LLM Integration Services
LLM Integration and Development Services
Connecting a model API is easy. Making it accurate, safe, fast and affordable inside a real product is the hard part. We integrate large language models into your software and take care of retrieval, evaluation, guardrails and cost.
- OpenAI, Anthropic Claude, Google Gemini and open-source
- RAG grounded in your documents and databases
- Prompt, retrieval and model evaluation with real test sets
- Caching, routing and monitoring to control cost and latency
Where it pays off.
In-product assistants
Contextual help, copilots and natural-language interfaces inside your web or mobile app.
Knowledge search & Q&A
Answer questions from policies, manuals, contracts and support history, with citations to the source.
Content and data generation
Drafting, summarising, translating and structuring text in the tone and format your product needs.
Classification & extraction
Tag, route and extract structured fields from messy text at a scale people cannot match.
What we deliver.
Every engagement is fixed-scope, fixed-timeline and fully transparent.
Retrieval-augmented generation
Chunking, embeddings, hybrid search and re-ranking tuned to your content, with permission-aware retrieval.
Prompt & output engineering
Versioned prompts, structured outputs and schema validation so responses are dependable in code.
Fine-tuning & model selection
We benchmark models on your task and fine-tune only when prompting and retrieval are not enough.
Guardrails & LLMOps
Input and output filtering, PII handling, rate limits, tracing, evals and cost dashboards.
Why teams work with us.
Better answers, grounded in your data
Retrieval reduces made-up answers and lets responses cite the underlying source.
Predictable cost
Model routing, caching and prompt tuning keep spend in line as usage grows.
Safer behaviour
Guardrails and access control keep the model within what it should say and see.
Provider flexibility
An abstraction layer lets you change models without rewriting your product.
How we work.
1. Define the task
Pin down inputs, expected outputs and what “good” means, using real examples.
2. Benchmark models
Test candidate models and approaches on your examples for quality, speed and price.
3. Build the pipeline
Implement retrieval, prompts, validation and the API or UI layer around them.
4. Evaluate & guard
Add automated evals, red-team the weak spots and put guardrails in place.
5. Ship & observe
Deploy with tracing and cost tracking, then iterate using real usage.
Our stack.
Tools we use every day to ship faster and safer.
What you get.
Every engagement ships with these, minimum.
- LLM features integrated into your product
- Retrieval pipeline and indexed knowledge base
- Evaluation set with benchmark results
- Monitoring for quality, latency and spend
Frequently asked questions.
What is LLM integration?
LLM integration means connecting large language models such as GPT, Claude or Gemini to your software so it can understand and generate language: answering questions, summarising, extracting data or taking actions. A good integration also covers retrieval of your data, evaluation, security and cost control.
What is RAG and do we need it?
Retrieval-augmented generation fetches relevant passages from your own data and gives them to the model when it answers. You need it when answers must reflect your private or frequently changing information, and it is usually cheaper and easier to maintain than fine-tuning.
Should we fine-tune a model?
Often not at first. Prompting and retrieval solve most problems. Fine-tuning can help with consistent style, specialised formats or reducing cost at high volume. We benchmark before recommending it.
How do you keep LLM costs under control?
By choosing the smallest model that meets quality, caching repeated requests, trimming prompts, routing easy tasks to cheaper models, and tracking spend per feature and per customer.
Can you work with private or on-premise models?
Yes. Depending on your data and compliance needs we can use enterprise cloud offerings with data-protection terms, or deploy open-source models in your own environment.
Want to add LLM capabilities to your product?
Tell us the feature you have in mind. We will recommend an approach, benchmark it on your data and scope the integration.
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