In practice
What the work produces — shown, not claimed.
No logos, no invented case studies. Below are the kinds of deliverables each engagement produces and representative scenarios of the method — plus one fully verifiable example: this site is built and deployed by the same governed system the work is built on.
AI Production-Readiness Audit & Buildout
Production-Readiness Audit service →AI Production-Readiness Scorecard
SampleA sample scoring of where a typical stalled LLM or agentic POC sits against the six-dimension production bar before any buildout begins.
- Deployment Lives in a notebook or one operator's machine. No reproducible build, no gated pipeline, no rollback path; shipping means manual click-ops.
- Evaluation Quality judged by eyeballing a handful of prompts. No eval set and no regression suite, so a prompt or model change ships blind.
- Observability Application logs only. Prompts, tool calls, and token usage aren't traced, so a failure can't be reconstructed after the fact.
- Guardrails Behavior steered by the system prompt alone. No enforced input/output validation, no policy-as-code, no human approval on consequential actions.
- Cost controls Provider dashboard shows a running total. No per-feature attribution and no budget ceiling that pages, so spend is understood only when the bill arrives.
- Ownership A single champion understands the system. No named on-call owner and no runbook defining what to watch or what should wake someone up.
Situation. A fintech has an agentic assistant that drafts customer dispute responses and reads from internal transaction systems. It demos well to leadership but has stalled before launch: it runs from a developer's environment, has no deployment path, and security will not approve an agent that touches account data without enforced controls.
Path
- 01 Audit the prototype against the readiness bar — deployment, evaluation, observability, guardrails, cost, ownership — and rank the gaps by what actually blocks launch.
- 02 Stand up a reproducible, gated deployment pipeline with scoped IAM and no long-lived keys, so a release is repeatable and rollback-safe.
- 03 Wrap the agent in policy-as-code and input/output validation, with a human approval step required before any action that moves money or alters an account.
- 04 Add an evaluation harness plus tracing for prompts, tool calls, and spend, so regressions and cost spikes surface before customers do.
- 05 Write the ownership model — named on-call, what they watch, what pages them — and hand it to the team that will run it.
Shape of outcome. The assistant moves from a laptop demo to a governed, observable deployment: every consequential action passes an enforced policy gate, regressions surface in evals rather than in production, spend becomes attributable per feature, and a named owner runs it on call.
Representative — illustrates the method, not a specific client.
Agentic Delivery & AI-Augmented Platform Engineering
Agentic Delivery service →Production-safety gate: agent proposal to recorded apply
SampleThe mandatory gate every agent-proposed infrastructure change passes through before any apply, shown as ordered stages.
- Change proposed (agent) An orchestrated agent drafts the change — Terraform, CDK, or a manifest — against a tracked ticket and emits a machine-readable plan and diff. Nothing touches infrastructure yet. A proposal with no readable plan does not advance.
- Automated verification CI runs type checks, lint, tests, and a plan or synth, plus drift detection. Any failing check, or a plan that does not match the stated intent, halts the change here.
- Policy-as-code gate The plan is evaluated against policy-as-code: no public exposure, no long-lived keys, required tagging, blast-radius limits. Any violation is a hard stop with no in-pipeline override.
- Human approves apply A reviewer sees the verified plan, the policy result, and the diff in one view and approves. Apply cannot run without a recorded approval bound to that exact plan; a re-planned change invalidates it.
- Apply executes (scoped) Apply runs under a short-lived, least-privilege credential, restricted to the approved plan. Anything outside that plan, or any credential drift, fails closed.
- Audit trail Proposal, verification output, policy decision, approver, plan hash, and apply log are written to an immutable record. An apply that cannot be fully attributed is treated as an incident, not a success.
Situation. A fintech platform team wants agent-driven throughput on its Terraform-managed AWS estate, but its security posture forbids any unattended apply. The throughput gain goes unrealized because no one will let agents near production infrastructure.
Path
- 01 Map the change types agents may propose and the policy each must satisfy: network exposure, key lifetime, tagging, blast radius.
- 02 Wire machine-verified guardrails into CI: type checks, tests, terraform plan, drift detection, and policy-as-code evaluated against the plan.
- 03 Stand up the production-safety gate so no apply runs without a passing plan, a clean policy result, and a recorded human approval bound to that plan.
- 04 Run agent-proposed changes through the governed workflow on low-risk infrastructure first, widening scope as evidence accumulates.
- 05 Hand over the runbook and enablement so the team operates the gate after the engagement.
Shape of outcome. Agent-proposed changes move at speed, but every apply is gated, attributable, and reversible — the throughput becomes usable because security and platform leads can approve it without trusting an unattended agent.
Representative — illustrates the method, not a specific client.
Data-Platform & Pipeline Architecture for AI Workloads
Data Platform Architecture service →Data-Platform Readiness for AI Workloads
SampleA short bar for whether a data platform is ready to carry AI workloads, not just dashboards and analytics.
- Lineage Every record traces to its source, the transform that touched it, and the run that produced it; column-level where it feeds a model. Without it, you cannot explain or reproduce a retrieval result, and 'where did this come from' has no answer.
- Idempotency Re-running an ingest or a backfill converges to the same state instead of duplicating or drifting. Silent duplication turns into skewed embeddings and double-counted signals that are hard to spot downstream.
- Reproducible builds Pinned dependencies, versioned transforms, and a content-addressed dataset so a given index can be rebuilt to the same bytes. Required to debug a regression, defend a decision, or roll an index back cleanly.
- Retrieval evaluation A labeled eval set and an offline harness that scores retrieval quality on every change, separate from the generation model. Without it, a chunking or embedding tweak ships blind and quietly degrades answers no one is measuring.
- Governance Access controls, PII handling, and retention wired into the pipeline and matched to your regulatory posture, not bolted on at the edge. For regulated data, ungoverned ingestion is a breach waiting to surface in retrieval logs.
- Cost/scale model A model of storage, embedding, and query cost as data and traffic grow, with the dominant drivers named up front. Keeps the platform from becoming unaffordable as usage climbs.
Situation. A carrier wants a retrieval-augmented assistant over policy wordings and claims notes, but the data layer underneath it is brittle. Pipelines fail silently, nothing is traceable to source, and the team does not trust what retrieval returns. Every proposed model improvement waits on the plumbing.
Path
- 01 Map the existing pipelines and name the gaps: where lineage breaks, where a re-run duplicates, what cannot be rebuilt.
- 02 Make ingestion idempotent and builds reproducible, so a given index ties back to a known source state and a version.
- 03 Stand up a retrieval eval harness against a labeled set that scores quality on every change before it ships.
- 04 Wire access controls, PII handling, and retention into the pipeline to match the carrier's regulatory posture.
- 05 Build a cost-and-scale model that names the dominant drivers as data volume and query traffic grow.
Shape of outcome. Retrieval results become reproducible and traceable to source, model work stops being gated on the plumbing, and the cost of scaling is known before it lands rather than discovered after.
Representative — illustrates the method, not a specific client.
Cloud + AI Cost Optimization (FinOps for AI)
AI Cost Optimization service →AI & Cloud Cost Teardown — Spend Drivers
SampleThe method a teardown follows to trace AI and cloud spend to its drivers before a single change is proposed.
- Model selection Whether each call routes to the smallest model that clears the eval bar, and where a premium model is doing work a cheaper one passes.
- Token economics Prompt and context bloat, repeated system preambles, retry storms, and whether caching and truncation are applied where they hold quality.
- GPU utilization Accelerator occupancy against what is reserved — batching, concurrency, and whether provisioned capacity tracks real throughput.
- Data egress Cross-AZ, cross-region, and vendor-bound traffic on the inference and retrieval paths, and which transfers colocation makes avoidable.
- Idle capacity Always-on endpoints, over-provisioned node groups, and non-prod environments that bill around the clock for daytime use.
- Cost attribution Whether tagging and telemetry can tie spend to a feature, team, and request, or whether the bill arrives as one undifferentiated line.
Situation. A fintech runs LLM-backed support triage and fraud-summary features. Inference and GPU spend climb faster than request volume, and finance cannot tie the bill to either product line.
Path
- 01 Instrument first: tag inference, GPU, and egress to feature, team, and request before touching any configuration.
- 02 Trace each driver — model routing, context and token size, accelerator occupancy, idle endpoints, egress paths — against the existing eval bar.
- 03 Quantify the saving behind each candidate change and rank by effort, risk, and quality exposure.
- 04 Ship approved changes through the production-safety gate, with quality evals gating every model or routing swap.
- 05 Wire budgets, alarms, and per-feature dashboards so the corrected spend curve holds after handoff.
Shape of outcome. Spend becomes attributable per feature and per request, the cost curve bends below the usage curve, and the controls to hold it stay in place after handoff.
Representative — illustrates the method, not a specific client.
Fractional Staff/Principal Platform Leadership
Fractional Leadership service →Fractional operating model — what a retained 1–3 day/week engagement covers
SampleThe standing scope of a retained Staff/Principal engagement — what a fractional week actually buys, not a one-off project.
- Technical direction Standing weekly input on the AI/platform roadmap: sequencing, build-vs-buy, and which bets to kill before they accrue cost. Named accountability, not a rotating bench.
- Architecture review Scheduled review of designs and significant changes before they're committed — retrieval, deployment topology, failure modes — with written rationale the team keeps.
- Production-safety & cost governance Every path to production held to a senior bar: a mandatory safety gate before apply, plus spend kept attributable per feature and reviewed each cycle.
- Hiring calibration Calibration on platform and AI hires — rubric, interview signal, leveling — so the bench you build clears the same bar, not just the open seat.
- Hands-on delivery on highest-leverage work Direct delivery on the problems where senior hands move the roadmap — the blocking, high-risk work — not staffing the backlog.
- Mentorship & decision transfer Working sessions with the team so judgment and standards transfer, and the direction outlasts the engagement rather than walking out the door with it.
- Accountability & access Named ownership with a defined escalation path; reachable between sessions for the go/no-go calls that gate a production change.
Situation. A funded fintech scale-up is putting AI into its underwriting and fraud path, but the platform team is mid-level and shipping to a production account with no safety gate and rising inference spend. The roadmap needs Staff/Principal judgment; the headcount and runway don't yet justify a full-time hire.
Path
- 01 Read the roadmap and the risk: map the AI initiatives against a production-readiness bar, then rank by leverage and by what could quietly go wrong in production.
- 02 Stand up the non-negotiables first — a safety gate before any apply and per-feature spend attribution — held to a senior standard the team can run unaided.
- 03 Take architecture review on the highest-risk design (retrieval and deployment topology) and leave written rationale, not just a verdict.
- 04 Go hands-on the one blocking problem the team can't clear alone, so the roadmap moves instead of waiting on a hire.
- 05 Calibrate the next platform hire — rubric and interview signal — so the seat is filled to the same bar the engagement set.
- 06 Hand back a documented operating model so direction and standards persist after the days/week step down.
Shape of outcome. The roadmap moves under named senior accountability: production changes pass a safety gate before apply, spend becomes attributable per feature, and the team inherits the standard and the hiring bar — without carrying a full-time Staff hire before the runway supports one.
Representative — illustrates the method, not a specific client.
Verifiable, not representative
This site is the case study.
Bootstrapped without AdministratorAccess,
gated by policy-as-code, deployed keyless via OIDC — and the guardrails
caught two real issues before they shipped. Every claim is checkable
against the site you're on.