We already built a prototype — can you take it to production?

Yes, this is one of the most common engagements — hardening an existing proof-of-concept for real production traffic and cost constraints.

How do you control AI costs at scale?

Through model routing (using smaller/cheaper models where accuracy allows), caching repeated queries, and prompt optimization to reduce token usage without sacrificing quality.

Services/AI & Agentic Services/LLM Integrations

🔗 AI & Agentic Services

LLM Integrations

Embed AI into the tools you already use.

LLM Integrations is the engineering layer that gets a language model working reliably inside your actual product or internal tooling — not a standalone chatbot, but AI embedded into an existing workflow. We handle prompt engineering, model selection, cost optimization, latency, and fallback handling so the integration holds up in production.

Discuss Your Integration →

Sound familiar?

!You know you want 'AI in the product' but aren't sure where to start technically

!A proof-of-concept works but breaks down or gets expensive at real usage volume

!Unsure which model or provider fits the use case and budget

!No structured approach to prompt engineering, versioning, or evaluation

What's Included

✓

Model Selection & Cost Analysis

The right model and provider chosen for your use case, latency, and budget.

✓

API Integration & Backend Engineering

Production-grade integration into your existing product or internal tools.

✓

Prompt Engineering & Versioning

Structured, tested, and version-controlled prompts — not ad hoc trial and error.

✓

Cost & Latency Optimization

Caching, batching, and model-routing strategies to control cost at scale.

✓

Fallback & Reliability Handling

Graceful handling of API failures, rate limits, and model errors in production.

✓

Evaluation Framework

A structured way to test and monitor output quality before and after launch.

Our Process

Use Case & Model Fit

We define the exact use case and evaluate which model fits the accuracy, latency, and cost requirements.

Prototype

We build a working prototype against real inputs, not synthetic test cases.

Production Engineering

We harden the integration — error handling, fallbacks, cost controls, and monitoring.

Evaluate & Launch

We test output quality systematically before rolling out to real users.

Monitor & Optimize

We track cost, latency, and quality post-launch and continue optimizing.

Tools & Technology We Use

OpenAI, Anthropic, Google & open-weight modelsStreaming & function-calling APIsRedis / caching layersPython / TypeScript / Node.jsObservability (LangSmith, custom eval pipelines)

🔗

AI that works reliably at scale

Production-grade integrations, not fragile proof-of-concepts

Frequently Asked Questions

It depends on the use case — we evaluate accuracy, latency, and cost across providers (OpenAI, Anthropic, Google, and open-weight models) rather than defaulting to one vendor.

Related Services

Ready to talk about llm integrations?

Book a free consultation and we'll show you exactly how this applies to your business.