AI Integration

Everyone is being asked about AI. The hard part is not connecting to an AI model; that takes an afternoon. The hard part is knowing where AI genuinely improves your product, making it reliable enough for real customers, and keeping its cost and risk under control as you grow.

We build AI into your product and workflows where it earns its place: to make the product more useful, cut operating costs, or unlock something that was not possible before. We prove it on your real data first, then build it properly.

• Proof of concept in 1 week

• Production feature in 1 week

• Model-neutral: OpenAI, Anthropic, Google or open-source, chosen on quality, cost and privacy

What we build

1. Conversational AI and agents

Assistants that help your customers and team get things done, inside your product.

• AI assistants and copilots that draft, summarise and suggest next steps

• Customer support that answers from your own help content, with sources

• AI agents that complete multi-step tasks, with a person approving anything important

• Integrations with OpenAI, Anthropic and Google models, including voice where it helps

2. Data-driven intelligence

AI that turns the data and documents you already have into answers and insight.

• Search and Q&A over your own documents and data, so answers come from your knowledge, not guesses (known as RAG)

• Safe connections that let AI read from and act in your tools and databases, under strict permissions (using MCP, the Model Context Protocol)

• Prediction and alerts, such as churn risk, anomalies and lead scoring

• Feedback analysis: themes in support tickets, reviews and surveys, surfaced in hours

3. Automation that scales

AI that takes repetitive work off your team's plate.

• Document processing: reading invoices, forms and contracts and pulling out what matters

• Ticket and request triage: sorting, tagging and routing automatically

• Automated reports and summaries, delivered on schedule

• Workflows that run around the clock, with a person stepping in only when needed

How we work

Step 1 — Discover & Strategy

Where would AI genuinely change your product or your costs?

We look at your customers' workflows, your team's manual work, and your data, and find the places where AI creates real value. Every idea is scored on value, feasibility, data quality, risk and running cost. If a simple rule or automation would do the job better, we tell you.

You get: an AI Opportunity Map, a ranked list of use cases, and one recommended first use case with a clear measure of success.

Step 2 — Prototype & Validate

Does it actually work on your real data?

We build a small proof of concept and test it against a set of real examples, such as actual tickets, documents or customer questions, rather than hand-picked demos. We compare leading models on quality, speed and cost per use.

You get: a working proof of concept, a measure of how often it gets things right, its cost per use, and a clear go / no-go. If it does not work, you find out in weeks, not months.

Step 3 — Integrate & Scale

Can it run reliably for real customers?

We build the feature into your product to the same standard as any production software, under senior engineering review:

• Designed for when AI is wrong: clear sources, easy correction, and human approval for consequential actions

• Tested before release: automated quality checks against the real-example test set, plus a safety review

• Cost controls: caching, the cheapest model that meets the quality bar, limits per user and spend alerts

• Backups: if a model provider is slow or down, your product keeps working

• Released gradually: a small group first, with a switch to turn it off instantly

• Measured once live: a dashboard of usage, acceptance, quality, cost and business impact

You get: a live, tested AI feature with monitoring, guardrails and a plan for what to improve next.

Built to be reliable, affordable and safe

Most AI features that disappoint fail in one of three ways. We control all three from day one.

Your data stays yours, and is never used to train anything outside your product.

Timelines and pricing

Fixed scope and a fixed fee, agreed before work starts. The Opportunity Sprint fee is credited against the Feature Build if you go ahead within 30 days. AI provider usage fees are paid directly by you, so you always see exactly what the AI costs to run.

Is this for you?

A good fit if you:

• Have a live product and want AI features customers will actually use

• Are under pressure from investors or competitors to show a credible AI strategy

• Have repetitive manual work, such as support, data entry or reporting, that AI could take on

• Hold valuable data or content that customers or staff struggle to use

• Tried AI before and ended up with a demo that never worked reliably

Not the right fit if you:

• Do not have a product or validated customer yet. Start with Product Ideation & Discovery or an AI-Driven MVP

• Want AI for the announcement, without a problem it solves

• Need research-grade model training at very large scale

Frequently asked questions

What is the difference between AI integration and AI development? AI integration connects existing, proven models, such as those from OpenAI, Anthropic or Google, to your product, data and workflows. AI development means building or training models yourself. Most startups need integration, which is faster and far cheaper. We only recommend development when an off-the-shelf model genuinely cannot do the job.

Can AI work inside our existing product and workflows? Yes. Most of the value comes from improving what you already have: sorting support tickets automatically, answering questions from your own documents, or letting AI act inside your tools under controlled permissions. Nothing needs to be rebuilt to start.

How do we know which AI features are worth building? That is what Step 1 is for. We score every idea on customer value, feasibility, data quality, risk and running cost, then prove the best one on your real data before you commit to a full build.

How long does it take, and what does it cost? A proof of concept takes 2–4 weeks and starts from US$5k. A production feature takes 4–10 weeks, typically US$15k–50k, depending on complexity. Every engagement has a fixed scope and fixed fee.

Is our data safe? Will it be used to train AI models? Your data stays yours. We send it only to providers and settings you approve, use options that prevent providers training on it where available, and never use it to train anything outside your product. Access is limited to exactly what each feature needs.

What happens when the AI gets something wrong? It will sometimes, so we design for it. Answers show their sources, users can correct them easily, consequential actions need a person's approval, and every change is tested against real examples before release. You will know how often it is right before your customers do.

Which AI models do you use? Whichever is best for your use case. We test models from OpenAI, Anthropic, Google and open-source providers on your data, and build so you can switch later without rebuilding, because the best option changes every few months.

Less hype, more leverage

The companies that win with AI will not be the ones that added it first or loudest. They will be the ones that put it where it changes what customers get, or what the business costs to run, and kept it reliable, affordable and safe as they grew.

Let's find where AI earns its place in your product.