You know what you want built. You need a team that can actually ship it.
You’ve already done the strategy work. You’ve identified the opportunity. What you need now is a senior team that can sit down, scope the system, and ship it to production — with the rigor of real software engineering, not the cargo-cult vibes of a prompt-engineering bootcamp.
That’s what Custom AI Builds is for. Project-based, scoped, milestone-driven engagements where we own the entire build: architecture, model selection, integration, observability, governance, and handoff. The output is running code in your production environment, with the documentation and test coverage to back it.
How it works
Step 1: Scope and architecture
We start with a paid architecture sprint (typically 1–2 weeks). We define the system boundaries, the data contracts, the model selection, the integration points, and the success criteria. We document the architecture in a written spec your engineering team can read. No verbal scope. No “we’ll figure it out as we go.”
Step 2: Build in production-ready milestones
We build in 2-week iterations. Every milestone ends with deployable code in your environment, observability dashboards, and a live demo. You see progress every two weeks. No 12-week black boxes. No surprise overruns.
Step 3: Handoff and operate
At the end of the engagement you own the code, the docs, the runbooks, and the dashboards. We offer optional ongoing operations support, but the system is built so your team can own it the day we leave.
What we build
- Custom Agents — single-purpose autonomous agents with tool use, planning, and reflection loops
- Multi-Agent Systems — orchestrated agent fleets with role-based specialization, budget controls, and inter-agent messaging
- RAG Pipelines — production retrieval-augmented generation over your private data, with hybrid search, re-ranking, and citation tracking
- MCP Servers — Model Context Protocol servers exposing your internal systems to Claude, Cursor, and other MCP-compatible clients
- Workflow Automations — Workflows (markdown SOPs) + Agents (LLMs) + Tools (deterministic code) — the WAT pattern
- Internal AI Platforms — multi-tenant AI capabilities embedded into your existing product
- Evaluation & Eval Harnesses — golden datasets, regression suites, A/B framework for model and prompt changes
Technical stack we work in
- Models: Claude (Opus, Sonnet, Haiku), GPT-4o/5, Qwen, Llama — selected per use case, not by vendor loyalty
- Orchestration: LangGraph, custom multi-agent frameworks, our own Ottolax platform
- Data: Postgres (incl. pgvector), Supabase, ClickHouse, Pinecone, custom retrievers
- Backends: FastAPI, Node, Cloudflare Workers, AWS Lambda
- Frontends: Next.js, React, Astro
- Observability: OpenTelemetry, LangSmith, custom metering, cost dashboards
Engagement model
Fixed-fee per milestone. Minimum engagement typically 6 weeks. Most builds run 8–16 weeks. You can pause or end the engagement between milestones — no long-term lock-in.
Who it’s for
- Engineering leaders who need a senior AI partner that ships, not coaches
- Product teams adding AI features and don’t want to lose 6 months learning the stack
- Operators with a specific automation in mind and want it built right the first time
- Companies that already ran a POC and need to take it to production