AI development
AI development is the work of wiring artificial intelligence into a business process, not training new models from scratch. At Beacon Coders that means building AI agents that complete tasks, chatbots that handle support and sales conversations, and integrations that connect large language models to the systems you already run. Most businesses need this kind of integration rather than custom model training, and our AI practice draws on the same engineering team that has delivered 450+ web, mobile and software projects since 2016.
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.
Frequently asked questions
Yes, and most of our projects are scoped that way. Agents typically handle the repetitive first pass of a task — sorting, drafting, flagging — and hand off the judgment calls to a person. The goal is usually freeing up hours, not removing the role.
For most projects, we use existing models from OpenAI, Anthropic or Google through their APIs and build the integration, prompting and guardrails around them. We only recommend training a custom model when a proven business case shows an existing model cannot meet the accuracy or cost requirement.
A chatbot responds to messages in a conversation. An agent completes a task, which may involve several steps and several tools — checking a database, drafting a document, sending it for approval — without a person driving each step. Many projects combine both: a chatbot interface backed by an agent doing the work behind it.
We scope the failure modes during discovery and build guardrails around them: restricting what data the system can access, requiring human approval for actions above a defined risk level, and logging every decision so you can audit what happened. No AI system is error-free, so the design goal is making errors visible and recoverable, not eliminating them entirely.
Yes. Most chatbot projects integrate with the CRM or helpdesk you already use — Salesforce, HubSpot, Zendesk or a custom system — so conversations create or update real records instead of living in an isolated tool.
We configure API access through provider settings that keep your data out of model training wherever that option exists. We document this as part of the project setup. You should still confirm the specific data-retention terms with your provider agreement, since these vary and change over time.
A single-purpose agent or chatbot typically takes six to ten weeks from discovery to launch. Agents that touch several systems or require extensive guardrail testing take longer, and we give you a fixed timeline once discovery defines the actual scope.
Not always. Some predictive tasks work with a few hundred well-labelled examples from your own operations; others genuinely need more data than you currently have. We assess this during discovery and will tell you if your dataset is not yet large enough to support a reliable model.
We build an approval step directly into the workflow — the system prepares an action and a person confirms it before it runs — rather than treating human review as an afterthought. Which actions need review is decided during discovery based on what happens if the system gets it wrong.
Explore our AI development services
What does AI development not include?
This page covers agents, chatbots, integrations and applied machine learning. It does not cover: - Training a new foundation model from scratch — a research engagement outside our standard scope. - General web or mobile application development — see web development and mobile app development. - Bulk AI-generated marketing content or SEO content at scale — see digital marketing. - Guaranteed accuracy on high-risk decisions without human review — we build human checkpoints into any process with real consequences for being wrong. - Ongoing API usage costs from OpenAI, Anthropic or Google, which are billed to you directly by those providers.