you@inventive:~$ automate --prod --no-demos

Everyone's Selling AI.
We're the Ones Actually Building It.

From AI strategy to production deployment - we build automation that works on Monday morning, not just in the demo.

Get a Free AI Readiness Assessment

// the problem

The AI roadmap is full. The production environment is empty.

Your inbox has 47 emails from AI vendors promising to "transform your business." Your leadership team just sat through a board presentation where AI appeared on every slide. There's a line item in next year's budget. Everyone agrees that AI is important. Nobody has shipped anything. Meanwhile, your team is using ChatGPT on personal accounts because the enterprise plan got stuck in procurement.

This is not a technology problem - the technology works. The problem is the gap between a polished demo and a production system that runs reliably, handles edge cases, integrates with your actual stack, and doesn't quietly fail until someone complains. That gap is an engineering problem. We close it.

// no more demos

The Demo Was The Easy Part.

200+
Products shipped
4.9
Clutch rating
10yr
Shipping since 2016
Close The Production Gap

// what we build

AI & automation means six different things.

Here's what it means when you work with us.

AI Copilots

Internal tools that make your team faster without replacing them - drafting, research, summarization, decision support. We build them into the tools your team already uses, so adoption is a convenience problem, not a training problem.

Workflow Automation

End-to-end process automation that combines AI judgment with traditional logic. We map your actual workflow, identify where AI earns its place and where it doesn't, and build automation that handles the whole thing - not just the part that photographs well.

LLM Integration

Adding AI to existing apps without rebuilding them. We integrate OpenAI, Anthropic's Claude, and open-source models - search, summarization, classification, generation, retrieval - scoped, cost-controlled, and tested before they touch users. The integration is the hard part.

Custom AI Solutions

Purpose-built models and pipelines for problems general-purpose APIs weren't designed to solve - fine-tuned models, custom RAG pipelines, domain-specific classifiers, and retrieval architectures designed around your actual data.

AI-Ready Infrastructure

The data and systems foundation AI actually needs to function. Clean, structured, well-labeled data is the prerequisite for any AI initiative - the most common reason projects stall isn't the model, it's the data layer that was never set up to support it.

AI Strategy & Assessment

Identifying where AI will actually help versus where it's an expensive distraction. We assess your workflows, data maturity, and business objectives, then come back with a realistic picture of where AI adds measurable value - and where you'd be better served fixing the process first.

// the proof

2

live AI products we run every day

We don't just build AI for clients. We run it.

There's a meaningful difference between a team that builds AI for other people and one that operates AI products in production. We've lived through every architectural decision, every hallucination incident, every model update that broke something - and every cost spike that forced a rethink. We've made the calls you'll have to make, and we know what they actually cost.

ProfitFinder.aiAI revenue automation for SMBs
StudentSignal.aiAI student-lifecycle for higher ed

// the proof

Two Live AI Products.
Zero Slideware.

// how we work

How we actually do this.

Figure out if you actually need AI

Sometimes the answer is no - or not yet. A well-designed workflow with solid automation can outperform a poorly-implemented AI system on every metric that matters. If AI belongs in the solution, we make that case clearly. If it doesn't, we tell you that instead, and point you toward what will.

Get the data ready

If AI is in the plan, data infrastructure comes first. Every time. The most common failure mode isn't model selection or prompt engineering - it's starting the AI work before the data can support it. Inconsistent schemas and siloed systems aren't details to clean up later. They're the project.

Build one focused proof of concept

One high-impact workflow. Real data. Real users. A measurable outcome defined before we start. The most successful AI transformations start here - not with enterprise-wide initiatives that try to change everything at once and produce nothing useful for twelve months.

Validate, iterate, and scale

What works gets expanded. What doesn't gets fixed or cut - without the organizational inertia that keeps funding systems that aren't delivering. The goal is AI that earns its place in your operations, not AI that looks good in a QBR slide and gets quietly deprecated six months later.

// the stack

We evaluate these tools constantly. We use what works, retire what doesn't - and don't get religious about any of them.

OpenAI / GPT-4llm
Anthropic / Claudellm
Llamaopen-source
Mistralopen-source
LangChainframework
LlamaIndexframework
Azure OpenAIcloud ai
AWS Bedrockcloud ai
Pineconevector
pgvectorvector

ready when you are

Ready to move past
the AI demo?

Tell us what you're trying to automate. We'll tell you if AI is actually the right answer - and if it is, how we'd build it.

Get a Free AI Readiness Assessment Or see our work