Everything I build: scoped & quoted for you.

A retrieval-augmented assistant that answers from your documentation, policies, and past tickets, never the open internet. Streaming UI, source citations, and a scope guard keep it on-topic.

A phone agent that answers 24/7, qualifies the caller, books the job, and texts you a summary. Or makes outbound reminder and follow-up calls. Human-sounding, with a hard consent gate on outbound.

An extraction pipeline that turns messy documents into validated structured records, schema-enforced, with confidence scoring and a human-review lane for the low-confidence cases.

A private assistant wired into your wiki, code, and tickets so your team gets answers in seconds instead of pinging the one person who knows. Access-scoped and auditable.

When the logic is genuinely agentic, a LangGraph-style orchestration with explicit state, tool-use boundaries, retries, and approval checkpoints. It's instrumented so you can see every step it took.

Schema-validated LLM steps with function/tool calling, fallback labels on parse failure, and raw inputs logged beside every decision. The difference between a demo and production.

A battery of adversarial runners that try to break your assistant the way a real user (or attacker) would: prompt injection, jailbreaks, PII leakage, toxicity, over-refusal, with verbatim transcripts of every failure.

Your eval suite wired into CI so it runs on every PR. A score below the ratcheted floor fails the build, with a scorecard your PM can actually read.

A grounding layer that binds answers to a source of truth and an eval that fails when the model states unverifiable specifics. The fix for "it made up a refund policy."

A/B and before/after evaluation across prompt and model versions, so a provider update or a prompt tweak comes with a concrete diff of what improved and what regressed.

Task-success and tool-use-correctness evals for agentic systems: did it call the right function, with the right args, and complete the job. Scored against a rubric, run repeatedly.

Tracing, per-run cost tracking, and drift monitoring on your live AI feature so quality decay and runaway spend surface on a dashboard instead of in a customer complaint.

Contract and integration tests for your APIs: schema validation, auth paths, error cases, and backwards-compatibility checks wired into CI.

End-to-end certification on real devices. The flows a simulator lies about. I've run 256-screen device-cert passes with retry-clean flake handling.

GitHub Actions (or your CI) set up from scratch or rescued: parallelized runs, artifact retention, required checks, and the gates that make merge-green trustworthy.

The flake protocol installed on your existing suite: quarantine lane, retry policy, isolation fixes, and a weekly triage ritual. At HighStrike this cut a live suite from 10% flake to under 1%.

k6 load baselines on the flows that matter, with budgets wired into CI so a performance regression trips the gate before it trips your users.

Screenshot-based regression across breakpoints and themes, so an unintended visual change is flagged in the PR instead of in a customer screenshot.

Automated + manual accessibility review against WCAG 2.2: keyboard navigation, screen-reader semantics, contrast, and reduced-motion behavior, with a prioritized fix list.

An intake pipeline that captures every lead from every source, scores and routes it, and triggers follow-up within minutes. Nothing slips into a spreadsheet nobody checks.

Extract-transform-load pipelines with validation, idempotency, and alerting, so the report your business runs on is built on data that's actually correct and fresh.

Connect the CRM, the billing system, the support desk, and the spreadsheet into one flow with proper error handling and an audit trail. No more copy-paste between tabs.

Scheduled monitoring of a website, feed, or metric with structured extraction and alerting, so you hear about the change that matters the moment it happens.

Cron-driven jobs for the invoicing, reporting, cleanup, and reconciliation work that eats your week: reliable, logged, alert-on-failure.

The admin panel, ops dashboard, or back-office tool your team currently operates in a fragile spreadsheet, built properly, with the right permissions and audit trail.

REST or typed APIs with validation, auth, rate limiting, and tests. The dependable backend your app, your integrations, your automations all build on.

Institutional-grade dashboards and visualizations treated as part of the design system. The metric your team argues about, made legible and live.

Research → script → thumbnail → publish, wired as a pipeline. The system that turns your expertise into a consistent publishing cadence — the AI does the heavy lifting, you approve the output. The same machine I run for my own channels.

A reusable reel template plus a content catalog, so you produce dozens of on-brand vertical videos from one system instead of editing each by hand.

The full inbound machine: a scoping/qualification flow, lead capture wired to your CRM, and a gentle nurture sequence — the same funnel discipline running on this site. Every lead caught, scored, and followed up in minutes.

Per-prospect landing pages plus a 1:1 video-outreach workflow — the highest-reply tactic there is — semi-automated so you send at volume without the copy-paste, built on real prospect research rather than spray-and-pray.

A manifest-driven system that generates on-brand thumbnails and creative at scale from a template plus data — so every video and post looks intentional without a designer per asset.