AI Product Development
Add a large language model to the software you already have
You do not need a new product to use AI. We integrate large language models into the software you already run, with the engineering that production use requires.
The problem
The problem this solves
Calling a large language model API takes a few lines of code. Running it reliably in a real product is a different job. Answers vary from one request to the next. Costs grow with every user. Requests fail or time out. Sensitive data may be sent to the wrong place. And without logging, nobody can say whether the feature is actually good.
Many teams discover this after a quick prototype impresses everyone and then struggles in production. The demo worked on five hand-picked examples; real users send thousands of unexpected ones.
LLM integration done properly covers the whole path: choosing a model for each task, designing prompts and output formats, managing the context window so the model gets the right information without waste, counting tokens to control cost, handling failures and timeouts, masking sensitive data, and logging inputs and outputs so quality can be measured.
We also avoid lock-in. A thin layer between your code and the model provider lets you switch models or providers as prices and quality change, without rewriting features. That matters in a field where the best choice changes every few months.
Common integrations include summarizing records, drafting emails, classifying tickets, extracting data, translating content and answering questions from documents. Our AI model directory shows the models we typically consider, and our guide explains how to choose.
What you get
What you get
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Feature design
Where AI adds value in your product, and what each feature must do well.
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Model selection
Models compared on your examples for quality, speed and cost.
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Prompts and output formats
Prompts, examples and structured formats that give consistent results.
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Provider abstraction
A layer that makes switching models or providers a configuration change.
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Cost controls
Token budgets, caching, rate limits and cost per feature tracked.
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Privacy and safety
Sensitive data masked, providers chosen for your rules, and content filters where needed.
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Reliability
Retries, timeouts, fallbacks to another model and graceful error messages.
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Evaluation and logging
Test sets, quality scores and logs for every request.
How we build it
How we build it
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1
Choose features
We pick one or two AI features with clear value and measurable quality.
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2
Collect examples
Real inputs and good outputs gathered for testing.
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3
Prototype and compare
Features prototyped and models compared on your examples.
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4
Integrate
Production code with the abstraction layer, controls, logging and tests.
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5
Launch and monitor
Staged release with quality and cost dashboards.
AI and people
Where AI helps, where people decide
AI makes the repetitive parts faster. The decisions that shape your product stay with experienced people.
Where AI speeds things up
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Running the AI feature itself inside your product.
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Generating test inputs, including unusual ones.
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Scoring outputs against expected answers automatically.
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Writing integration code and tests.
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Summarizing logs to spot quality problems.
Where people decide
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Which features are worth adding.
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Which data may be sent to which provider.
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The quality bar for each feature.
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Budget limits and cost per feature.
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When a feature is ready for all users.
Is this right for you?
When this is the right choice
A good fit when
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You have an existing product or internal system and want AI features in it.
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A prototype worked but production quality or cost is a problem.
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You want to avoid being locked into one AI provider.
Consider something else when
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You need a new standalone AI product. See AI agents or knowledge assistants.
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The task follows fixed rules that normal code can handle.
Timeline and cost
What affects the timeline and cost
A first production AI feature in an existing product usually takes a few weeks, including evaluation. Later features are faster because the integration layer, logging and controls already exist.
We do not publish fixed prices because scope drives cost. How we estimate.
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Number of features
Each feature needs prompts, tests and interface work.
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Existing codebase
Integrating into older or complex code takes more care.
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Data rules
Strict privacy needs masking, provider checks or self-hosted models.
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Usage volume
Model costs grow with requests; caching and model choice help.
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Quality requirements
Customer-facing features need more evaluation than internal ones.
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Self-hosting
Running open models on your own servers adds infrastructure work.
Keep exploring
Related services, solutions and reading
Related services
View all related services- AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
- AI copilots AI helpers built into your product or internal tools that draft, summarize and suggest while people decide.
- App store launch Store listings, review requirements, test builds and release management for the App Store and Google Play.
- AI agents AI that takes actions in your systems, such as qualifying leads or processing requests, with people checking the results.
- Landing pages Single-purpose pages for a campaign, a launch or a product, built to load fast and test quickly.
- Headless CMS Content managed in one place and published to your website, app and other channels through an API.
Solutions
View all solutions- AI-assisted content workflows Briefs, first drafts, edits and repurposing in a workflow where people set the angle and approve every word.
- Feedback analysis Read every review, survey and ticket, group them by theme and sentiment, and show what customers keep asking for.
- AI translation workflows Translate websites, products and support content quickly with AI, a shared glossary and native-speaker review where it matters.
- AI support agent An assistant that answers routine questions from your own content, checks orders and hands anything else to a person.
- AI lead qualification Score, research and route every new inquiry so your team talks to the best leads first.
- AI sales research agent Short, sourced briefings on each prospect before a call, drafted by an AI agent from public information and your CRM.
Industries
View all industries- SaaS and startups MVPs, subscription billing, AI features, multi-tenant platforms and scaling support for founders and product teams.
- Ecommerce Online stores, product data cleanup, support assistants, price monitoring and order automation for online retailers.
- Education and elearning Course platforms, student portals, content workflows and AI tutoring assistants for schools and training companies.
- Hospitality and travel Booking sites, guest messaging, review analysis and multilingual content for hotels, tour operators and travel businesses.
Case studies
View all case studiesGuides and articles
View all guides and articles- How to choose an AI model for your business A step-by-step way to pick an AI model by testing candidates on your own tasks, data rules and budget.
- What is RAG and when do you need it? How retrieval-augmented generation lets AI answer from your documents, when it fits and how to build it well.
- How to keep customer data safe when using AI Practical steps to protect customer data when you use AI services, from data terms to access and logging.
- Prompt writing basics for business teams How to write prompts that get consistent, useful results from AI tools, with templates your team can reuse.
- RAG vs fine-tuning Two ways to make AI work with your knowledge, compared by cost, accuracy and upkeep.
AI models
View all ai models- General purpose All-round language models for writing, summarizing, support, extraction and most everyday business tasks.
- Reasoning Models that think through multi-step problems before answering: analysis, planning, math, complex documents and agent work.
- Image and video Models that generate or edit images and video from text and reference images.
- Open weights Models whose weights you can download and run on your own servers or a cloud of your choice.
- Claude Opus 5.5 Anthropic's recommended starting point for most serious work: long-running agentic coding and knowledge work, with a 1M token context window.
- Claude Sonnet 5 Anthropic's balance of speed and intelligence: a strong everyday model for assistants, document work, tool calling and coding.
Glossary terms
View all glossary terms- LLM integration LLM integration is connecting a large language model to your software and data, so AI features work inside your own products and processes.
- Large language model A large language model, or LLM, is an AI model trained on vast amounts of text that can understand and generate language, and often images and code.
- Token A token is a small piece of text, often part of a word, that AI models read and write, and that providers use to measure limits and pricing.
- Context window A context window is the maximum amount of text, measured in tokens, that an AI model can consider at once, including the question, documents and its answer.
- AI copilot An AI copilot is an assistant built into a tool people already use, which suggests, drafts and explains while the person stays in control.
- Artificial intelligence Artificial intelligence is the broad field of building software that performs tasks that normally need human judgment, such as understanding language or images.
FAQ
Questions about LLM integration
Have a question that is not here? Ask us directly.
It depends on the task, cost, speed and data rules. We test candidates such as Claude, GPT and Gemini models, and open-weights options, on your examples. See our AI model directory.
Business API terms from major providers generally state that API data is not used for training by default, but terms change and differ. We check current terms and choose settings and providers that fit your rules.
With the right model per task, prompt and context trimming, caching, usage limits and cost tracking per feature. We estimate costs from your expected volume before launch.
Yes. We build a thin abstraction layer so switching models is a configuration change plus a re-run of the test set. See AI model evaluation.