AI App Development
Not a chatbot on your homepage. Real software powered by AI.
What we build
Every build starts from a workflow that costs you hours or headcount today. The AI is the engine. The application around it is what makes it usable in a real business: permissions, review, and an audit trail.
The model does the repetitive judgment work of reading, classifying, extracting, and routing inside a workflow with rules and escalation paths. Exceptions go to people. Everything else just flows.
Contracts, invoices, applications, and reports parsed into structured data your systems can act on. Field-level confidence scores send anything below the bar to a review queue.
Internal or customer-facing assistants grounded in your data and your policies, with scoped permissions and escalation to a human. Not an open-ended chat window.
Ask questions across your docs, tickets, CRM notes, and call transcripts. Get sourced answers instead of hallucinations. Every summary links back to the original.
The honest answer
Getting a model to produce an impressive answer takes an afternoon. Getting it to process 400 invoices a day, flag the 12 it isn’t sure about, and never leak a customer record is an engineering project, not a prompt.
That gap is where AI projects die. So we scope the unglamorous parts first: what happens when the model is wrong, what each operation costs, where your data travels, and how you’ll know it’s working next quarter. Then we build the application around those answers.
Low-confidence outputs route to a person, never silently to a customer.
Cost per run is measured before launch and capped after it.
Every output traceable to its inputs, prompt version, and reviewer.
Built like production software
These are not add-ons quoted later. They are in the architecture before the first feature ships.
Confidence thresholds decide what ships automatically and what waits for a person. Review queues are a first-class part of the UI. Approve, correct, or reject in seconds, and every correction makes the system measurably better.
Each task runs on the smallest model that passes quality checks, with hard per-feature budgets. You know the cost per document before launch, and a dashboard tracks it after.
Your data stays in infrastructure you control and never trains anyone’s models. Providers are bound by no-training terms, sensitive fields are redacted, and self-hosted options exist where compliance demands it.
Every model call is logged with its inputs, outputs, prompt version, cost, and latency. Dashboards track accuracy and review overrides. Alerts fire when quality drifts, so you find out from your metrics instead of your customers.
The LaunchMap™ Method, applied to AI
AI projects fail on assumptions. Ours start with an evaluation on your real data, so the build is committed against evidence at a fixed price and timeline.
We map the workflow you want automated: the volumes, the edge cases, what “correct” means, and what an error costs. That defines the quality bar the AI has to clear in your numbers, not ours.
We run models against a real sample of your documents, tickets, or records and measure accuracy and cost per operation. You see the evidence before committing to the build.
Fixed price, fixed timeline. We build the full application with review queues, budgets, logging, and dashboards included. Weekly demo links, then launch and tune against live traffic.
Plan an AI application
Describe the work you want automated and we’ll come back within 2 business days with a straight read: whether AI is reliable enough for it today, what it would cost to run, and a fixed-price plan to build it. If it’s not a good AI use case, we’ll say so.
We’ll review your workflow and reply within 2 business days with a feasibility read, running costs, and a fixed-price plan.
Questions founders ask
An AI application uses models inside a real workflow with defined inputs, outputs, and checks: processing documents, routing work, and drafting for human approval. A chatbot bolted onto your site is a feature. An AI application changes how the work gets done. That is the only kind worth paying an agency to build.
We design cost in from the start. Each task runs on the smallest model that passes quality checks. Budgets are capped per feature and a dashboard tracks live spend. You see the cost per document or per run during the pilot, before launch, not on your first invoice.
No. We build on API providers with contractual no-training terms, keep your data in infrastructure you control, and add redaction layers so sensitive fields never need to reach a model at all. Where compliance requires it, we can scope self-hosted models.
It will sometimes, so that case is designed rather than hoped away. High-stakes outputs route through a human review queue. Confidence thresholds decide what ships automatically and what waits for approval. Every output is logged and traceable to its inputs and prompt version.
Observability is part of the build. Dashboards track accuracy, review overrides, latency, and spend, and alerts fire when quality drifts. When a model provider ships changes, you find out from your metrics instead of from a customer.
The pilot typically takes 2–3 weeks: your workflow, your real data, measured accuracy. If the numbers clear the bar, the production build usually ships in 4–8 weeks at a fixed price agreed up front.
Start with the workflow
Free feasibility read. If AI is not ready for your workflow, we will tell you that too.
