What we build

Seven pillars, one discipline.

Every pillar comes back to the same thing: how the system is designed, not which model we called.

Flagship

1. Agent reliability & AgentOps hardening

The flagship. Reliability designed into the harness, not patched.

Most agent stacks are built prompt-first and break the first time the real world pushes back. We treat reliability as architecture. The tactics below are the proof.

  1. Idempotency tokens

    a retry that lands twice counts once

  2. State checkpoints before any stateful operation

  3. Connector layer

    circuit breakers, retry budgets, read-vs-write permissions

  4. Cost guards

    daily budget, soft alert 70%, hard kill 95%

  5. Reliability SLIs

    success rate, retry rate, p95 latency, cost-per-output

  6. Progressive tool access

    read-only first, write earned

2. Autonomous AI departments

The harness at department scale — an orchestrator plus a fleet of sub-agents running a function.

One orchestrator delegating to specialist sub-agents, each with its own memory, model config, and scoped tools. What makes it trustworthy is the design: agents loosely coupled through a shared file bus, an idempotent dispatcher, eval-gated model selection, self-hosted observability, human approval gates.

Flagship: a 24/7 content department turning one chat brief into scheduled, published multi-platform output. ~14 sub-agents, one orchestrator.

Orchestrator dispatches over a Shared file bus to ~14 sub-agents (extract, route, draft, review, publish, and more), whose output passes through a human approval gate to Published content; gated by idempotent dispatch and eval-gated routing.
Orchestratorcoordinator
Dispatch / routing
  • idempotent dispatch
  • eval-gated routing
Shared file bus
  • extract
  • route
  • draft
  • review
  • publish
  • …

~14 sub-agents

Human approvalgate
Published contentmulti-platform

3. AI agents & custom MCP servers

The tooling the ecosystem runs on — plugins and custom MCP servers.

A single-orchestrator-plus-N-tools architecture — each tool with its own prompt, integration, and memory; the orchestrator holds the state so failure stays recoverable. When the tool you need does not exist, we build it. goalkeeper is a Claude Code plugin whose subagent judge gates completion against a Definition of Done; reaper-mcp and vst-bench are MCP servers others run.

4. Multi-model routing & cost optimization

Pay for the model the task earns, not your default.

Capability-tiered routing: match each task to the cheapest model that does it well, fail over across providers, share memory. We tier Claude Opus, Sonnet, and Haiku — Haiku for cheap reformatting, Sonnet for reasoning, Opus only where it earns it. We pin the architecture, not the version, behind the Vercel AI SDK — the next model is a config change.

5. Document → structured extraction pipelines

Messy documents in, schema-validated data out — with citations.

Spec, quote, and proposal PDFs and DOCX in; schema-validated fields out, each citing the source page. Layout-aware parsing preserves tables. Built on Unstructured plus a frontier vision model, enforced through the tool-use API so output is schema-valid before the UI.

6. Full-stack AI SaaS

The boring, shippable default stack.

Next.js on Vercel, Supabase (Postgres, Auth + RLS, Edge Functions, pgvector), Stripe (Checkout, webhooks, subscriptions, credit systems). On top: streaming AI chat, tool-calling, structured output, OAuth, background jobs, admin dashboards. The stack that turns an AI feature into a product.

7. RAG & memory systems

Retrieval and memory built for production.

Chunking, embeddings, and retrieval that hold up under real load. A three-layer memory model: episodic in Redis, semantic via RAG over Pinecone / Weaviate / pgvector, procedural as explicit JSON/YAML config — deliberately not embeddings, because some knowledge should be exact. Persistent agent memory through Obsidian and a Memory MCP, tone profiles refined by diffing the human reply against the draft.

Our differentiator

A research-grade ML edge.

Original neural audio codecs and score-based diffusion models, trained from scratch in PyTorch and benchmarked against published baselines. That depth is the difference between reasoning about how a model behaves and calling an API and hoping — it is why we can tell you where a model will fail.

The named stack

What we build on.

Specific tools, chosen on purpose.

Languages

  • TypeScript / JavaScript
  • Python
  • Rust
  • C++
  • SQL

AI / LLM

  • Claude API (primary)
  • OpenAI GPT-5.5
  • Google Gemini
  • OpenRouter
  • Ollama
  • LangChain / LangGraph
  • Vercel AI SDK
  • Claude Code
  • MCP

Web & app

  • Next.js
  • React
  • Express
  • FastAPI / Flask
  • Electron
  • Tailwind
  • Zustand

Data

  • Supabase / Postgres
  • Redis
  • MongoDB
  • Pinecone / Weaviate / pgvector
  • Snowflake / Databricks

Infra & deploy

  • Vercel (primary)
  • AWS
  • GCP
  • DigitalOcean
  • self-hosted VPS
  • Apple Silicon for local agent hosting
  • Docker
  • launchd

Orchestration & automation

  • n8n
  • Make.com
  • Inngest
  • Bull
  • Playwright / Puppeteer
  • Firecrawl

ML

  • PyTorch
  • Transformers
  • RunPod
  • Sentry / PostHog
  • experiment tracking

Not sure which pillar your problem falls under?

Most engagements touch three or four. Tell us what is breaking; we will map it.

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