Service

LLM Integration

Connect AI to your existing systems — APIs, local inference, streaming, and production deployment patterns.

What We Build

Connecting LLMs to your existing business systems is the hard part. The model itself is a commodity — the engineering that makes it useful in your specific context is the real work. API selection, streaming architecture, cost control, error handling, and graceful degradation.

API & Local Inference

Cloud APIs (OpenAI, Anthropic, Azure) for high-capability tasks. Ollama for local inference where data privacy or latency requires it. Often both in the same system.

Streaming Responses

Server-sent events for real-time streaming. Users see responses generate token by token — no waiting 10 seconds for a complete response.

Cost Management

Token budgeting, model routing (expensive models for hard tasks, cheap models for simple ones), caching of repeated queries, and usage dashboards.

System Integration

REST APIs, database connections, CRM integration, file system access. LLMs that talk to your real systems, not just respond to chat prompts.

How It Works

1. Integration Assessment

Map your existing systems, APIs, and data flows. Identify where LLM capabilities add real value — summarization, classification, extraction, generation, or decision support.

2. Model Selection

Choose the right model for each task — not every problem needs GPT-4. Local models via Ollama for data-sensitive operations, cloud APIs for complex reasoning, small models for classification.

3. Build Integration Layer

API abstraction layer with provider fallbacks, streaming support, token tracking, and error handling. Containerized with Docker for consistent deployment.

4. Production Hardening

Rate limiting, cost caps, graceful degradation when APIs are unavailable, response caching, and monitoring dashboards. Built to run reliably at scale.

Built & Deployed

7 Live Applications with LLM Integration

Production applications spanning CRM, security tools, compliance systems, and content platforms — all with integrated LLM capabilities via both cloud APIs and local Ollama inference.

Hybrid Cloud + Local Architecture

Systems that route between cloud APIs and local Ollama transparently. Sensitive data stays local, complex reasoning goes to cloud — users never notice the difference.

Firewall Rule Analysis Engine

LLM-powered analysis of firewall configurations across 33 vendors. Parses complex rule sets, identifies risks, and generates remediation recommendations.

Frequently Asked Questions

Which LLM providers do you work with?

OpenAI (GPT-4), Anthropic (Claude), Azure OpenAI, and Ollama for local inference. We build provider-agnostic abstraction layers so you can switch models without changing application code.

How do you control LLM costs?

Token budgeting per request, model routing (expensive models for complex tasks, cheap models for simple ones), response caching for repeated queries, and usage dashboards. Most integrations reduce costs 40–60% through smart routing.

Can I keep my data on-premises?

Yes. Ollama runs locally on your infrastructure — no data leaves your network. For hybrid setups, sensitive operations use local models while non-sensitive tasks use cloud APIs.

How long does an LLM integration take?

2–3 weeks for a single integration point (e.g., adding AI summarization to your CRM). 4–6 weeks for multi-system integrations with streaming, cost management, and monitoring.

Deep Dive

LLM Integration for Business Systems: A Practical Guide Without the Hype →

Ready to Build?

Production systems, not demos. Tell us what you need.

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