AI Development & Consulting

AI Development & Consulting for Enterprises and Product Companies

We help businesses build with AI — whether you're an enterprise integrating AI into existing operations or a founder building an AI-native product from scratch. Practical AI implementation backed by eight years of software engineering experience — not hype, not prototypes, not proof-of-concepts that never ship.

600+

Projects Delivered

99%

Satisfaction Rate

8+

Years Experience

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Two Ways We Work with AI

AI means different things to different businesses. We work with both.

For Enterprises

Already running a business. Want AI to make it faster, cheaper, or smarter.

You have existing operations, existing software systems, and specific workflows that cost more than they should or take longer than they need to. AI can be applied selectively to the highest-value problems — but only if the implementation is grounded in your actual processes, not in generic AI demos.

AI-powered document processing and data extraction
Intelligent automation of manual operational tasks
Predictive analytics and decision-support tools
AI-enhanced customer interfaces and support
LLM integration into existing software systems

Talk to us about enterprise AI →

For Product Companies

Building a product where AI is the core value proposition.

Your product idea depends on AI doing something useful that wasn't possible before — generating content, processing language, understanding context, or making decisions based on patterns in data. This requires more than calling an API. It requires prompt engineering, model selection, latency management, cost optimisation, and production architecture that holds up under real usage.

AI-powered SaaS products (OpenAI, Anthropic, Gemini integration)
Custom LLM applications and chat interfaces
AI-driven content generation platforms
Intelligent search and recommendation systems
AI automation tools and workflow platforms

Build your AI product with us →

AI Services We Deliver

From OpenAI integration to custom AI infrastructure — practical AI that ships.

LLM Integration & Prompt Engineering

Integration of OpenAI, Anthropic Claude, Google Gemini, and open-source models into your products and workflows. Prompt design, context management, output validation, and the engineering work required to make LLM outputs reliable enough to ship to production users.

AI-Powered SaaS Products

End-to-end development of SaaS products where AI is central to the value proposition. Full-stack development covering AI integration, application logic, user interface, API design, and cloud deployment. Products built to be used by real customers — not demos.

Intelligent Process Automation

AI applied to specific operational workflows — document extraction, classification, routing, and summarisation. Email triage systems, invoice processing automation, and decision-support tools that reduce manual work without requiring full workflow replacement.

AI Data Pipelines & RAG Systems

Retrieval-Augmented Generation (RAG) systems that ground LLM responses in your own data. Vector databases (Pinecone, pgvector), embedding pipelines, semantic search, and document ingestion systems. AI that knows your business context — not just generic training data.

AI Consulting & Strategy

Before building, we help you decide what to build and whether AI is the right tool for the problem. Use case identification, model evaluation, build vs. buy analysis, cost modelling, and risk assessment. Honest advice from engineers who have shipped AI products — not consultants selling frameworks.

AI Infrastructure & Cost Optimisation

AI API costs can scale unexpectedly. We design systems with caching, prompt efficiency, model tier selection, and batching strategies that keep costs predictable. Monitoring, rate limit management, and cost dashboards for AI workloads that need to be commercially viable.

AI Technologies We Work With

We're model-agnostic. We use what's right for the use case.

LLM Providers

OpenAI (GPT-4o, GPT-4)Anthropic (Claude)Google (Gemini)Mistral AIMeta (Llama)

Vector & Search

PineconeWeaviatepgvector (PostgreSQL)ElasticsearchSemantic Search

AI Frameworks

LangChainLlamaIndexHugging FaceOpenAI SDKVercel AI SDK

Built on Our Core Stack

Node.js Python React Next.jsAWS / GCP

We don't have a favourite model we apply to every project. GPT-4o is often the right choice for production applications requiring consistent quality. Claude is strong for long-context tasks and nuanced instruction following. Open-source models through Hugging Face make sense when data privacy requirements make third-party APIs unsuitable. We'll recommend what we'd actually use if it were our product.

What We Tell Every Client Before Starting an AI Project

AI is genuinely powerful and genuinely overhyped. Here is what eight years of building software has taught us about where AI creates real value — and where it doesn't.

Where AI creates real value

AI delivers genuine business value when it's applied to problems where speed and scale matter more than perfect accuracy, where humans currently do repetitive pattern-matching at high volume, or where synthesising large amounts of text into actionable summaries creates leverage.

Processing thousands of documents that would take humans weeks
Generating first drafts of content that humans then review and approve
Classifying and routing incoming requests intelligently
Answering questions about your own documentation and knowledge base

Where AI is the wrong tool

AI is often proposed as the solution to problems that are actually solved more reliably by simpler software. Before recommending AI, we ask whether the problem can be solved with deterministic logic, a well-designed database query, or a straightforward rules engine.

Problems with clear, deterministic rules that can be coded directly
Use cases where 100% accuracy is required and errors have high cost
Tasks where the existing manual process is fast enough and the volume is low
Marketing problems dressed up as AI opportunities

What nobody tells you about AI costs

API costs are per token — and production usage at scale can be surprisingly expensive. A feature that costs $0.05 per interaction might seem trivial until you have ten thousand users. Cost architecture is part of our AI project scoping, not an afterthought.

We model AI costs before committing to an architecture
Caching, batching, and prompt compression are part of our standard toolkit
We recommend smaller models for tasks that don't require GPT-4-level capability
We build cost monitoring into every AI product we deploy

Hallucination is a real product risk

LLMs generate plausible-sounding text that is sometimes factually wrong. In a consumer chatbot this is an annoyance. In a product used to make business decisions, it's a liability. We build validation layers, output constraints, confidence thresholds, and human-in-the-loop checkpoints to manage this risk.

RAG systems ground responses in your actual data, reducing hallucination risk
Output parsing and structured response formats reduce unpredictability
Human review workflows for high-stakes outputs
Logging and monitoring to catch systematic errors in production

Building AI products also requires security by design. See our Cybersecurity & Secure Development services — AI security considerations are part of every AI project we deliver.

How We Approach AI Projects

AI projects fail in predictable ways. Here is how we avoid them.

Use Case Validation

Before any development starts, we spend time understanding the specific problem you're trying to solve. We ask what happens if the AI output is wrong 5% of the time. We ask whether this problem could be solved with simpler software. We ask what a successful outcome actually looks like. If AI is the right tool, we define the use case precisely — including what data we have, what data we need, and what "good enough" output means for this specific application.

Model Selection and Prototyping

We run targeted experiments — not full builds — to validate that the AI approach will work before committing to full implementation. This means testing multiple models against your actual data, measuring accuracy against the threshold that makes the feature viable, and identifying edge cases that will require special handling. The output of this phase is confidence that the approach works and a clear specification for the production build.

Architecture and Cost Design

We design the technical architecture with cost and reliability as first-class concerns. Which model for which tasks. Where to apply caching. How to handle rate limits. What happens when the AI API is unavailable. How to structure prompts for consistency and cost efficiency. How to monitor quality in production. This phase produces an architecture that will hold up at the scale you're targeting — not just in demo conditions.

Production Build and Integration

The full production implementation — AI pipeline, application logic, user interface, API integrations, and deployment infrastructure. We build the AI components as part of the full product, not as isolated experiments bolted onto an existing system. Two-week sprints, working builds, and your sign-off before each sprint continues. The AI functionality ships as part of a complete, tested product.

Monitoring and Optimisation

AI products require ongoing monitoring in a way that deterministic software doesn't. Model outputs drift. User patterns change. API providers update models. We build logging, monitoring, and cost tracking into every AI product — and we offer ongoing support engagements for products where quality and cost management require continued attention after launch.

AI Products We've Built

Two AI-powered products built for US clients using OpenAI and deployed to real users.

AI Product · E-Commerce · United States

Mebag — AI-Powered Universal Shopping Cart

A mobile shopping platform that uses AI to aggregate products from across the web into a single, unified cart. Users describe what they're looking for and the AI identifies, compares, and surfaces relevant products from multiple retailers. OpenAI integration for natural language product understanding, React Native mobile application, and a Node.js backend handling product data pipelines and AI response caching at scale. Live on Google Play and serving US customers.

Available on Google Play →

AI Platform · Content · United States

Tully & Heidi — AI Content Writing & Conversation Platform

An AI-powered platform for content writers and marketers that combines long-form writing assistance with an AI conversation interface. Built on OpenAI with custom prompt chains for different content types — blog posts, marketing copy, product descriptions, and social content. Multi-user SaaS with subscription billing, usage metering, and a React-based interface designed for professional writers who need AI as a tool, not a replacement. Deployed and serving US-based content teams.

Learn more →

Common Questions About AI Development

Yes — OpenAI (GPT-4o and GPT-4) is the model we've used most in production applications and the one we're most confident making architectural decisions around. We've also shipped production applications using Anthropic Claude and have built internal prototypes with Google Gemini and open-source models. We recommend based on the specific requirements of your project — accuracy needs, context window requirements, cost constraints, and data privacy considerations.

Yes, and this is often the most valuable conversation we can have before any development starts. Our consulting engagements begin with a structured discovery process — understanding your workflows, identifying where the highest-value problems are, and assessing whether AI is the right tool or whether a simpler solution would be more reliable and cost-effective. We'll tell you honestly if your problem isn't a good AI use case. We'd rather lose a project than build something that doesn't deliver value.

AI API costs are a real commercial consideration that we treat as a first-class architectural concern, not an afterthought. During the design phase we model expected costs at different usage levels. We implement caching for repeated or similar queries, use smaller models where GPT-4-level capability isn't needed, optimise prompt length, and build rate limiting and cost monitoring into every production AI system. We also provide cost dashboards so you can see exactly what the AI layer is costing you on an ongoing basis.

This is a legitimate concern and the answer depends on your specific data and regulatory environment. OpenAI's enterprise API terms include data processing agreements that address GDPR requirements. For applications handling highly sensitive data — HIPAA-covered health information, legally privileged documents, or information that cannot leave your jurisdiction — we evaluate whether a self-hosted open-source model is more appropriate. We'll discuss the tradeoffs with you specifically and document the approach in writing before development starts.

Yes, with caveats. Before committing to take over an existing AI project, we perform a technical review — assessing the current architecture, the quality of the existing codebase, the prompt design, and whether the current approach is salvageable or requires a rebuild. We'll tell you honestly what we find, including if the current implementation has fundamental problems that mean a rebuild would be more efficient than an extension. We've taken over AI projects from other agencies and know what to look for.

It depends significantly on whether we're integrating AI into an existing product or building an AI-native product from scratch. Adding a single AI-powered feature to an existing application — document summarisation, intelligent search, or a chat interface — typically takes four to eight weeks. A new AI-powered SaaS product built from the ground up is typically three to six months for the first production-ready release. The validation phase at the start is not optional — we won't commit to a timeline for an AI feature before we've confirmed the approach works with your actual data.

Ready to build with AI?

Whether you're an enterprise exploring AI adoption or a founder building an AI product, we'd like to understand what you're trying to achieve. We'll tell you honestly whether AI is the right tool, and what building it properly actually involves.