LLM Integration Services for the Software You Already Run
Add AI to the products you already run. Our LLM integration services bring large language model features into your existing web app, platform or internal tool, so you don't have to start again. We pick the right model for each job, connect it to your data safely, test the quality of its answers, and keep costs and response times under control.
AI features you can add to existing software
| Feature | What it does for your users |
|---|---|
| Smart search | Finds answers by meaning, not just keywords, across documents and records |
| Summaries | Turns long threads, reports or records into short, readable overviews |
| Drafting | Writes first drafts of replies, descriptions or documents for people to edit |
| Data extraction | Pulls structured fields out of emails, PDFs and forms |
| Classification and tagging | Sorts content, tickets or feedback automatically |
| Natural-language queries | Lets people ask questions of their data in plain words |
| In-app assistant | A helper inside your product that knows the user's context |
| Translation | Makes content available in more languages |
What our LLM integration services cover
Use-case selection
Not every feature benefits from AI. We look at where your users lose time or get stuck, then pick features where a language model clearly improves the experience and can be measured.
Model selection
Models differ in quality, speed, cost, language support and data-handling terms. We choose per feature and design the integration so you can switch models later without rewriting your product.
Grounding in your data (RAG)
Retrieval-augmented generation (RAG) lets a model answer using your own documents and records, retrieved at the moment of the question. We prepare your content, build the search index and connect it, so answers are relevant and can cite their sources.
Integration into your product
We build the AI features into your existing front end and back end, through APIs that fit your architecture. For products on our stack, that means TypeScript, Node.js and PostgreSQL alongside your current code.
Prompt and output design
We write and version the instructions behind each feature, define the output format your code expects, and validate every response before it's used.
Evaluation
We build test sets of real examples and measure each feature's quality before launch and after every change, so improvements are proven rather than guessed.
Cost, speed and reliability
We use caching, the right-sized model for each task, sensible limits and fallbacks, so AI features stay fast, affordable and available.
Built with security and privacy in mind
LLM features introduce new risks. The OWASP Top 10 for LLM Applications lists the main ones, including prompt injection, sensitive-information disclosure and unbounded resource use. Our integrations address them by design:
- Least data: models receive only the data a feature needs.
- Access rules respected: users only see AI answers drawn from content they're allowed to see.
- Input and output checks: untrusted content is treated carefully and outputs are validated.
- Usage limits: rate and spending caps prevent runaway costs.
- Logging: requests and responses are logged for review, in line with your data policies.
How an integration project runs
Discovery
Review the product, users and data, and pick the first features.
Prototype
Test the feature on real examples and agree on quality targets.
Build
Integrate it into your product, with retrieval, guardrails and logging.
Evaluate
Run test sets, fix weak spots and check costs.
Release
Roll out gradually, often to a small group first.
Improve
Monitor usage, quality and cost, and refine.
Is this the right service for you?
| If you need… | Start with |
|---|---|
| AI features added inside software you already run | LLM integration services (this page) |
| A stand-alone assistant for customers or staff | AI chatbots & assistants |
| A model trained on your own data for prediction | Custom AI & ML solutions |
| A new SaaS product with AI at its core | SaaS & web app development |
Frequently asked questions
They add large language model capabilities, such as search, summarising, drafting and extraction, to existing software. The work covers choosing models, connecting your data, building the features, and testing and monitoring their quality, cost and safety.
No. In most cases, AI features are added through new APIs and interface components alongside your existing code.
That depends on the model provider and the terms you choose. We review each provider's data-handling terms with you and pick options that match your policies.
Retrieval-augmented generation means the system first finds relevant passages in your own content, then gives them to the model to answer from. It makes answers more accurate, current and traceable.
Yes. We design integrations with a layer between your product and the model, so changing providers is a configuration change rather than a rewrite.
Timelines and pricing depend on scope, so contact us for an estimate. Ongoing model usage costs depend on traffic, and we estimate them during the prototype.
Let's add AI where it helps
Tell us about your product and where your users lose time. We'll come back with a clear next step.