Software, AI & automation that ships.

What we build, and how.

Most development engagements get stuck in the same loop: a sales team scopes optimistically, a junior engineer builds something different from what you described, and three months later you're paying for change orders to fix the gaps. We work differently.

Every project here is senior-led from kickoff to handoff. The same person who scopes your build is the one writing the code. There's no translation layer, no offshore handoff, no half-trained team-member who needs the requirements re-explained. This is what makes a small operation actually faster than a bigger one — fewer moving parts, fewer dropped balls.

What we typically build

• Full-stack web applications — customer-facing portals, dashboards, marketplaces, internal tools. Built with modern stacks (React, Next.js, TypeScript) and deployed to AWS or Vercel.
• AI agents & agentic systems — production-grade conversational agents and autonomous workflows with RAG, persistent memory, dynamic tool-calling, and event-driven orchestration. See details below.
• APIs and integrations — REST and GraphQL services, third-party connectors (Stripe, Salesforce, HubSpot, QuickBooks, internal ERPs), webhook pipelines, and data sync layers between systems that should already talk to each other.
• Internal tools and admin dashboards — the unsexy software that makes your team faster. Reporting interfaces, approval workflows, customer-management consoles, anything you've been hacking together in spreadsheets.
• Legacy system modernization — strangler-fig migrations off aging codebases, framework upgrades, untangling deployment processes that haven't been touched in years.
• Mobile and cross-platform — React Native or Flutter when a native app is genuinely needed, progressive web apps when it's not.

AI & agentic systems

A growing slice of the work we do is building production-grade AI agents for clients who want to move beyond chatbots into systems that actually do things. Not demos — software that runs in your environment, talks to your data, and takes action.

The current generation of language models makes it possible to build software that reasons, remembers, and adapts in ways that weren't realistic a couple of years ago. But there's a wide gap between a prototype that looks impressive in a demo and an agent that's reliable enough to put in front of customers or internal stakeholders. Closing that gap is craft work — and it's where most off-the-shelf solutions fall over.

• Custom AI agents with RAG-backed reasoning — multi-turn conversational systems built on modern inference APIs (Grok, OpenAI, Anthropic, open-weight models) with retrieval-augmented generation grounding them in your actual documents and data, so responses are accurate rather than confidently wrong.
• Persistent, stateful memory — agents that remember conversation history, user preferences, and session context across interactions and long-running processes. Lightweight, durable storage that scales with your usage without operational overhead.
• Event-driven orchestration — agents that consume real-time data from multiple APIs, trigger workflow cycles, and adapt their decision trees based on incoming payloads and runtime conditions. Less "chatbot," more "intelligent middleware."
• Dynamic tool-calling and conditional branching — agents that self-modify behavior, reassign parameters, and select optimal tool paths based on real-time outcome evaluation rather than running the same rigid script every time.
• Modular, extensible frameworks — plug-and-play tool integration, custom prompt templates, and evaluation harnesses so the agent improves over time instead of degrading silently.

We use modern AI development tooling internally to move fast — but what we ship is yours, runs on your infrastructure, and doesn't depend on any vendor lock-in we can't justify. If a workflow can be solved with a simpler tool, we'll tell you that too.

Process automation

A lot of the highest-leverage work isn't a brand-new app — it's removing the manual work between the systems you already have. The export-from-one-tool, the manual reconciliation, the spreadsheet someone "owns" but nobody understands. These are exactly the problems that pay back fastest when properly automated.

• Cross-system workflows — order and lead flows that touch your CRM, billing, accounting, and shipping, instead of someone forwarding emails between them.
• Document & form processing — extracting structured data from PDFs, invoices, and forms, then auto-routing it to the right system of record.
• Data pipelines & reporting — scheduled syncs between systems and automated reports delivered exactly when stakeholders need them.
• AI-powered steps — when the automation needs judgment (classifying inbound messages, summarizing documents, drafting first-pass replies), we embed the agents above directly into the pipeline.

Where AWS-native services (Lambda, Step Functions, EventBridge) fit the volume or compliance needs, we use them; where a lighter tool like Zapier or Make is genuinely better, we use that. The choice is always driven by what your team can maintain after we leave.

How a typical engagement runs

How we price it

For most well-scoped projects, fixed-price by milestone. You pay when each deliverable lands, not on a clock. For projects where the scope genuinely can't be locked down upfront — research-heavy work, early-stage product exploration, ongoing iteration — we use a capped weekly or monthly retainer with full transparency on hours.

The first conversation is always free. If the project's not a fit, I'll tell you that too, and where possible point you toward someone who is.