AI Agents & Automation

LLM Integration

Add large language models to your product with retrieval over your own data, evaluation on real questions, guardrails, cost controls and a clear fallback when the model is unsure.

Discuss your projectDiscuss your LLM feature

LLM features built for production

Calling a model API is the easy part. Making its answers accurate, safe and affordable inside your product is the real work. We integrate models such as OpenAI's GPT models, Claude, Gemini, Llama or Mistral into your software, from a single feature to retrieval-augmented generation (RAG) over your knowledge base. For product and engineering teams, we handle model choice, retrieval, prompts, evaluation, guardrails and cost control, and design how users see sources and flag wrong answers.

AI-assisted, expert-led LLM engineering

How AI assists

  • Coding agents draft integration code, retrieval pipelines and test harnesses for engineers to review.
  • AI proposes test questions from your documents; your experts approve the ones that define quality.
  • AI compares outputs across candidate models and prompts, and flags likely regressions for review.
  • AI summarizes production logs and user feedback to surface recurring failure patterns.

What our experts own

  • Model and provider choice, based on evaluation results, cost and your data rules
  • What the model can access: data sources, user permissions and retention settings
  • The quality bar answers must meet before any release
  • Fallback behavior when retrieval fails, a provider is down or the model is unsure

How one grounded answer is produced

Illustrative retrieval-augmented request; source rules, checks and fallback wording are designed for your product.

  1. Question asked

    A signed-in user asks a question; their identity and role travel with the request.

  2. Permitted retrieval

    Search ranks passages from your indexed sources by relevance to the question.

    Checkpoint: Only passages this user may see

  3. Prompt with sources

    The question, retrieved passages and versioned instructions on format and refusals are combined into one prompt.

  4. Model response

    The chosen model returns a structured answer that cites the passages it relied on.

  5. Grounding check

    Automated checks confirm each citation points to a retrieved passage and flag claims that go beyond them.

    Checkpoint: Unsupported answers are held back

  6. Answer or fallback

    The user sees the answer with source links, or a plain message that nothing permitted covers it.

When something fails: If retrieval finds nothing, checks fail or the provider is down, the feature says so instead of guessing, and the case is logged for review.

Who asks for LLM features

You want a model answering from your own data, but a quick prototype states things your documents don't support, ignores who may see what, and has running costs nobody can predict.

  • Product teams adding search, summaries or drafting to an existing SaaS app
  • Organizations whose staff struggle to find answers across internal policies and manuals
  • Engineering teams taking an LLM prototype to production for the first time

What you receive

What an LLM feature needs in production

  • Model selection and integration

    Connection to OpenAI, Claude, Gemini or open-weight models from your backend, with rate limiting, retries, provider fallbacks and usage tracking.

  • RAG over your knowledge base

    Retrieval from your documents and data through a vector database, with source references and access rules, so answers respect each user's permissions.

  • Prompts and structured outputs

    Versioned prompts, few-shot examples and structured outputs your code can validate, instead of free text it has to guess at.

  • Evaluation suite

    Test sets built from your real questions, scored for accuracy, grounding in sources and format, and rerun before every prompt, model or data change.

  • Guardrails and fallbacks

    Input filtering, output moderation, prompt-injection defenses and a defined fallback when the model is unsure or a provider is unavailable.

  • Fine-tuning where it helps

    Fine-tuning on your data for domain language, tone or formats, used when evaluation shows prompting and retrieval aren't enough.

Outside an LLM integration

  • Multi-step work that changes records or triggers actions in other systems is Automation Agents; an LLM feature here answers, drafts or extracts inside your product.
  • A customer-service assistant on your website or WhatsApp, with handoff to support staff, is covered by AI Chatbots.
  • We don't write or correct your source documents: when retrieval surfaces outdated or conflicting content, we flag it for its owners to fix.
  • Running open-weight models on your own servers or cloud account is scoped separately as Private AI Infrastructure, often alongside the integration.

Typical LLM requests

Typical scenarios we scope, not client case studies.

  • Answers from internal policy documents

    Staff search a shared drive and often find outdated policy copies. We would index only the current versions, apply each team's access rules, cite the passage behind every answer, and decline when no permitted source covers the question.

  • Summaries inside a multi-tenant SaaS product

    A SaaS app wants a button that summarizes an account's recent activity. We would filter retrieval by tenant and role before the prompt is built, and add test cases that try to pull another customer's data.

  • A prototype calling the model from the browser

    A working prototype sends prompts straight from the browser with a shared API key. We would move calls to your backend, add per-user limits, logging and versioned prompts, and build an evaluation set before the first release.

How we deliver an LLM integration

  1. 01

    Use case and feasibility

    We try the use case on your real data and questions, compare candidate models, and estimate running costs before you commit to a full build.

  2. 02

    Architecture and data rules

    Integration pattern, retrieval design, access rules, provider terms and fallback behavior are agreed with your team and documented.

  3. 03

    Build and evaluate

    Engineers build the integration and the user-facing feature, and QA scores it against the evaluation set until it meets the quality bar you approved.

  4. 04

    Release and monitor

    A controlled rollout with monitoring, cost limits and feedback capture, then continued evaluation as models, prompts and data change.

Two ways to work with AI tools

Choose where AI coding agents may process your code while we build. The engineering standard is the same either way.

Not sure? We'll recommend one during scoping. Compare AI delivery options

How design, QA and operations support your LLM feature

  • Design for trust and correction

    Designers plan how answers, sources and uncertainty appear, how users edit or flag a wrong answer, and how your team reviews those flags, so the feature is honest about its limits.

  • QA beyond the happy path

    QA maintains the evaluation sets, tests answer quality, data permissions and prompt-injection exposure, and reruns regression checks after model, prompt or data changes.

  • Operations and cost control

    DevOps runs the integration with logging, usage and cost dashboards, caching and model routing, and open-weight models on private infrastructure where data rules require it.

  • Model and prompt updates

    Providers change and retire models. We test replacements against your evaluation set before switching, and keep prompts, retrieval and costs tuned as usage grows.

FAQ

Frequently asked questions

What is RAG, and do we need it?

Retrieval-augmented generation (RAG) lets a model answer from your own documents and data, not only from its general training. You need it when answers must reflect private, specialized or frequently changing information. We add source references so answers can be checked, and access rules so users only retrieve content they're permitted to see.

Should we fine-tune a model or use RAG?

Most features should start with good prompts and retrieval, because they're quicker to change and easier to audit. Fine-tuning helps when you need a consistent format, tone or domain vocabulary that prompting can't hold, and sometimes lets a smaller, cheaper model do the job. We decide using evaluation results on your data, not by default.

Can we use a private or open-source model?

Yes. Open-weight models such as Llama or Mistral can run on infrastructure you control, or we can use hosted providers such as OpenAI or Claude under agreed account and data-retention settings. Our own development work follows the package you choose: Private / Local AI Engineering or Claude Code / OpenAI Codex Engineering.

What does an LLM feature cost to build and run?

Build cost depends on scope: data sources, integrations, interface work and evaluation depth. Running cost depends on usage and model choice. We estimate both during feasibility, then manage running cost with model routing, caching, token limits and usage dashboards you can see.

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Put an LLM feature into production

Bring a use case and sample data. We'll test feasibility, compare models, and outline the evaluation, guardrails and running costs before you commit.

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