A London-founded team building custom AI models, generative AI features and RAG pipelines for UK businesses, with GDPR and ICO-aware data practices built in from the first sprint.
Real AI products we've built
Quick answer: Apptechies is a London-founded AI development company building custom models, generative AI features and RAG pipelines for UK businesses, matched to whether your product needs a hosted API, a fine-tuned model, or a fully grounded retrieval pipeline, with GDPR-aware data handling from day one.
Supervised, unsupervised and reinforcement learning models tuned to your own UK business data through our machine learning development services, not a generic pretrained checkpoint.
Fine-tuning, prompting and RAG pipelines connecting large language models to your product through our generative AI development services, without hallucinated output reaching users.
Feature pipelines and data versioning that keep model performance reproducible as your UK data volume grows.
Bias testing and explainability considerations covered by our compliance and security practices, important groundwork for ICO expectations around automated decision-making.
We work with businesses across the UK, not just London, and service coverage does not depend on a client being near one of our desks. If you would rather scale your own AI team than outsource the whole build, our hire an AI developer service can embed a dedicated engineer inside your existing team instead.
From intelligent automation to production-ready AI systems, we engineer solutions around your data, workflows and business goals.
Models trained and evaluated against your actual task through our machine learning development services, not a generic leaderboard score.
Fine-tuning, prompt engineering and RAG pipeline development grounded in your own documents and data.
Our AI chatbot development builds context-aware conversational interfaces that actually resolve a task, not a scripted FAQ bot.
RPA development for agentic workflows that call tools and APIs, with guardrails to prevent unsafe actions.
Containerised inference, monitoring and CI/CD so a model keeps performing after launch, not just at demo time.
Our computer vision development covers image classification and quality-control automation for UK manufacturing, retail and healthcare clients.
Most AI projects fail on data quality and scope, not model choice. We scope honestly before we build.
Start with a focused AI assessment
A custom AI-powered keyboard for digital creators and direct sellers — surfacing saved content, generating on-the-fly copy, and putting an entire content library one tap away in any app.
A creator and brand content platform with topic management, review workflows, and an approval dashboard for coordinating content across teams and campaigns.
An AI-assisted hunting and fishing companion app helping outdoor enthusiasts identify species, log catches, and plan trips smarter.
They cleared all my doubts and turned my idea into a powerful app.
Data minimisation and a documented lawful basis are agreed before a single model touches your data, and where possible, fine-tuning happens on de-identified or synthetic data.
Where a model materially affects a UK customer outcome, we build in explainability and a human-review path from the start, not bolted on after a compliance review flags it.
Yes. For clients with data-residency requirements, we scope UK/EU-region cloud deployment (AWS eu-west-2, Azure UK South, etc.) during discovery.
The engineers who scope your model architecture are the ones who build, evaluate and support it in production.
Founded in 2018 and headquartered in London, with 150+ specialists, UK time-zone overlap is the default.
Named Best AI Development Agency by UpCity in 2025, based on verified client work, not a self-issued badge.
A working session on what the feature needs to do, and for whom, before any model gets picked.
We tell you whether prompting, fine-tuning or a full RAG pipeline actually fits, not whichever is easiest for us to build.
A working prototype tested against real questions your UK users will actually ask, not a cherry-picked demo script.
Shipped with drift monitoring and an evaluation harness so quality does not quietly degrade after launch.
The questions we hear most from teams hiring a ai development team. Don't see yours? Ask us directly on the right.
A focused AI feature, a chatbot, a recommendation engine, typically runs $25,000-$60,000. A full custom model or RAG platform usually runs $80,000-$200,000+. We quote against your actual data and use case after a discovery call.
A working prototype typically takes 2-4 weeks once we understand your data. A production-grade deployment with monitoring and evaluation usually runs 8-16 weeks depending on integration complexity.
Data minimisation, a documented lawful basis and encryption-at-rest are architectural decisions made before training starts, with UK/EU-region hosting available where data residency matters.
We start by auditing your current systems and data quality, then scope the smallest AI feature that delivers real value. Most engagements avoid a ground-up rebuild.
Yes. On-device inference, generative features and predictive analytics can typically be layered onto an existing product without a full rebuild, provided the architecture supports it.
Yes. We are happy to sign an NDA before any detailed discussion of your data, models or business logic.
Ask to see AI products the team has actually shipped to production, not just a prototype, then compare how each proposal handles data privacy, model evaluation and what happens after launch, rather than just the headline price.
We scope the specific use case, give an honest recommendation on whether prompting, fine-tuning or a full RAG pipeline fits, build and evaluate against real questions your users will ask, then deploy with monitoring rather than treating launch as the finish line.
An off-the-shelf AI tool can work when your use case is close to standard, like a generic support chatbot. Custom development earns its cost once you need the model grounded in your own data or evaluated against your own accuracy bar.
Prompting works with a hosted model as-is and is fastest to ship. Fine-tuning adjusts the model itself on your data and suits narrow, repeatable tasks. A RAG pipeline grounds a hosted model in your own documents at query time, which usually gives the best accuracy for knowledge-heavy use cases without the cost of fine-tuning.
Encryption at rest and in transit, role-based access control scoped to the smallest team that needs it, and versioned data pipelines so any model output is traceable to its source data.
Yes. You can hire an AI developer to work inside your existing team and codebase on a dedicated basis, or we can run the full project as a self-contained team depending on how you want to staff it.
You own the source code, trained model artifacts and documentation delivered under the engagement. We are happy to sign an NDA before any detailed discussion of your data or business logic.
Yes. Our AI integration services scope exactly what a given AI feature needs from your CRM, ERP or internal API before any code is written.
Tell us what you're building, and a senior engineer or solutions architect will reply within 24 hours.
