AI Integration Consulting
Our AI integration consulting services start with your use cases, data and budget. You get a ranked list of features, the right model for each and a fixed-price plan.
LLM Integration Services That Put GPT, Claude and Gemini to Work in Your Product
AngleBrackets offers LLM integration services for startups, SaaS companies and growing businesses in the US, UK, Canada and UAE. We connect large language models like GPT, Claude, Gemini and Llama to the app, data and tools you already run, so you get real AI features: summaries, drafting, smart search, data extraction, copilots and agents. Every custom LLM integration comes with guardrails, cost controls and monitoring, and you can switch models without a rebuild. As part of our artificial intelligence development work, we ship one feature to real users first, then grow it.
First LLM feature from $4,000. See pricing →
- First feature live in 2 to 4 weeks
- Fixed prices, no surprise invoices
- Switch models without a rebuild
- 2 weeks of free support after launch
Book a Free Model Cost Check
Our LLM Integration Services
From one feature in an existing app to a company-wide platform, these are the AI integration solutions we build most. Each one ships with tests, guardrails and a cost dashboard.
LLM API Integration
We wire the OpenAI, Anthropic, Gemini, Azure OpenAI or AWS Bedrock APIs into your backend with streaming, retries, rate limits and structured JSON output.
LLM Gateway and Model Routing
One layer between your app and every model. Route by cost and quality, fall back when a provider is down, and switch models with a config change.
AI Data Integration
LLM database integration with your SQL, CRM, ERP, documents and files, so the model answers and acts on live business data with the right permissions.
Prompt Engineering and Evaluation
Versioned prompts, test sets of real examples and automatic scoring, so quality is measured and doesn't drift after launch.
LLMOps, Monitoring and Cost Control
Dashboards for latency, quality and cost per request, with caching, token budgets and alerts.
Security and Compliance Layer
Personal data redaction, prompt-injection defense, audit logs and data residency for regulated teams.
Fine-Tuning and Open-Source Models
When prompts and RAG aren't enough, we fine-tune or self-host Llama and Mistral for privacy, speed or lower cost.
LLM Integration Services FAQs
Straight answers to the questions we hear on almost every first call. Don't see yours? Ask us on a free call.
What are LLM integration services?
LLM integration services connect a large language model such as GPT, Claude, Gemini or Llama to your software, data and workflows, and make it safe to run in production. That includes choosing the model, building the API layer, connecting your data, writing and testing prompts, adding guardrails and setting up monitoring and cost control. A custom LLM integration turns a demo into a feature real users rely on.
How does AI integration in business actually work?
Your app sends a request to an LLM gateway, which checks the user, removes personal data and fetches only the records that user may see. The gateway sends the job to the right model, checks the answer against your rules and returns it to your app, logging quality and cost. Your users just see a faster, smarter feature.
How much does LLM integration cost?
A first LLM feature in an existing app usually costs $4,000 to $10,000. Several connected features with RAG, a gateway and monitoring cost $10,000 to $30,000, and enterprise platforms with private hosting and governance start around $30,000. See pricing. Model API fees are billed separately by the provider, and after 2 weeks of free support an optional Care Plan starts from $750 a month.
How long does LLM integration take?
A single feature in an existing app usually goes live in 2 to 4 weeks. Several connected features take 4 to 8 weeks, and enterprise rollouts take 8 to 16 weeks. You see working results on your own examples in the first week.
Which LLM should we use: GPT, Claude, Gemini or open source?
It depends on the task, your data rules and your budget, so we test rather than guess. Our free model cost check runs your real examples through several models and compares quality, speed and cost per request. Many products end up using two models: a strong one for hard tasks and a cheaper one for simple ones.
What is the difference between generative AI integration and building a chatbot?
Generative AI integration adds LLM features inside the software you already have, like summaries, extraction, smart search or a copilot. A chatbot is one type of integration: a conversation window, usually for customers. If a chatbot is all you need, our AI chatbot development page covers it in detail.
What AI integration strategy should we start with?
Start with one feature that is frequent, easy to measure and low-risk if the AI is sometimes wrong, such as summarizing tickets or extracting fields from documents. Prove it with real users, measure the time saved, then reuse the same gateway and data connections for the next feature.
Do we need RAG, fine-tuning or just prompts?
Most features only need good prompts plus RAG and LLM integration, which lets the model answer from your own documents and database. Fine-tuning helps when you need a very specific style or format at high volume, or want a smaller, cheaper model. We start simple and only add fine-tuning when tests show it pays off.
Can you connect an LLM to our database, CRM or ERP?
Yes. Through LLM database integration and tool calling, the model can look up records, run safe read-only queries and, with approval, update your CRM or ERP. It only touches the data and actions each user is allowed to use.
Is our data safe when we use an LLM API?
Yes, when it's set up properly. We use enterprise API tiers that don't train on your data, redact personal data before it leaves your system, keep logs in your region and add audit trails. For the strictest needs we deploy open-source models in your own cloud so nothing leaves it.
How do you keep LLM costs under control?
We route simple jobs to cheaper models, cache repeated answers, trim prompts, set token budgets per user and alert on spikes. Cost per request is on your dashboard from day one, so there are no surprise bills.
Will we be locked into one AI provider?
No. We build every integration behind a gateway, so switching from GPT to Claude, Gemini or an open-source model is a configuration change plus a test run, not a rebuild.
Do you offer enterprise AI integration?
Yes. LLM integration for enterprise adds single sign-on, role-based access, audit logs, data residency, private or self-hosted models and one gateway that every team can use. Our builds follow GDPR principles and are HIPAA-ready and SOC 2-ready when the right hosting and agreements are in place.
How do you stop the model from giving wrong or unsafe answers?
We ground answers in your data, validate every output against a schema, block prompt injection, and test each release on 100+ real examples before it ships. After launch we track failures and fix the prompt, data or model behind each one.
Who owns the integration, and do you support it after launch?
You own the code, prompts, API keys and cloud accounts. Every launch includes 2 weeks of free post-launch support to fix issues and tune on real traffic. After that you can choose a monthly Care Plan or run it in-house with our handover docs and training.
Let's build together
Ready to Put LLMs to Work in Your Product?
Book a free 30-minute call. We'll look at your product, your data and your budget, then show you the fastest safe path from idea to a live AI feature, with the right model and a cost you can predict. Every launch comes with 2 weeks of free support.