It depends on the task. Lightrains compares candidate models on your own evaluation set and picks on accuracy, latency, cost and where the data may go. The integration is built so the model can be changed later without rewriting the feature.
LLM Integration Services
LLM integration for existing products and internal tools: model selection, prompt and output design, evaluation, guardrails and cost control, by senior engineers.
LLM integration means adding large language model features to software you already run: a product, an internal tool or a back-office workflow. The model is the easy part. The work is making its output reliable enough to put in front of customers, and keeping cost and data exposure under control.
Lightrains integrates LLMs into existing codebases, working in your repository and tools. Every feature ships with an evaluation set, so you can see how well it works before launch and after every change.
What Lightrains builds
Features inside your product
Summaries, drafting, rewriting and natural-language search added to the screens your users already work in, behind your existing auth and billing.
Using AI and ML in a startup MVP →Extraction into structured data
Reading invoices, forms, emails and reports and returning validated fields your systems can store, with low-confidence results sent for review.
Prompt controls for consistent output →Question answering over your content
Answers grounded in your documents and data with citations, using retrieval rather than retraining.
RAG development services →Internal tools and assistants
Assistants for support, sales and operations staff that work with your CRM and ticketing data and leave the final decision to a person.
AI agent development →Choosing the right approach
Most LLM projects need less than teams expect. Start with the simplest approach that meets the requirement and add complexity only when the evaluation set shows it is needed.
| If you need | Start with |
|---|---|
| The model needs better instructions or a fixed output format | Prompt and output design |
| Answers must come from your own documents and stay current | Retrieval-augmented generation (RAG) |
| A stable domain, a strict style, or very low latency | Fine-tuning, often alongside RAG |
| The model must take actions in other systems | An agent with MCP tools |
| Data cannot leave your devices or network | An open-weight model, self-hosted or on the edge |
How LLM features are made dependable
These are part of every integration. The linked articles explain how the Lightrains team implements them.
Model selection and routing
Closed frontier models or open-weight models, chosen per task on accuracy, latency, cost and data residency. A routing layer lets you switch models without rewriting the feature.
Prompts and structured output
Prompts are versioned like code, and outputs are validated against a schema so downstream systems never receive malformed data.
Evaluation sets
A set of real inputs with expected results is built before launch and run on every prompt or model change, so quality is measured rather than eyeballed.
Guardrails
Input validation, sensitive-data detection and output checks around every model call, with a safe fallback when a check fails.
Cost and latency
Caching, smaller models for simple steps, and quantised or distilled models where they hold up on the evaluation set. Spend is reported per feature.
Designing for wrong answers
Interfaces that show sources and confidence and make correction easy, because every LLM is sometimes wrong.
In production
Report-grounded assistant for a genomic testing lab
An LLM assistant that answers partner, clinician and patient questions from the user's own genetic report and the lab's approved content, and routes anything needing clinical judgement to a specialist. It let the lab support more accounts without adding specialist capacity at the same rate. Read the case study.
How a project runs
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Step 1
Scope and evaluation set
Pick one feature with a clear success measure and collect real examples. The evaluation set is the definition of done.
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Step 2
Prototype on your data
Compare candidate models and prompts against the evaluation set, with cost and latency for each, and choose with numbers in front of you.
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Step 3
Integrate and launch
Build the feature into your product with guardrails, monitoring and per-feature cost reporting, then release to a subset of users first.
The work is done by Lightrains engineers with 5+ years of production experience, in overlapping IST and US/EU hours. For a longer-term team, see offshore AI and software teams.
Frequently Asked Questions
Not if the integration is set up correctly. Enterprise API tiers from the major providers do not train on customer data by default, and open-weight models can run entirely in your environment. Lightrains documents where data goes for your security review.
Yes. Open-weight models can run in your cloud account, on-premises or on edge devices. This usually costs some accuracy compared with the largest hosted models, and the evaluation set shows whether that trade-off is acceptable for the task.
By grounding answers in your data with retrieval, validating outputs, and designing the interface so users can see sources and correct mistakes. No technique removes errors completely, so high-impact actions keep a person in the loop.
Each feature has a budget and cost reporting. Caching, routing simple requests to smaller models and trimming context keep spend down. AI budgets and cost control covers the techniques.
You do. All IP belongs to you, access to your systems is VPN-only under NDA, and payments are tied to milestones.
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