Eight client work examples — seven anonymized, one public.

01 · Case study

Production emergency: containing a $40K/day agent cost explosion

Projected $40K+/day burn contained within 72 hours. No recurrence reported during the engagement.

The $40K/day figure is a projected run-rate, not a claim that a full day of that spend was invoiced. This write-up describes one production incident and the controls shipped during the response.

02 · Case study

Platform Design & Build: multi-tenant agent substrate

250-450 concurrent agent instances in production. Release overhead down roughly 40%.

This is a summary of platform engagements across multiple clients, not a benchmark of one installation. The concurrency range and release-overhead figures below describe the published engagement results.

Client.

Three client platforms running multi-agent systems in production.

Engagement.

Reliability advisory · two quarters.

The problem. Teams were operating production agents without agreed reliability targets — every incident was argued over opinion instead of data, and improvements could not be measured.

What we shipped

• Defined SLO frameworks on task completion rate, mean tool calls, hallucination escape rate, and end-to-end latency.
• Tied error budgets to each objective, with agent trace instrumentation feeding the dashboards.

Result

• SLO compliance improved from 71% to 96% over two quarters.

OpenTelemetry · Prometheus · Grafana · agent trace instrumentation

Engage on this: Reliability Foundation — SLOs and evals in CI · Agent Production Audit

Client.

Platform running heavy tool-use agents in production.

Engagement.

Audit and build · production hardening.

The problem. A retrieval and governance layer that had grown ad hoc: no deterministic validation on tool calls, no visibility into what agents were actually invoking, and no evidence of enforcement for security review.

What we shipped

• Audited the retrieval stack on Pinecone and OpenSearch and rebuilt the ingestion and query paths.
• Enforced deterministic policy validation on every tool call, with zero known bypasses across deployments.

Result

• 1.8M+ tool invocations running under policy validation.

Pinecone · OpenSearch · policy-as-code tool-call validation

Engage on this: Agent Production Audit · Agent Governance & Insurability Package · AI Compliance Package — EU Article 50 plus US module

05 · Case study

LLM cost reduction program

$200K/yr aggregate reduction in cloud and LLM spend.

The $200K/year figure is aggregate cloud and LLM spend reduction across advisory engagements. It is not the result of a single sprint, a promised saving, or a measured percentage applicable to every workload.

Client.

Two client platforms with classical ML workloads.

Engagement.

Data platform standardization.

The problem. Data lakes and model registries that had diverged across platforms, making model lineage and dataset governance inconsistent.

What we shipped

• Standardized data lakes and model registries across both platforms, integrating Databricks and Domino Data Lab with existing workloads in scikit-learn, PyTorch, and TensorFlow.

Result

• One data and model lifecycle across both platforms.

Databricks · Domino Data Lab · scikit-learn · PyTorch · TensorFlow

Engage on this: Substrate Build · Fractional AI Architect

Client.

Proprietary high-frequency trading firm · remote.

Engagement.

Systems engineering · Jan. 2026 - Jul. 2026. Independent advisory work was paused during this role.

The problem. A production trading system for prediction markets pulling live data from centralized exchanges and on-chain sources, with hard risk controls and sub-10ms peak latency requirements, trading real capital.

What we shipped

• Designed and ran event-driven Python and Rust pipelines holding sub-10ms latency at peak load.
• Designed the live risk-control layer: position limits, daily loss limits, and slippage checks enforced on every order.
• Built statistical models of market order flow to detect volatility clustering, and used them to size trades and set execution pace under fast-moving conditions.
• Implemented Kelly-criterion position sizing that scales exposure to model confidence.
• Maintained the research pipeline: historical backfill, detector-faithful backtesting, walk-forward validation, and automated signal outcome labeling.

Result

• Execution slippage cut by 14%.
• Zero limit breaches while trading real capital.

Python · Rust · centralized exchange APIs · on-chain data feeds

Engage on this: Reliability Foundation — SLOs and evals in CI · Agent Production Audit

Client.

Iguazio AI (employer engagement · public on this site) · remote contractor.

Engagement.

Jun. 2021 - Mar. 2023.

The problem. Enterprise NLP and computer-vision inference workloads on a monolithic FastAPI service: 18-minute cold starts, a 900ms+ p99 on core analytics queries, and no canary rollout path.

What we shipped

• Deployed and operated a multi-GPU Triton Inference Server cluster with dynamic batching and model quantization, with inference SLOs and Prometheus/Grafana alerting runbooks for OOM failures.
• Decomposed the monolith into event-driven microservices on Docker, PostgreSQL, and Kafka.
• Owned schema and indexing strategy for behavioral analytics datasets across three product teams; redesigned indexes and materialized views.
• Engineered GitOps CI/CD with GitHub Actions and ArgoCD, automated health checks, and canary rollout policies.

Result

• Cold-start deployment time from 18 minutes to under 4.
• p99 query latency from over 900ms to 120ms.
• 99.9% uptime sustained across production containerized environments.

Triton Inference Server · FastAPI · Docker · PostgreSQL · Kafka · Prometheus · Grafana · GitHub Actions · ArgoCD

Engage on this: Substrate Build · Reliability Foundation — SLOs and evals in CI