AI Integration Services
AI integration is connecting a large language model's API to a product you already have, so a feature like summarisation, drafting, classification or search appears inside the tool your team already uses, instead of a separate AI tab nobody opens. At Beacon Coders this is the smallest and most common AI engagement we run, and honestly, it is what most businesses asking about "AI development" actually need — not a custom-trained model, not an autonomous agent, just an existing model wired into an existing workflow. Projects start at ₹1,20,000 / $1,450 and typically run 3 to 8 weeks.
Why most businesses need this, not model training
Training a new model from scratch is a research undertaking — it needs a large, well-labelled dataset, specialised infrastructure, and a cost and timeline structure that looks nothing like a normal software project. Very few businesses have a problem that actually requires it. What most businesses have is a specific, well-defined task — summarise this document, draft a reply to this type of email, classify this ticket by urgency — that an existing model from OpenAI, Anthropic or Google can already do well once it is given the right context and prompt. That is integration, not training, and it is a fraction of the cost and timeline. We will tell you plainly which one your project actually needs, and it is usually this page.
This is also distinct from a standalone chatbot or agent. Integration adds an AI-powered feature inside an interface you already have — a "summarise this thread" button in your support tool, a "draft a response" suggestion in your CRM — rather than building a new conversational surface or a multistep autonomous workflow. If what you are describing is a whole new chat interface, or a system that takes actions across multiple tools on its own, start on one of those pages instead; if it is one feature bolted into an existing screen, this is the right scope.
What's included
Scoping the specific task the AI feature needs to perform, with realistic accuracy expectations set before development starts, not after
Scoping the specific task the AI feature needs to perform, with realistic accuracy expectations set before development starts, not after
API integration with the model provider best suited to the task
OpenAI, Anthropic Claude, or Google Gemini — chosen per task rather than a fixed preference
Prompt engineering and testing against real examples from your data, not generic samples
Prompt engineering and testing against real examples from your data, not generic samples
Interface work to surface the feature inside your existing product, matching your current design rather than looking bolted on
Interface work to surface the feature inside your existing product, matching your current design rather than looking bolted on
Cost controls
rate limiting, caching, and usage monitoring — since API costs scale with usage and an unmonitored integration can produce a surprising bill
Data handling review so you know exactly what information is sent to the model provider and whether that fits your privacy and compliance requirements
Data handling review so you know exactly what information is sent to the model provider and whether that fits your privacy and compliance requirements
Fallback behaviour for when the AI feature is unavailable or produces a low-confidence result, so the surrounding product keeps working
Fallback behaviour for when the AI feature is unavailable or produces a low-confidence result, so the surrounding product keeps working
A support agreement covering prompt tuning as real usage reveals edge cases
A support agreement covering prompt tuning as real usage reveals edge cases
What's not included
AI integration does not include training a new foundation model — that is a distinct research engagement outside our standard scope, and we will say so if that is genuinely what your problem requires. It does not include building a standalone chatbot interface (see AI chatbot development) or a multistep autonomous agent (see AI agent development). It does not include the ongoing API usage costs from the model provider, which are billed to you directly by OpenAI, Anthropic or Google based on your usage.
Frequently asked questions
How we scope and build an integration
Discovery is short relative to other AI services, because the scope is narrow by design — we confirm the specific task, the data it needs access to, and what a "good enough" result looks like, as part of the same discovery stage behind every AI development project. Build typically fits inside a single sprint to a few sprints depending on complexity, with prompt tuning tested against your real data rather than synthetic examples throughout. Launch includes usage monitoring so you can see cost and performance from day one, not discover them in next month's invoice.