Generative AI Development Company
Assistants that answer from your knowledge base, copilots that draft in seconds and content engines that scale your output. We build generative AI that's accurate, secure and ready for real users.
Large language models can write, summarise, reason and answer questions with remarkable fluency. But on their own they don't know your products, policies or customers — and they can be confidently wrong. Business value comes from connecting them to your data and wrapping them in the right controls.
We design generative AI systems that do exactly that. Using retrieval-augmented generation, fine-tuning where it's justified and careful prompt and guardrail design, we build assistants and tools that produce accurate, on-brand output — then integrate them into the applications your team and customers already use.
Generative AI solutions designed for specific, measurable jobs.
Assistants that answer questions from your documents, wikis and databases, with citations.
In-app assistants that help users draft, analyse and complete tasks inside your software.
Systems that generate product descriptions, reports and marketing copy at scale, on-brand.
Models adapted to your domain language, tone or task where prompting alone isn't enough.
Add generative capabilities to existing CRMs, help desks and internal tools via APIs.
Use-case discovery and rapid proofs of concept that validate value before major investment.
A cross-section of the AI systems we've built. Each one began with a specific operational problem and was judged by its impact in production, not in a demo.
SaaSA retrieval-augmented assistant that answers customer questions from approved documentation, cites its sources and escalates to agents when unsure.
FintechAn NLP and vision pipeline that classifies documents, extracts fields and flags inconsistencies for human review.
HealthcareA conversational intake agent that collects symptoms and history, summarises them for clinicians and writes to the EHR via API.
EcommerceA machine learning forecasting model combining sales history, seasonality and promotions to recommend purchase quantities.
Three principles that make generative AI dependable.
Responses are based on retrieved, approved sources — and cite them — to reduce hallucinations.
We build evaluation sets and test accuracy, tone and safety before and after launch.
Caching, model routing and prompt optimisation keep token costs predictable as usage grows.
The techniques behind generative AI that works in the real world.
Documents are indexed in a vector database; relevant passages are retrieved and supplied to the model with each question.
In practice
An HR assistant that answers policy questions from the employee handbook and links to the exact section.
Models produce validated JSON and structured data that flows straight into your systems.
In practice
A sales tool that turns call transcripts into CRM-ready summaries, next steps and deal updates.
Style guides, examples and fine-tuning teach models to write in your voice consistently.
In practice
An e-commerce engine generating thousands of product descriptions in the brand's tone for editor review.
Simple requests go to fast, low-cost models; complex ones to more capable models — optimising cost and quality.
In practice
A support assistant that routes FAQs to a small model and complex troubleshooting to a frontier model.
Tell us where your team loses time to repetitive writing, searching or summarising. We'll identify the generative AI use cases with the highest return.
Book a Free GenAI WorkshopEvery sector has its own rules, audiences and pressure points. We shape each engagement around yours, so the work lands with the people it is meant for.
Ecommerce & Retail
Digital Healthcare
Fintech & Banking
Delivery & Logistics
PropTech & Real Estate
On-Demand ServicesWe measure generative AI by time saved, quality improved and revenue influenced — not by how impressive a demo looks. Every project defines those metrics upfront and tracks them after launch.
We also keep you flexible. Our model-agnostic architecture means you can switch providers as models improve or prices change, protecting your investment in a fast-moving market.
The generative AI applications we build most often.
Instant answers from company documents and wikis.
Accurate self-service answers with human handover.
Long reports, contracts and calls condensed in seconds.
On-brand drafts for campaigns and product pages.
Where generative AI changes how work gets done.
Drafting, searching and summarising happen in seconds.
Self-service resolves common questions instantly.
Produce more content without growing the team.
Institutional knowledge is available to everyone.
Copilots make your software more valuable to users.
AI-powered features can be packaged and sold.
Most projects draw on more than one discipline. These are the services that sit alongside this one.
Generative AI refers to models that create new content — text, images, code or audio — based on patterns learned from data. Large language models like GPT, Claude and Gemini are the most widely used examples.
We ground responses in retrieved, approved sources, require citations, set confidence thresholds, test against evaluation sets and route uncertain answers to humans.
Yes. We use enterprise APIs that don't train on your data, or deploy open-source models privately in your cloud. Access controls ensure users only see data they're permitted to.
RAG is usually the best starting point for knowledge-based tasks because it's cheaper and easier to update. Fine-tuning helps when you need a specific style, format or specialised behaviour.
Proofs of concept typically cost $25,000–$50,000. Production solutions range from $50,000 to $180,000+, plus ongoing model usage costs we help you optimise.
We work with OpenAI, Anthropic, Google, Meta Llama, Mistral and others, selecting the best fit for each use case.
Tell us about your project — whether it's a website, an app, SEO or marketing.
