint is a formal compliance regime. Sometimes it's a small business that needs something secure, understandable, and supportable without a large platform team. In both cases, our role is the same: assess the constraints, design the architecture, prototype the risky parts, and hand your builders an approach that won't fight them later.
- Threat modeling and workflow or data-flow analysis for ML + agent systems
- Reference architectures: lineage, provenance, key management, audit hooks
- Design patterns for local-first, on-device, and air-gapped inference
- Evaluation frameworks your QA team and your auditor both understand
- Prototype builds that de-risk the hardest technical calls before commitment
- Compliance-aware design — we work with your compliance team, not around them
The hard part isn't the model.
It's the harness.
Coding agents don't fail because the model is bad. They fail because the harness around the model is thin: unstructured context, no persistent memory, ambient file-reading, no guardrails, and no review loop. We design the surrounding system. Tiered context that fits the window. Scoped sub-agents with restricted tool belts. Filesystem-backed knowledge graphs that survive across sessions. Research → plan → execute → verify loops with pre-commit hooks, architectural fitness tests, and LLM-judge review.
We use these ideas ourselves with ContextMarmot, our open-source graph-based memory engine. It runs as an MCP server and on the SWE-QA benchmark it cut tokens per question by 37% and cost by 22% at identical answer quality. For client work, that turns into architecture reviews, reference patterns, and small prototypes that let teams adopt agents deliberately instead of by trial and error.
- Context architecture: tiered progressive disclosure
- Agent specialization: scoped prompts, restricted tools
- Persistent memory: filesystem-backed knowledge graphs
- Structured execution: research → plan → execute → verify
- Computational guardrails: hooks, linters, structural tests
- Inferential review: LLM-judge eval harnesses and hill-climbing
AI is new on the scene. We aren't.
The background here matters because our consulting work is architectural, not theoretical. Federal ML platforms under audit, enterprise endpoint security at scale, AAA live services under load, and on-device inference in the Alaskan backcountry all shaped how we think about constraints. We bring that experience into assessments, design reviews, and prototypes so your team gets advice grounded in systems that have had to hold up in production.
Federal ML Platform
Distributed anomaly detection, RL-tuned ranking, national-security workloads.
Apple
Endpoint security architecture, Jamf + MDM hardening at enterprise scale.
Imperial College + Stanford
Applied ML research and certification, published anomaly detection methods.
AAA Game Services
Live multiplayer infrastructure, ML-driven behaviors shipping to millions.
ContextMarmot
Open-source graph memory engine for coding agents (Apache 2.0).
Alaska Backcountry
On-device ML, GPS-dependent, cell-dark. Cheechako ships in the App Store.
Three phases. No slide decks. Runnable systems.
Discovery
Stakeholder interviews, current-state review, a workflow or data-flow map, and a candid read on your constraints. You leave with a written brief that clarifies the real problem and the sensible next steps.
Architecture
Decision records, system boundaries, data-handling rules, guardrails, and implementation options sized to your team. This is where we make the hard calls explicit before anyone commits to a full build.
Prototype & Handoff
A small reference prototype for the risky parts, plus handoff notes and review criteria your team or delivery partner can build from. We de-risk the path; we do not need to own the whole product to make it shippable.
Need clarity before you commit to a build?
Let's talk.
Based in Anchorage, working with teams that want practical AI guidance, a solid architecture, and a prototype path without hiring a full product shop.