The Flight Simulator for Enterprise AI.

You can, and 70% of ML deployments do exactly that before failing spectacularly in production. Historical data is a nice, tidy CSV where all the fields magically appear at once. Production is chaos: vendor data shows up 6 hours late, your database has an outage during month-end, schemas drift, and that 'optional' field becomes required when you're not looking. We simulate the chaos before you deploy into it. Think of it as the difference between studying for a test in your quiet room versus taking it in a loud cafeteria while someone's juggling chainsaws next to you.

Boeing doesn't build a 787 and immediately load it with passengers to 'see how it goes.' They run thousands of simulations first. Pilots don't hop in the cockpit on day one—they spend hundreds of hours in simulators practicing emergency scenarios. Yet somehow we've all agreed to deploy ML models by crossing our fingers and hoping production looks like the POC. Spoiler: it doesn't. The average failed deployment costs $500K and 6 months. Our simulations cost $50K and 2 weeks. Do the math, then decide if it's paranoia or common sense.

Depends. Are you doing language tasks? Then yes, LLMs are great. Are you doing fraud detection, demand forecasting, or risk scoring on structured data? Then probably not, and whoever quoted you $200K/month for GPT-4 inference is laughing all the way to the bank. We simulate both approaches in your actual production environment—with your data timing, your schema quirks, your infrastructure constraints. Sometimes the LLM wins. Often a specialized model hits 94% of the accuracy at 2% of the cost. We show you the numbers, you make the call. We're not anti-LLM, we're anti wasting money on solutions you don't need.

You give us read-only access to your systems and tell us about the model you want to deploy (fraud detection? UTI prediction? demand forecasting?). We spend a week mapping how your data actually moves through your systems—what arrives when, what depends on what, where things break. Then we generate synthetic data that behaves like your production data (not just looks like it—actually exhibits the same timing patterns, spikes, and failures). We run your candidate models through a few weeks of simulated production. Two to four weeks later, you get a report that says 'Approach A works, Approach B breaks when X happens, Approach C is overkill.' Then we deploy the winner as a managed API you can query. You integrate it however you want. Total time: 2-4 weeks. Total cost: $25K-$100K depending on complexity. Beats the hell out of spending 6 months and $500K to find out it doesn't work.