Most AI projects fail the same way software projects fail: no owner, no measurable outcome, no path to production. We build AI into working systems — document intelligence, LLM-powered features, and workflow automation with a number attached. Fixed scope. Fixed price. You own everything on handoff.
Every AI engagement starts with the same test: what manual work disappears, what decision gets faster, or what revenue gets captured — and how will we measure it? If we can’t answer that with you in the first call, we’ll tell you AI isn’t your project yet. That honesty is cheaper than a proof-of-concept that dies in a demo folder.
When the case is real, we build it like production software, because it is production software: versioned prompts, evaluation sets, fallback paths for when the model is wrong, cost ceilings per request, and human review where the stakes demand it. An LLM feature without a failure path is a liability wearing a demo’s clothes.
We hold ourselves to the same test. On Devon Avenue in Chicago, AI-era tooling runs inside our own restaurant platform — not as a chatbot bolted on the homepage, but as working automation in a business that has no patience for toys.
Extraction, classification, and summarization over invoices, contracts, and records — the paperwork your team re-types today.
Search, drafting, and assistant features inside your product — with evaluation sets and guardrails, not vibes.
Multi-step business processes automated end-to-end, with human checkpoints exactly where judgment matters.
The unglamorous prerequisite: clean, queryable data pipelines — because no model outruns bad inputs.
Model selection, caching, and routing that keep per-request cost and response time inside written budgets.
Test sets, output monitoring, and failure-mode handling — so quality is measured, not assumed.
Related: API design & integration · web application development. Based in Chicago? See software development in Chicago.
AI-era automation runs inside our own restaurant platform in Chicago — SMS loyalty, ordering, and operations tooling in a business with zero tolerance for gimmicks.
Read the Devon Ave story →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.
Read the analysis →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.
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.
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.
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.
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.