We build AI into software you already run.
Most AI projects fail the way other software projects fail. Nobody owns the result, and nobody agreed on a number to measure. We build AI into working systems. Most of it is document processing and LLM features inside your product, and every project has a number attached. Scope and price are fixed up front. You own everything at handoff.
When AI is worth it
Every AI project starts with one test: what manual work goes away or what revenue gets captured, and how will we measure it? If we can’t answer that with you on the first call, we’ll tell you AI isn’t your project yet. That’s cheaper than a proof of concept that dies in a demo folder.
When the case is real, we build it as production software, because that’s what it is. That means versioned prompts, evaluation sets, fallback paths for when the model is wrong, cost ceilings per request, and human review where the stakes call for it. An LLM feature with no plan for failure is a liability.
We hold ourselves to the same test. In Chicago, AI-era tooling runs inside the restaurant platform we built and run. It’s working automation in a business that has no patience for toys. There’s no chatbot bolted onto the homepage.
What’s included
Document intelligence
We pull the data out of the invoices and contracts your team re-types today. Other records get classified and summarized.
LLM product features
Search and drafting features, or an assistant, inside your product. Each one ships with evaluation sets and guardrails.
Workflow automation
Multi-step business processes automated from start to finish, with a human checkpoint wherever judgment matters.
Data foundations
The unglamorous prerequisite is a clean data pipeline you can query. No model outruns bad inputs.
Cost & latency engineering
We choose the model and set up caching and routing so per-request cost and response time stay inside written budgets.
Evaluation & safety
Test sets and output monitoring, plus handling for known failure modes. Quality gets measured.
How we work
- The metric comes first. The SOW names the metric, such as hours saved or response time, before it names a model.
- Pilot on real data. A scoped pilot runs against your actual documents and workflows, with a written evaluation. We don’t demo on cherry-picked samples.
- Production from day one. Versioning, fallbacks, cost ceilings and monitoring ship with the feature.
- The digital handoff. Prompts, evaluation sets, pipelines and integration code transfer to you. There are no black boxes, and you won’t need us to understand your own system.
Related: API design & integration · web application development. Based in Chicago? See software development in Chicago.
Proof
AI in a Chicago restaurant
AI-era automation runs inside the restaurant platform we built and run in Chicago. It covers SMS loyalty and ordering along with operations tooling, in a business with no tolerance for gimmicks.
How AI is changing South Asian restaurants in ChicagoWhy software projects fail
AI projects fail for the same reasons ordinary software does. We wrote down the failure patterns we keep seeing and the principles that avoid them.
Why software projects fail, and what we doCommon questions
What kind of AI projects actually pay off?
The unglamorous ones: document processing that eliminates re-typing, support and intake triage, search that actually finds things, and report drafting with human review. Projects with a clear before-and-after metric pay off; projects that start with a technology and go looking for a problem don’t.
Which AI models do you work with?
We are model-agnostic and integrate the major commercial and open-source options. Model choice is an engineering decision driven by your accuracy, privacy, latency, and cost requirements — it is made during the project, in writing, and revisited as the landscape shifts.
What about our data privacy?
Data handling is designed before any model sees a byte: what leaves your environment, what gets logged, what the provider may retain, and what must stay on infrastructure you control. For sensitive workloads we architect for private or self-hosted deployment and document the boundaries.
Can AI features run inside our existing software?
Yes — that is the usual case. Most AI value lands as features inside systems you already run, delivered through APIs and integration code rather than a new platform. If your current system makes that hard, we will tell you what has to change first.
How do we know it is working after launch?
Because measurement ships with the feature: evaluation sets, output monitoring, cost tracking, and the baseline metric from the SOW. You see the same numbers we do — and if the numbers say it is not earning its keep, we say that too.