AI development

Who is AI development for?

This service fits businesses with a defined, repeatable process they want handled faster or around the clock: support ticket triage, lead qualification, document processing, or internal knowledge lookup. If you can describe the steps a good employee follows today, we can usually design an agent or integration around them.

It is not the right fit if what you actually need is a new foundation model trained on proprietary data at scale — that is a research undertaking with a different cost structure and timeline, and we will tell you if that is what you are describing rather than scope it as a standard project.

A useful question to ask yourself first: could a skilled person follow written instructions to do this task using the tools you already have? If so, an agent or integration can very likely automate most of it. If the task requires judgment no one could write down as a rule, that is a sign the project needs more discovery before it needs any code.

What services fall under AI development?

Four services make up this practice:

• AI agents — systems that complete multi-step tasks against your existing tools, such as pulling data, drafting a response and flagging it for approval before anything is sent.
• AI chatbots — conversational interfaces for support or sales, trained on your documentation and connected to your CRM or helpdesk so conversations turn into real records.
• AI integration — connecting a large language model API to an existing application, so features like summarisation or drafting appear inside tools your team already uses instead of a separate tab.
• Machine learning — predictive models built on your own data, for tasks like demand forecasting or anomaly detection, distinct from general-purpose language models and usually smaller and cheaper to run.

How do you build an AI agent or integration?

Discovery here focuses on mapping decision points and failure modes: what the system should do automatically, what it should flag for a person, and what "wrong" looks like for your specific process. Design covers the conversation or task flow, plus the guardrails that stop the system from acting outside its scope.

Build happens in two-week sprints against a real subset of your data, not synthetic examples, so we catch edge cases early. Testing focuses on accuracy and failure handling specifically — how the system behaves on ambiguous or unexpected input, not just whether it works on the happy path. Launch includes a monitoring setup so you can see what the system is doing, and support includes tuning as real usage reveals gaps the original scope did not anticipate.

What technologies do you use for AI development?

We build primarily on the OpenAI, Anthropic Claude and Google Gemini APIs, choosing the model based on the task rather than a fixed preference. For agents that need to reason across multiple steps, we use orchestration frameworks like LangChain or LlamaIndex. Where a system needs to search your own documents, we use vector databases such as pgvector or Pinecone. Backends are built in Python or Node.js and integrated with your existing CRM, helpdesk or ERP through their APIs.

How do you handle data privacy in an AI build?

AI systems that touch customer or business data need the same access controls and audit logging as any other system, plus explicit rules about what gets sent to a third-party model provider and what stays inside your own infrastructure. We map this during discovery, field by field, rather than assuming every piece of data can flow through an external API.

Where a client's compliance requirements rule out sending certain data externally, we design around self-hosted or restricted-access models instead, even though that usually costs more to run.

What does an AI development engagement cost?

Cost depends on how many systems the agent touches, how high the accuracy bar is, and how much human review the process needs before an action is trusted to run unattended. As with our other practices, we work under fixed scope, dedicated team or staff augmentation models, detailed on our pricing page.

A single-purpose chatbot or integration fits fixed scope well. An agent that will keep expanding into new tasks as it proves itself fits a dedicated team, since tuning and scope both grow with usage.