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Bring LLMs Into Your Existing Stack

We integrate large language models into the products and workflows you already run — with the prompt engineering, evaluation and guardrails that make it production-ready.

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An API call isn't a strategy

Calling a model is easy; making it reliable in production is the hard part. We integrate LLMs into your existing applications with proper prompt design, output validation, cost controls and fallback handling — so the AI feature behaves predictably under real usage, not just in a demo.

What we deliver

Provider-agnostic integration

We choose and integrate the model provider that fits your cost, latency and compliance needs.

Prompt engineering & versioning

Structured, tested prompts with version control, not one-off text strings.

Output validation

Schema checks and guardrails so malformed or unsafe outputs never reach users.

Cost & latency controls

Caching, model routing and token budgets to keep AI features affordable at scale.

Evaluation pipelines

Ongoing testing against real examples to catch regressions before users do.

How we integrate

  1. Step 1

    Define the use case

    We scope exactly what the LLM feature needs to do and how success is measured.

  2. Step 2

    Select the model

    We evaluate providers and models against your cost, latency and data needs.

  3. Step 3

    Engineer & integrate

    We build the prompts, API integration and validation layer into your app.

  4. Step 4

    Evaluate rigorously

    We test against real-world examples and edge cases before launch.

  5. Step 5

    Ship & monitor

    We launch with usage and cost monitoring, then iterate based on real data.

Frequently asked questions

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