Start with AI integrations & management — the help most businesses actually need. Below that, the full engineering bench across web, AI, business systems, and growth, each linked to live proof.
Most companies don't have an AI problem. They have an ownership problem — everyone agrees AI matters, nobody knows which tools to trust, what it should cost, where the data goes, or who answers for it when it gets something wrong.
That's the gap we fill. We act as the AI function you don't have on staff: choosing the tools, integrating them into work your team already does, writing the rules for safe use, training people to use them well, and staying on the hook for cost and quality afterward. You don't need to become an AI expert. You need one accountable partner who already is.
Every one of these is a management gap, not a technology gap.
We inventory the work you actually do, find where AI earns its keep, and say plainly where it doesn't. You get a ranked list of opportunities with effort, cost, and expected payback — not a hype deck.
A sequenced plan: what to pilot first, what to defer, which model or vendor fits each job, and what success looks like in numbers. Written so a non-technical owner can hold us to it.
The build. AI wired into the systems your team already uses — CRM, inbox, phones, docs, dashboards — so it shows up inside the workflow instead of in a separate tab nobody opens.
A written AI use policy, decisions on what data may leave your walls, human-approval gates on anything customer-facing or financial, and an audit trail that shows who approved what.
Role-specific training on the tools you've actually deployed, with prompt patterns and guardrails for each job — so adoption comes from competence rather than mandate.
Consolidated spend across providers, per-feature cost and usage monitoring, quality evals when models change under you, and a monthly review of what's working. Managed, not abandoned.
Two weeks. Interviews, tool audit, and a ranked opportunity list.
Sequenced plan with costs, owners, and success metrics.
One high-value workflow shipped end-to-end and measured.
Roll into the systems the team already lives in.
Governance, training, cost review, and quality evals on a cadence.
Below is the engineering bench behind that work — the specific skills, grouped by discipline. Each card links to somewhere on this site where you can see it running.
The foundation everything else sits on. Production Next.js applications — not templates — with typed end-to-end data flow, real auth, payments that reconcile, and tests in CI. Built so a second developer can pick it up without a rewrite.
Production apps with SSR, ISR, server actions, and the full App Router — from routing to deployment.
Strictly typed component architecture, custom hooks, context, and performance-optimized rendering patterns.
Scalable design tokens, consistent component libraries, and pixel-perfect responsive UIs from scratch.
Schema design, complex queries, migrations, and relational modeling for production databases.
Well-documented, versioned APIs with proper auth, rate limiting, validation, and OpenAPI specs.
End-to-end type-safe API layers with zero schema duplication between client and server.
Full auth flows — OAuth, magic links, sessions, JWTs, role-based access, and multi-tenant support.
Subscriptions, one-time payments, webhooks, metered billing, and customer portal integrations.
Globally distributed compute with Vercel Edge, Cloudflare Workers, and cold-start optimized lambdas.
Cache strategies, job queues (BullMQ), pub/sub, and rate limiting to handle high-throughput workloads.
Live dashboards, chat, collaborative features, and streaming UIs using WebSockets and SSE.
Containerized apps, multi-stage builds, GitHub Actions workflows, and automated test/deploy pipelines.
Object storage, CDN configuration, edge caching, and infrastructure-as-code for scalable deployments.
Unit, integration, and E2E test suites with CI enforcement, coverage thresholds, and typed mocks.
The engineering underneath the AI features: retrieval that cites its sources, agents with tool access and human checkpoints, streaming interfaces, and evals that catch quality drift when a provider changes a model on you.
Chat completions, function calling, structured outputs, assistants, and the Realtime API for voice and streaming.
Claude Opus, Sonnet, and Haiku integrations — extended context, tool use, agents, and vision capabilities.
Gemini 2.x Flash and Pro — multimodal inputs, long context, and native Google ecosystem integrations.
Multi-step autonomous agents with tool calling, memory, planning loops, and human-in-the-loop checkpoints.
Full RAG pipelines — chunking, embedding, retrieval, reranking, and grounded response generation.
High-performance vector search with cosine similarity, hybrid search, and metadata filtering at scale.
Systematic prompt design, chain-of-thought, few-shot patterns, and automated eval frameworks.
Image analysis, document parsing, audio transcription, and mixed-modality pipelines across providers.
Low-latency voice applications using OpenAI Realtime API, WebSocket streaming, and speech synthesis.
Token-by-token streaming UIs, server-sent events, and backpressure-aware streaming architectures.
Index management, namespace isolation, upsert pipelines, and ANN search for production RAG systems.
Composable LLM pipelines, document loaders, retrievers, and chain abstractions for complex workflows.
Building and consuming MCP servers to give AI agents structured access to tools, data, and APIs.
No-code/low-code automation workflows with LLM nodes for classification, extraction, and generation.
Head-to-head model scoring on your real tasks — cost, latency, and quality per model, with an LLM-as-judge rubric so the pick is evidence, not vibes.
Purpose-built models for your domain: fine-tuned open-weight bases, distilled smaller models to cut cost, and private deployments where data can't leave your walls.
Domain adaptation of open-source models using LoRA and PEFT techniques on custom datasets.
Programmatic image creation, variation, and editing pipelines for product, marketing, and content use cases.
Text- and image-to-video pipelines across OpenRouter's video models (Seedance, Kling, Wan, Hailuo, Veo), with AI auto-selecting the best model per scene.
Dictation-to-text with LLM cleanup and rewriting — wired into any text input for faster, polished copy.
The unglamorous software a business actually runs on — CRM, invoicing, bookkeeping, scheduling, inventory. Replaces the spreadsheet-and-sticky-note layer with something auditable that your team will keep using.
End-to-end customer management — contacts, companies, deal stages, activity history, and notes.
Send branded invoices with Stripe Payment Links, track payment status, and reconcile automatically via webhooks.
Schedule monthly or custom-interval invoices that generate and send themselves on autopilot.
A QuickBooks-style ledger — chart of accounts, expense categories, and live profit & loss reporting.
Upload a receipt, let AI extract the vendor, date, and amount, and file it against the right account.
Log trips with automatic distance lookup and IRS-rate mileage deductions for tax time.
Build estimates, send them for client acceptance, and convert accepted quotes straight into invoices.
Automated invoice, receipt, statement, reminder, and welcome emails with branded HTML and PDF attachments.
Generate on-brand ad images and video for campaigns, with prompt tooling and a saved asset library.
Drag-and-drop designer for product labels and print assets with precise sizing and export.
Live dashboards for service status, uptime, and integration health with at-a-glance diagnostics.
Measurement first. Clean attribution and analytics so you can tell which channel actually produced revenue, then the campaign, SEO, email, and experimentation work that compounds on top of trustworthy numbers.
Full analytics setup — event schemas, custom dimensions, conversion tracking, and data layer architecture.
Multi-touch attribution, assisted conversion analysis, and channel ROI reporting across the funnel.
Core Web Vitals, structured data, crawl optimization, content gap analysis, and programmatic SEO.
Editorial calendars, SEO-led content briefs, long-form articles, and AI-assisted content workflows.
Lifecycle flows, segmented campaigns, A/B tests, and behavioral trigger sequences for retention.
Google Ads and Meta campaign setup, audience building, bidding strategies, and performance reporting.
Hypothesis-driven tests, statistical significance analysis, and iteration loops to lift conversion rates.
Consistent UTM taxonomy, tagging audits, and real-time dashboards to keep attribution clean.
Self-serve BI dashboards with live data connections, drill-downs, and automated report delivery.
RFM models, cohort analysis, and lifecycle stage mapping to improve LTV and reduce churn.
Source/destination setup, identity resolution, and event tracking pipelines through Segment.
