Your dedicated AI engineering team.

We own AI feature tracks in your product end-to-end — architecture, implementation, testing, monitoring. In your standup every day. Not a vendor. Not a body shop. A team that acts like yours.

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The problem

The AI features that keep getting pushed

Your roadmap has AI features that matter.

Features that will define whether you stay competitive in the next 12 months. Features your board is asking about. Features your competitors are shipping.

They keep getting pushed because your engineering team is at capacity. Every sprint, something more urgent takes priority. And the senior AI engineers you need to hire are either unavailable, or want $250k+ in the US — which you can't justify at Series A.

So the features sit. The gap grows.

We close it.

What we build

What we ship

AI assistants and copilots

Embedded in your product. Context-aware, integrated with your data, built with proper evals and fallback handling so they don't embarrass you in front of customers.

LLM integrations

Claude, GPT-4, Gemini — we integrate the right model for your use case with proper prompt engineering, cost controls, observability, and latency tuning. Not a wrapper. A production integration.

Agentic features

AI that takes actions on behalf of your users — not just generates text. Multi-step workflows, tool use, decision logic, human-in-the-loop where needed.

Recommendation and personalisation systems

ML models that learn from user behaviour and improve the product experience. Built on your data, running in your infrastructure.

Intelligent document processing

AI that reads, extracts, classifies, and acts on documents. Contracts, reports, forms, medical records — any domain where humans are reading documents and making decisions.

How we work

What working with us actually looks like

We integrate into your team from week one. This is not how most agencies work. It's how we've always worked.

We join your standup. Every day. We know what's happening across the product, not just in our track.
We own the decisions. Architecture, library choices, infrastructure — we bring recommendations and take accountability for the outcome.
We write the code you'd write. High quality, well-tested, documented, maintainable by your team after we're done.
We handle incidents. If something breaks at 2am, we're on it. You don't chase us.
We tell you what we think. If we see a technical decision that will cause problems in six months, we say so. Even if it's not in our track.

Engagements we've run

Proof

Venture-backed ClimaTech · California

Full product

Full AI product engineering — CPO coverage + fleet automation

The situation: Early-stage startup needed to build a full product fast — mobile app, backend, integrations with multiple charge point operators — with aggressive deadlines to secure customer contracts.
What we did: Became their dedicated engineering team. Built the full product stack. Delivered full California CPO coverage. Helped them secure contracts with national fleet operators.
The outcome: The engagement has grown continuously since the first sprint. Still running.

“Working with nh66 feels like having an internal engineering team — high ownership, focused on business growth, not just ticking off deliverables. Their impact on our business is evident in the speed at which we brought the product to market.”CEO, ClimaTech startup, California

Funded Medical EdTech · California

Full platform rebuild

Production ML recommendation engine on AWS SageMaker

The situation: Existing platform had significant technical debt and performance issues. Needed a full rebuild while retaining the video library — with a board presentation deadline.
What we did: Reimplemented the entire platform from scratch. Added a comprehensive search and an ML-based recommendation engine on AWS SageMaker.
The outcome: New platform delivered in 14 weeks. 4× performance improvement. Board thoroughly impressed. New customers and a CME partnership followed. A mobile engagement was signed immediately after.

FAQ

Common questions

We've been burned by outsourced teams before. How is this different?

The most common thing we hear. Our answer: talk to our clients directly. We'll introduce you to the CEO of the ClimaTech company and the CTO of the EdTech company — both available for a call. Ask them what it was actually like. If it doesn't match what we're telling you, don't hire us.

Why not just hire an AI engineer internally?

A senior AI engineer in the US costs $220k–$300k plus equity and takes 3–6 months to recruit. With us, you get a team with production experience available in two weeks at a fraction of the cost. If the engagement isn't working, there's no permanent hire to unwind. Most clients find the risk-adjusted cost is significantly lower.

How do we start without committing to a long engagement?

We structure the first engagement as a fixed 6–8 week project with a clearly defined scope. If it works — and it usually does — it transitions to an ongoing retainer. You're not committing to 12 months upfront.

What's your tech stack?

Python, Golang, React, Node.js, AWS (EC2, ECS, RDS, SageMaker, S3), Terraform, PostgreSQL, Redis. LLM integrations across Claude API, OpenAI, and open-source models. LangGraph and n8n for orchestration. We pick the right tool for your stack, not ours.

What size companies do you work with?

Series A and B SaaS companies in the US and Canada. Typically 20–150 employees with an engineering team of 3–20. You have a product with paying customers and AI features on the roadmap.

Have AI on the roadmap that isn't moving?

Talk to our engineering team.

30 minutes to understand your stack, your roadmap, and whether we're the right fit. We'll tell you honestly if we're not.

Book a technical discovery call