AI Software Development
Built to Solve Real Problems
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 data pipelines — we build AI that earns user trust, not AI that demos well and breaks in production.
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Why Most AI Projects Fail
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 nobody modelled the infrastructure costs before building.
AI features without a clear use case
Adding AI because competitors are doing it produces features nobody uses. The integration that delivers value is the one that solves a specific, well-defined user problem — not the one that demos well in a boardroom.
Data that isn't ready for AI
AI systems are only as good as the data they run on. Inconsistent, incomplete, or unstructured data creates outputs that users don't trust and systems that are impossible to maintain in production.
API costs that weren't modelled
OpenAI and Anthropic charge per token. Products built without modelling usage at scale can find themselves with AI infrastructure costs that destroy unit economics before they reach $100k ARR.
Outputs that can't be trusted
Hallucinations and inconsistent outputs are a product problem, not just a model limitation. Building AI that users trust requires prompt engineering, output validation, and careful UX design around uncertainty.
AI Development Services
AI product development from use case definition through to production deployment and ongoing cost optimisation.
AI-Powered Product Features
OpenAI, Anthropic, and other LLM integrations built into your product. Content generation, intelligent search, recommendation engines, and conversational interfaces that work reliably in production.
AI-Augmented Applications
Existing applications enhanced with AI. Intelligent data processing, automated classification, smart suggestions, and workflow automation that reduces manual effort for your users.
Conversational AI Platforms
AI chat interfaces, writing assistants, and conversation platforms built on foundation models. Properly prompt-engineered, cost-modelled, and tested against real user behaviour before go-live.
AI Automation & Pipelines
Backend AI systems for document analysis, data classification, and automated decision support. Pipelines that process data at scale with AI at the core — not just bolted on.
AI Products We've Built
Two AI-powered products built by Teamseven for US clients, shipping in production.
Mebag — AI-Powered Universal Shopping Cart (US)
Mebag is the first AI-powered universal shopping cart for the open web. We built the full product — an AI system that lets users save products from any online store, track price drops, and buy across multiple retailers in a single checkout. OpenAI 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 OpenAI for content generation and multi-turn conversations. Two AI products. Two US clients. Both built from scratch by Teamseven.
Technologies We Use for AI Development
We match the AI infrastructure to the problem — these are the tools we use most on AI builds.
Questions About AI Development
Do you build with OpenAI, Anthropic, or other LLM APIs?
Yes — we've built production AI products using OpenAI's API including GPT and embedding APIs, and Anthropic's Claude API. We help clients choose the right model based on capability, cost, and reliability requirements. We also model API costs as part of every AI build — no surprises at scale.
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 UX design around uncertainty are part of how we build every AI feature. 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 building?
Before any AI build we estimate token usage based on expected user behaviour, model pricing, and feature scope. We produce a cost-per-user-per-month estimate and build it into the product economics discussion. AI infrastructure costs that aren't modelled upfront become business model problems at $500k ARR.
Do you build RAG systems or custom AI pipelines?
Yes — retrieval-augmented generation (RAG) systems that allow AI to answer questions against your specific data are one of our AI specialisms. We build the full pipeline: document ingestion, chunking, embedding, vector storage, retrieval, and the LLM layer on top.
Building an AI product?
Tell us what it does.
We've built AI-powered products for US, UK, and Australian clients. Whether you're adding AI to an existing product or building AI-first from scratch, we'd like to understand the use case.
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