LLM integration services for products that need AI features that hold up

What LLM integration involves

Most product teams start with a prompt and an API key and get something impressive in a day. The problems start when it meets real data, real users and a finance team: answers that are confidently wrong, latency of several seconds, bills that scale with usage, and no way to tell whether a prompt change made things better or worse. LLM integration done properly addresses each of those.

• Retrieval-augmented generation (RAG): grounding answers in your documents and data with citations.
• Prompt and context management: versioned prompts, templates, few-shot examples and token budgets.
• Evaluation: a set of real cases scored automatically on every change.
• Cost and latency control: caching, model routing, streaming, batch processing.
• Safety: input and output filtering, prompt-injection defences, PII handling.
• Observability: traces, cost per request, quality metrics and user feedback loops.

Choosing a model

There is no single best model. We choose per feature based on quality on your evaluation set, latency, cost per million tokens, data residency and vendor risk, and we build so the choice can change later.

Common integrations we build

How a project runs

1. Feature definition. What the user asks, what a good answer looks like, and what must never happen.
2. Data and retrieval. Connect the sources, decide chunking and indexing, respect permissions.
3. Prototype on real cases. Two weeks to a working feature you can try on your own data.
4. Evaluation set. Fifty to a few hundred real cases with expected outcomes, scored automatically.
5. Hardening. Guardrails, caching, streaming, rate limits, monitoring and cost caps.
6. Launch and iterate. Ship behind a flag, watch quality and cost, expand.

Cost of running the feature

Model fees scale with tokens. A Q&A feature over documents typically costs a fraction of a cent per question on a mid-tier model, so ten thousand questions a month is tens of dollars, not thousands. We design for cost with caching of repeated questions, smaller models for routing and classification, context trimming and hard monthly caps, and we show cost per request on a dashboard from day one.

Data privacy and compliance

• Requests can go through your own cloud account; open models can run entirely in your VPC.
• PII is masked before it reaches a model where the use case allows.
• Prompts and outputs are logged under your control, with retention you set.
• GDPR data processing agreement and, in healthcare, HIPAA safeguards and a BAA.

Innovation Insight has integrated language models into SaaS products, mobile apps and internal tools since 2023. The AI team works alongside our web, mobile and cloud engineers, so the feature is built into your product properly rather than bolted on.

A worked example: Q&A over contracts

A property management company holds 4,000 lease agreements as PDFs. Staff spend hours finding clauses. We build a feature inside their existing portal: upload and index documents, ask questions in plain language, get answers with citations to the exact page, and export a summary. Retrieval respects the portfolio permissions each user already has.

Running cost at a few thousand questions a month is under $100 in model fees. Accuracy on the evaluation set is tracked from the first week and reported after every change.

Retrieval done properly

Most disappointing LLM features fail at retrieval, not generation. The model can only answer from what it is shown. We tune chunk sizes to the document type, add metadata filters so a question about one property does not pull answers from another, use hybrid keyword and vector search, and re-rank results before they reach the model. Citations are mandatory, so users can verify and so the evaluation set can score correctness automatically.

Prompt management and versioning

Prompts are code. They live in version control, have tests, and change through pull requests with evaluation results attached. Product managers can propose changes without deploying, and every production request records which prompt version produced it. This is what makes 'the answers got worse last Tuesday' a solvable problem.

Fine-tuning and when it is worth it

Most products do not need fine-tuning; retrieval and good prompts solve the problem more cheaply and remain easy to update. Fine-tuning earns its place for narrow, high-volume tasks with a consistent output format, where a smaller fine-tuned model can replace a large general one and cut cost and latency. We recommend it only after an evaluation set shows the gap and the volume justifies it.

Working with your team

Your engineers can own the feature after launch. We document the pipeline, hand over the evaluation harness and dashboards, and run a workshop on how to change prompts and models safely. Or keep a monthly retainer for monitoring and improvement. Either way, the code, prompts and data are yours.

Latency and user experience

A feature that takes eight seconds to answer will not be used. We stream responses so users see the first words within a second, cache repeated questions, keep context windows tight, and route simple requests to smaller, faster models. Where a task can run in the background, such as summarising a document on upload, we do it ahead of time so the interface feels instant.

Mobile and web integration

LLM features live inside the product, not in a separate chat window. Our web and mobile teams build the interface patterns that make them useful: inline suggestions, citation previews, feedback controls, and graceful fallbacks when a model is slow or unavailable. The same backend serves web and mobile so behaviour is consistent.

What to bring to the first call

Bring examples of the questions or tasks users would give the feature, a sample of the data it would need, and any constraints on where data can be processed. Within one business day we will reply with a feasibility view, a rough range and, if it makes sense, a two-week prototype plan on your real data. Innovation Insight also offers dedicated AI and ML engineers on a monthly basis if your team prefers to build in-house with help.