Agentic systems
Agents that gather organizational context, make plans, use tools, cross system boundaries, request approval, and return finished work—not just chat.
01 / What we build
Not AI theater.
Working intelligent systems.
A model is the beginning. The product is everything required to trust it, operate it, improve it, and put it to work.
We work where product, applied AI, and infrastructure meet: context and retrieval, orchestration, tools, permissions, evaluation, observability, deployment, human approval, and the interfaces people actually use.
- Prototype through production
- Architecture through interface
- Measured, observable, operable
Evaluation
Test sets, metrics, traces, failure analysis, and feedback loops that make model quality a product discipline instead of a demo-day opinion.
Autonomous ML
Systems that move from raw business data through modeling, training, evaluation, packaging, deployment, prediction, and monitoring.
AI infrastructure
GPU workloads, inference, orchestration, observability, cost control, distributed compute, and the blockchain rails beneath decentralized systems.
02 / Selected work
We’ve built the difficult parts.
More than once.
Windstream / Agentic engineering
Work that starts where teams already work.
Designed an autonomous engineering funnel around organizational and code context: requests arrive from native work surfaces, the system plans and decomposes work across repositories, invokes specialized agents, preserves approvals and auditability, and returns results to the tools the team already uses.
- Multi-repository context and decomposition
- GitHub, Slack, ticketing, and MCP execution surfaces
- Control-plane visibility without forcing a new front door
From proprietary data to a production model.
Built an autonomous ML engineering system spanning data understanding, modeling strategy, training, evaluation, deployment, monitoring, and runtime reliability—used on real industrial prediction problems.
- #1 result on MLE-bench Lite
- Evaluation loops, GPU execution, OTEL, and cost controls
- Prediction systems for aerospace, energy, and R&D teams
Make quality visible before users find the failures.
Worked in the emerging discipline of evaluating RAG and LLM systems: turning traces, test data, metrics, and observed failure modes into evidence teams can use to improve what they ship.
- Evaluation architecture and product thinking
- From subjective outputs to repeatable signals
- Feedback loops for continuous improvement
Infrastructure beyond one cloud—or one machine.
Built and led work across blockchain, distributed scheduling, peer-to-peer systems, browser and edge inference, Kubernetes, WASM, and globally distributed compute.
- Experience spanning Akash and decentralized cloud
- AI inference systems scaled across 3M+ edge nodes
- Rust, Go, libp2p, WASM/WASI, and production orchestration
Turn deep domain logic into software people can operate.
Shaped complex technical and operational requirements into durable product architecture—connecting data, workflows, infrastructure, and clear interfaces without flattening what makes the domain valuable.
- Technical strategy carried into implementation
- Systems designed around real operating constraints
- Senior product and engineering judgment, end to end
15+ yearsBuilding ML and production systems
3M+ nodesDistributed edge infrastructure scaled
0 → productionFounder-level product and engineering
The house specialty
We like
ugly problems.
The agent that works in a demo but fails in production. The model nobody can evaluate. The workflow split across six systems. The infrastructure that has to coordinate thousands—or millions—of machines. That’s where we do our best work.
03 / The method
From impressive demo
to trusted system.
Define the claim
What should the system do, for whom, under which conditions—and what would make it meaningfully better than the current way?
Build the proof
We turn the claim into scenarios, datasets, traces, metrics, and operational acceptance criteria before scale hides the problems.
Ship the system
Models, agents, tools, product surfaces, infrastructure, permissions, and observability move together into real use.
Close the loop
Production outcomes feed evaluation and iteration, so the system gets more reliable rather than merely more complicated.
04 / Work with us
Senior consulting now.
RadOps is coming.
Put senior builders on the hard part.
We work with founders and technical teams on intelligent products where architecture, evaluation, infrastructure, and product judgment must happen together.
- Intelligent-system architecture and product strategy
- Hands-on prototypes and production implementation
- Evaluation, reliability, and infrastructure reviews
Operations, with intelligence built in.
RadOps is our productized operating layer for teams whose workflows have outgrown spreadsheets and disconnected software—bringing data, decisions, automation, and human judgment together.
- Workflows shaped to the actual operation
- AI for context, exceptions, and recommendations
- Approvals, auditability, integrations, and control
Appointments now open
Bring us the system
that has to actually work.
Agentic product, evaluation stack, autonomous ML, distributed infrastructure, or a hard operating problem—we’ll help turn the ambitious version into the production version.
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