AI & Technology

AI Software
Development Company

We build AI-powered products and integrations for startups and businesses across the US, UK, and Australia. From OpenAI-powered applications to intelligent automation and machine learning systems — built to solve real problems, not to follow trends.

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Where AI Projects Go Wrong

Most AI projects fail not because the technology doesn't work, but because the problem wasn't defined precisely enough, the data wasn't ready, or the integration into the existing product was never properly planned.

AI features without a clear use case

Adding AI because competitors are doing it produces features nobody uses. The AI integration that delivers value is the one that solves a specific, well-defined user problem — not the one that demos well.

Data that isn't ready for AI

AI systems are only as good as the data they're trained on or given access to. Inconsistent, incomplete, or unstructured data creates AI outputs that users don't trust and systems that are impossible to maintain.

API costs that weren't modelled

OpenAI, Anthropic, and other AI API providers charge per token. Products built without modelling usage costs at scale can find themselves with AI infrastructure costs that destroy unit economics.

Outputs that can't be trusted

AI hallucinations and inconsistent outputs are a product problem, not just a technology limitation. Building AI products that users trust requires prompt engineering, output validation, and careful UX design around uncertainty.

What We Build for AI-First Companies

AI product development from use case definition through to production deployment and ongoing optimisation.

AI-Powered Product Features

OpenAI, Anthropic, and other LLM integrations built into your product. Content generation, intelligent search, recommendation systems, and conversational interfaces that work reliably in production.

AI-Augmented Applications

Existing applications enhanced with AI capabilities. Intelligent data processing, automated categorisation, smart suggestions, and workflow automation that reduces manual effort for users.

Conversational AI Platforms

AI chat interfaces, writing assistants, and conversation platforms built on top of foundation models. Properly prompt-engineered, cost-modelled, and tested against real user behaviour.

AI Automation & Pipelines

Backend AI systems for data processing, document analysis, classification, and automated decision support. Pipelines that process data at scale with AI at the core.

AI Products We've Built

Two AI-powered products built by Teamseven for US clients.

AI Product · Shopping · United States

Mebag — AI-Powered Universal Shopping Cart (US)

Mebag is the first AI-powered universal shopping cart for the open web. Teamseven built the full product — an AI system that lets users save products from any online store, track price drops, and buy across multiple retailers with a single checkout. OpenAI integration powers the AI product discovery and recommendation engine.

We also built Tully & Heidi — an AI content writing and conversation platform for a US client, using the OpenAI API for content generation and multi-turn AI conversations. Two AI products, two US clients, one development partner. Both built from scratch by Teamseven.

Common Questions

Do you build products with OpenAI or other LLM APIs?

Yes — we've built production AI products using OpenAI's API, including the GPT and embedding APIs. We also work with Anthropic and other providers depending on the use case. We help clients choose the right model and manage API cost modelling as part of every AI build.

Can you add AI features to an existing product?

Yes — AI feature integration is one of our most common AI engagements. We assess the existing codebase, define the specific AI use cases that will deliver user value, and build the integration in a way that fits the existing architecture. We don't add AI features for their own sake.

How do you handle AI output quality and reliability?

Prompt engineering, output validation, fallback handling, and user-facing design around uncertainty are part of how we build AI features. We build AI systems where users can trust the output, not AI systems that occasionally produce impressive demos and regularly produce unusable results.

How do you model AI API costs?

Before any AI build we estimate token usage based on expected user behaviour, model pricing, and feature scope. We size the cost per user per month and factor it into the product economics discussion. AI infrastructure costs that aren't modelled upfront become business model problems at scale.

Building an AI product?

We've built AI-powered products for US clients since 2022. Whether you're adding AI to an existing product or building an AI-first product from scratch, we'd like to hear about it.