AI software development that earns its place in your product
We build AI features that solve a specific user problem, such as assistants, document processing, search and automation, with the evaluation and guardrails needed to run them in production.
Adding AI is easy to demo and hard to do well. Models hallucinate, costs creep up, latency frustrates users and data privacy raises real questions. The difference between a gimmick and a feature people rely on is careful engineering around the model.
Our AI software development team focuses on use cases where AI measurably saves time or unlocks something new: understanding documents, answering questions over your own data, automating multi-step tasks and personalising experiences. We design for accuracy, cost control and clear fallbacks, so the feature keeps working when the model does not.
Who this service is for
SaaS teams adding AI features to an existing product
Founders building an AI-native product from scratch
Operations teams automating document-heavy or repetitive processes
Businesses that want to use their own data with large language models, safely
What we build
LLM assistants & chat interfaces
Conversational assistants grounded in your product's data and permissions.
Document processing & extraction
Read invoices, contracts, forms and reports, and turn them into structured data.
Semantic search & RAG
Retrieval-augmented generation so answers are based on your content, with source references.
Workflow automation & AI agents
Multi-step automations that call tools and APIs, with human approval where it matters.
Classification & recommendations
Tag, route, score and recommend using models matched to the task and budget.
Evaluation, guardrails & monitoring
Test sets, quality checks, cost tracking and safety filters to keep outputs reliable.
Our AI Software Development approach
Problem before model
We start from the user problem and success metric, then pick the simplest approach, which is sometimes not AI at all.
Evaluation from day one
We build test sets so you can measure quality, compare models and catch regressions.
Cost and latency aware
Caching, model selection and prompt design keep running costs predictable.
Privacy conscious
Clear data handling, minimal data sharing and options for provider choice based on your requirements.
How an engagement works
Identify the use casePinpoint where AI can create measurable value and define how success is measured.
Prototype & evaluateBuild a quick proof of concept and test it against real examples from your data.
Integrate & hardenAdd it to your product with permissions, fallbacks, logging and cost controls.
Monitor & improveTrack quality and usage in production, then refine prompts, data and models.
We work with leading hosted models and can choose per use case based on quality, cost, latency and data requirements. We design the integration so you can switch providers later.
How do you keep AI answers accurate?
We ground answers in your own data using retrieval, add citations, write evaluation tests and include human-in-the-loop steps where mistakes would be costly. No system is perfect, so we design clear fallbacks.
Is my data safe when using large language models?
We minimise the data sent to models, apply access controls and can use providers and settings that do not train on your data. We will walk through the options against your compliance needs.
Can you add AI to our existing product?
Yes. Most of our AI work is integrating features into existing SaaS products without a rebuild.
