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/AI Integration

AI integration services. Real results. No hype.

We build AI into the systems you already run. It takes repeat work off your team, helps you decide faster, and gives you capabilities you didn't have before.

AI that works
for your business

Most businesses know AI matters. Few know where to start. The gap between "we should use AI" and something running in production is where projects stall and budgets burn.

We close that gap. We build LLM features for extraction, search and support, plus predictive models where the data supports them, and connect them to the tools you already use. At Samacare, LLM-based data extraction was part of the work behind a "115% increase in user engagement".

We use the same tools ourselves. Our AI competitor intelligence platform, now in private beta, is built by the same engineers. In the partner centers we select and manage for customer support, AI routes, transcribes, drafts and scores QA on every interaction, and a person answers and decides.

What we build

LLM Features

Extraction, summaries, search over your own documents and drafting, built on GPT, Claude or open models and wired into your workflow.

Workflow Automation

Automation that takes repeat work off your team: routing, document processing and data entry. We estimate the hours saved before we build, and measure them after.

Assistants & Chatbots

Assistants that answer customer questions, qualify leads and hand off to a person when they should.

Predictive Models

Forecasting, anomaly detection and scoring, where you have the data to support them.

Deployment & Running Costs

Prompt design, evaluation, monitoring, and fine-tuning where it's worth it, with model choices that keep running costs predictable.

AI Strategy

Which use cases are worth building, which aren't, and in what order.

Who this is for,
and who it isn't

This fits companies with a repeated, costly task: documents to read, tickets to sort, data to move between systems. It works best when there's a clear owner and a way to measure whether the AI is doing the job.

It isn't a fit if the goal is to "do something with AI" without a problem in mind. We'll help you find the problem first, or tell you that a simpler fix, like a better form or a small script, will do.

How a project works

01

Discovery

We look at your systems, data and workflows, and rank the use cases by value and effort. You get specific options, not a generic AI roadmap.

02

Design

We choose models and tools, design how it plugs into your stack, and plan for data, security and cost.

03

Build

We ship in small increments, so you see something working before the project is finished.

04

Test

We test for accuracy, edge cases, bias and behavior under load before anything reaches production.

05

Monitor

We watch quality and cost after launch and adjust as your data and the models change. AI isn't something you install and forget.

Case study

Samacare

AI work for a healthcare platform. Healthcare platform optimization with LLM-based data extraction and a rebuilt Chrome extension, delivering a 115% increase in user engagement.

  • 115% increase in user engagement
  • LLM-based automation of data extraction
  • Multi-provider patient enrollment automation
Read the case study

Frequently asked questions

What kinds of AI do you build?

Mostly features built on large language models, such as GPT, Claude and open models, for extraction, search, summaries and support. Where the data supports it, we also build predictive models for forecasting and scoring. We'll say plainly if your problem needs something we don't do, and we'll often suggest a simpler fix when AI isn't the right tool.

How long does an AI integration project take?

A focused feature, such as an assistant or an LLM step in an existing workflow, can often go live in two to four weeks. Custom models and larger automations usually take two to four months. We start with a short discovery phase to scope the work properly, then ship in increments, so you see results before the whole project is done.

Do we need a lot of data to benefit from AI?

Not necessarily. Pre-trained models can do useful work with very little of your own data, especially for reading documents or answering questions. Custom predictive models need more, and we'll check what you have before recommending one. We'll be honest about what's realistic with your current data, even if the answer is to collect better data first.

How do you handle AI safety and responsible use?

Before launch we agree what the system may decide on its own and what needs a person, test for bias and bad outputs, and check which rules apply to you, such as those for health data. After launch we monitor quality and drift. For a public reference on the risks worth checking, see NIST's AI Risk Management Framework.

Can AI work with our existing tools?

That's usually the point. We connect AI to the systems you already use: CRMs, ERPs, help desks, document stores and custom apps. We integrate through APIs where they exist and build connectors where they don't, so your team keeps working in familiar tools and the AI does its part in the background.

Will adding AI disrupt the systems we already run?

It shouldn't, and we plan it so it doesn't. We connect through the APIs your systems already offer, build and test against realistic data before anything touches production, and ship in small increments rather than one big switch. Your team keeps working in the same tools while the AI takes on one step at a time. If a step doesn't hold up in testing, it doesn't go live.

How do you control what the AI can see and do?

We decide that in the design phase, before the first line of code. For each use case we agree which data the AI can read, which actions it can take on its own and which need a person to approve. Most of what we build drafts, sorts or suggests, and a person makes the final call. Your security team can review those limits before launch, alongside the data map.

What happens to our data when you use an LLM?

We pick providers and settings that don't use your data to train their models, and keep sensitive fields out of prompts where we can. If you need it, models can run in your own cloud account. We document which data goes where, so your security team can review it before launch, and we'll follow your rules if they're stricter than ours.

What do AI integration services cost?

We quote per project after discovery, because cost depends on how many systems are involved, how much data work is needed and the model's running costs. You'll see both the build price and an estimate of monthly running costs before you commit. Starting with one well-chosen use case is usually the cheapest way to find out what AI can do for you.

Related services

Web & Product Design

Products designed with the AI built in from the start.

Strategy & Consulting

Decide where AI fits in the plan, and where it doesn't.

SEO & Content

Get found in search and in AI answers.

Got a task
AI should be
doing?

Tell us what it is. We'll tell you whether AI is the right fix, and what it would take.

Talk to an engineer