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AI SOFTWARE DEVELOPMENT & INTEGRATION

We build the thing,
not just the prompt.

Custom AI features, API integrations, and intelligent automations built on Claude, GPT, and other leading models. Production-grade — not a demo.

Get Started (815) 600-5070
What's included

Custom AI features

Chat interfaces, content generation, classification, summarization, and intelligent search — built into your product as a real feature, not a bolt-on.

API integration

OpenAI, Anthropic, Google Gemini, Mistral — we integrate the right model for your use case and handle rate limits, fallbacks, and cost management.

RAG & semantic search

Retrieval-augmented generation so your AI answers questions using your own data — documents, databases, knowledge bases — not just training data.

AI agents & automation

Multi-step agents that take actions, use tools, and complete workflows on your behalf. More than a chatbot — a system that does work.

Fine-tuning & evaluation

Custom model fine-tuning for specialized domains where base models don't perform well enough. Evaluation frameworks to measure what's actually improving.

Workflow automation

AI-powered document processing, data extraction, routing, and decision support wired into your existing systems — not a separate tool to manage.

FEATURED AI BUILD

The AI assistant in the corner of this page? We built that.

Click the cream circle in the bottom-right corner. The bot answering your questions is the same one we ship to clients — trained on your real content, wired into your lead pipeline, and styled to match your brand.

  • Trained on your business
    Knows your services, pricing, locations, and blog. Auto-refreshes from your sitemap on every deploy — no manual uploads.
  • Captures qualified leads
    Collects name, email, and phone mid-conversation when intent is clear. Texts you a transcript the moment a lead is captured.
  • Branded to match your site
    Colors, type, voice, avatar, starter prompts, and tone are all configurable. Reads like your business — not a generic ChatGPT wrapper.
  • Cites your real pages
    Links to your service pages, case studies, and posts as proof — so visitors don't just get an answer, they get a path to convert.
  • Fast and lightweight
    Streams replies in under a second. Loads the chat runtime only when a visitor opens it — Lighthouse stays untouched.
Add this to your site Live, right now, in the bottom-right corner
Try the real one ↘
FEATURED AI BUILD · 02

Your form fills out at 11 PM. By 11:00:30, the lead is already qualified.

Form submissions get an instant, personal SMS reply written by Claude in your business's voice — with the smart follow-up question that qualifies the lead before you've even checked your phone.

  • Replies in 30 seconds, not 30 minutes
    Leads contacted in under a minute convert at 5–10× the rate of leads contacted an hour later. Most businesses miss that window. AI doesn't.
  • Qualifies before you read it
    Claude reads the form context and asks the one follow-up that matters — timeline, budget, scope. By the time you check your phone, you already know if it's real.
  • Sounds like you, not a bot
    Voice and tone configured per business — trained on your sales emails, FAQs, and brand guidelines. Prospects can't tell it's AI.
  • Every transcript filed for review
    Admin surface logs every thread. Take over any conversation, edit a reply before it sends, or kill auto-reply for a specific lead — full control.
  • Hands off when intent spikes
    When a prospect signals they're ready to buy, AI drops a calendar link or pings you on SMS to take it from there. No more leads stuck in a bot loop.
  • Wires into any form
    Contact, quote request, audit signup, demo, newsletter — same engine for all of them. One place to tune the voice, one place to review the threads.
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How we work

AI that works in production
is an engineering problem.

Most AI demos fail in production because nobody thought about latency, cost at scale, failure modes, or evaluation. We treat AI features like any other engineering problem — scoped, tested, monitored, and optimized after launch.

We start by understanding the actual job to be done: what decision or task is the AI helping with, what does good output look like, and how will you know when it's working? That shapes model selection, prompt design, retrieval strategy, and the whole architecture — not the other way around.

What you walk away with
  • Architecture proposal and model selection rationale
  • Production-ready AI feature or integration
  • Prompt system and evaluation framework
  • Cost monitoring and rate limit handling
  • Documentation and handoff for your team
  • Post-launch optimization included

The gap between an AI demo and an AI product.

Most companies are stuck at the demo stage. They've integrated a chat interface, or added a 'summarize this' button, or wired up a basic RAG pipeline — and it works in a controlled environment with curated inputs. Then it hits production, and the model hallucinates, the latency is unacceptable, costs spiral, and edge cases surface that nobody anticipated. Building AI features that actually work in production is a different discipline than building AI demos.

The difference is in the systems around the model: the prompt architecture, the retrieval system that feeds it relevant context, the evaluation framework that catches regressions, the fallback logic that handles failures gracefully, and the cost management that keeps usage predictable. We've built AI features that handle hundreds of thousands of requests per month — the patterns that make those systems reliable are the starting point for every project we take on.

We work primarily with Anthropic's Claude models and OpenAI's GPT-4 family, and we've built integrations with Google Gemini, Mistral, and open-source models via self-hosted infrastructure. Model selection is a product decision — different models have different strengths for different tasks — and we give you an honest assessment of which fits your use case before a line of code is written.

Common questions

Everything you need to know before getting started.

Primarily Claude (Anthropic) and GPT-4 (OpenAI), and we have production experience with Google Gemini and Mistral. For most business applications, Claude is our default recommendation — it has strong instruction-following, large context windows, and excellent performance on document processing and structured output tasks.

RAG (Retrieval-Augmented Generation) is the technique of giving an AI model access to your specific data at inference time, so it can answer questions based on your documents, databases, or knowledge base — not just its training data. If your AI feature needs to know anything specific to your business, you almost certainly need some form of RAG.

We configure API integrations so your data goes directly to the model provider's API — not through intermediaries. We advise on what data is appropriate to include in prompts given each provider's data retention policies, and we can build on-premise or self-hosted deployments for use cases where data can't leave your infrastructure.

Yes. Agents are AI systems that can take multi-step actions — searching the web, calling APIs, reading and writing files, executing code — to complete a task. We've built agents for lead research, document processing, customer support, and internal operations. The key is designing the tool set and guardrails correctly so the agent does what you intend.

We quote fixed-price for clearly scoped features and time-and-materials for exploratory or research-heavy projects where the scope evolves. Most integrations are fixed-price. Most novel agent builds start with a discovery sprint before we commit to a fixed scope.

Have an AI idea worth building?

Tell us the problem you're trying to solve. We'll tell you whether AI is actually the right tool and how to build it.

Start the project (815) 600-5070