Large Language Models are transformative, but only when they are integrated into the systems your team already uses. We connect LLMs to your applications, databases, and workflows via API or local deployment, turning general-purpose intelligence into a purpose-built tool for your business.
The integration code, prompt engineering, and evaluation discipline are the same. What differs is where inference runs, who sees your data, and how the costs behave. Explore both paths.
Connect to providers like OpenAI, Anthropic (Claude), Google (Gemini), or Mistral via their APIs. Best for teams that want rapid deployment, no hardware overhead, and access to frontier models.
We handle prompt engineering, structured output parsing, token management, rate limiting, and fallback logic. We build integrations that go far beyond a chat window: LLMs that read your database, classify inbound tickets, draft reports from structured data, extract information from documents, and power voice systems.
Cost behaves like a utility bill: it scales with usage, and we make it visible per feature so you always know which workload spends what.
For organizations that need data sovereignty, predictable costs, or offline capability, we deploy open-source models on your own hardware or VPS. Inference engines like vLLM, llama.cpp, and TGI provide OpenAI-compatible APIs so your application code stays the same.
We handle GPU selection, model quantization (AWQ, GGUF, FP8), VRAM planning, continuous batching configuration, and production monitoring (TTFT, tokens/second, queue depth). Your data never leaves your infrastructure.
Cost behaves like rent: fixed and predictable regardless of volume, which flips the economics in favor of self-hosting at sustained high usage.
Most mature deployments end up hybrid: sensitive or high-volume routine work runs on local models, while complex reasoning routes to a frontier cloud model.
Because both paths expose OpenAI-compatible APIs, routing is a policy layer, not a rewrite: rules based on data classification, task complexity, latency needs, and budget decide per request where inference happens.
This is how you get frontier capability where it matters and sovereignty plus cost control everywhere else.
Answer three questions about the workload you have in mind. The recommendation updates live.
System prompts engineered and versioned like code, structured outputs validated against schemas, and regression tests so a prompt change never silently breaks production.
LLM capability embedded in the tools your team already uses: your ERP, your CRM, your internal apps, your ticketing. No new tab to remember.
Cost-aware routing between models, automatic fallback when a provider degrades, and caching so repeated questions do not bill twice.
A test suite of real cases scored on every change. You see accuracy numbers, not vibes, before anything ships.
Per-feature token and spend dashboards. When finance asks what the AI costs, the answer is a chart, not a shrug.
Full private inference stack: GPU sizing, quantization, vLLM or llama.cpp serving, OpenAI-compatible gateway, and monitoring.
Extract structured data from invoices, contracts, emails, and PDFs. Parse, classify, and route documents automatically.
Natural-language interfaces to your company knowledge, SOPs, product catalogs, and internal documentation.
LLM-powered developer tools: SQL generation from natural language, code review assistance, documentation generation.
Automated report narratives, product descriptions, email drafts, and marketing copy generated from your data.
Inbound ticket classification, sentiment analysis, intent detection, and intelligent routing to the right team or system.
LLMs powering real-time voice conversations with customers via phone systems. GPT-Realtime, ElevenLabs, Vapi integration.
On properly configured enterprise API tiers, no; we set the data-handling flags and document them. With self-hosted inference the question disappears entirely: nothing leaves your infrastructure.
The honest answer is several. We benchmark candidates against your actual tasks and route each workload to the cheapest model that clears your quality bar, keeping a frontier model for the hard cases.
It depends on the model class: capable small models run on a single consumer GPU, while larger models need data-center cards or multi-GPU setups. We size it from your latency and volume targets, not from hype.
An evaluation harness of real cases runs on every prompt or model change, and production sampling watches live quality. Drift shows up in a dashboard before it shows up in complaints.
Tell us the workflow you want to accelerate. We will recommend the model, the path, and the integration plan.