AI inside the software you already use
LLM Integration Services
Add AI to the tools your team already works in. We connect a large language model (LLM), the kind of AI behind ChatGPT, to your helpdesk, inbox, or records so it can draft replies, answer questions from your documents, and sort incoming requests. We handle the security, the cost limits, and what happens when it's wrong.
Tell us the tool and the task. An engineer replies within one business day.
Which one do I need?
- Your process has too many manual steps across several tools. See AI Workflow Automation.
- You want AI to handle a whole task and decide what to do next. See AI Agent Development.
- You want AI features inside software you already run. You're here: LLM Integration.
- You're typing data out of invoices, forms, or PDFs. See Document Processing.
- Your main concern is where the data lives and who can see it. See Private AI Agents.
Not sure? Tell us the problem and we'll point you to the right one, or tell you none of them fits.
Calling the model is easy. Running it safely is the work.
Your helpdesk, inbox, and records already hold the answers. But they can't draft a reply, summarize a long thread, or sort a request on their own. Connecting an AI model looks easy in a demo. In real use, the hard questions show up fast: What data can it see? What does it cost at our volume? What happens when it's wrong, or when someone emails it instructions it shouldn't follow? That's why many small teams stop at a pilot.
Three guardrails on every integration
We add AI only where it changes real work, one task at a time. Each integration gets three guardrails. Limits on what it can see and do, so it reads only the data that task needs. Cost controls: usage caps, reuse of repeat answers, and alerts before a bill grows. And a fallback: if the model is down or unsure, the task goes back to your normal process. Your team keeps the screens they already know.
One tool, one task, then the next
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Pick one tool and one task. We find where AI would save the most time and write down what it may and may not touch, plus the expected monthly cost at your volume.
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We build it into that tool. It answers from your own data, with usage caps and alerts. Nothing goes to a customer without the review step you choose.
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We keep it working. We track answer quality and cost every month, update it as AI models change, and move to the next task when you're ready.
AI you can trace and a bill you can predict
Your team works in the same tools, now with drafts, answers, and sorting built in. You know what it costs each month before the bill arrives. And every AI answer can be traced back to the data it came from, so nobody has to take it on faith.
What IT teams check first
- Works inside the tools your team already uses
- Each integration sees only the data its task needs
- Usage caps and alerts, so the bill stays predictable
- A fallback to your normal process when the AI is unsure or down
- Kept up to date as AI models change, by a CISSP-led security practice
Questions, answered
What's an LLM integration, as opposed to a chatbot?
An LLM (large language model) is the kind of AI behind tools like ChatGPT. A chatbot is a separate window people visit. An integration lives inside your existing software: a support inbox that drafts replies, a document system that answers questions about its contents, an intake form that sorts requests. The work just happens where your team already works.
How much does an LLM integration cost?
Two parts: the build and the running costs. Projects start at $4,999, professional services are $250 an hour, and nonprofits and first-time clients get 20% off automatically. Running costs are usage-based and capped with limits and caching you control. We size both against your actual volume so the monthly bill is a number you've seen before, not a surprise.
How long does a typical integration take?
Weeks, not months, since we connect to software you're already running. The first project also builds the pattern we reuse, so the second and third integrations move faster.
What happens when the model gives a wrong answer?
Integrations use your own documents and data as the source, checks flag answers the AI isn't sure about, and every integration has a fallback to the normal workflow. Drafts wait for a person to approve them before they go out, so a wrong answer can be fixed first.
Does our data train the AI model?
We set up integrations with provider settings that keep your data out of model training, or with a privately hosted model when your rules require it, and we show you those settings in writing. If you need everything on infrastructure you control, see our private AI agents page.
Can someone trick the AI through an email or a form?
It's a real risk, called prompt injection. We limit what each integration can see and do, so a bad instruction can't reach data or actions the task doesn't need. We write about this risk on our blog, including how AI tools can leak data.
How is this different from workflow automation?
LLM integration adds an AI feature inside one tool, like a suggested reply in your helpdesk. Workflow automation connects several tools so the steps between them happen on their own. Many projects use both.
Tell us the tool and the task
We'll reply within one business day with what it would take: the data it needs, a build price, and expected monthly usage cost. An engineer reads every request, and if AI isn't the right fix, we'll say so.