BackContext is live: one context layer for every agent you run.
Explore BackContextA model is only useful once it is wired into the work. Forward-deployed engineering is how we get it there: engineers inside your team until the workflow runs itself.
Most AI projects stall in the gap between a promising demo and a workflow people rely on. The model is rarely the hard part. The hard part is the forty unwritten rules about how work actually gets done, the five tools it passes through, and the person who knows which exceptions matter. Forward-deployed engineering closes that gap by putting our engineers in your channels, your standups and your tools until the work runs without us.
Applied Crew's two founders work directly with every customer, alongside our engineers.
We are Pacific-time aligned and available 7am–9pm PT. That covers the morning on the East Coast and the full working day on the West Coast. When something breaks during a rollout, the person who built it is the person who answers.
A first production model typically takes four to six weeks. The engagement follows the work.
Algebra IT, an IT staffing firm, started with us in mid-September 2026. BackContext was rolled out across the team's agent tools in a day. Five weeks later they had 64K work records and three workflows mapped for fine-tuning: intake, screening and submission. Their first model is in training, with a benchmark against Opus 5.5 and GPT-6.1 Sol scheduled.
Our core values are short, and they shape the work.
It is not a staffing contract or an open-ended consulting retainer. We are not there to write slide decks about AI strategy, and we do not bill for hours spent in meetings. Every week of the engagement should leave something running that was not running the week before: a tool wired into BackContext, an eval set, a model version, a runbook. If a week goes by without one, we should be asked why.
We are done when your team can run, monitor and retrain the model without us. That means the model is in production behind a quality gate, the cost and fallback dashboards are in daily use, retraining runs on fresh outcomes from BackContext, and your engineers have shipped at least one model version themselves using the runbooks. After that, we stay on call and help with the next workflow.
It works best when one task runs thousands of times a day, outcomes are recorded somewhere you can reach, and someone on your side owns the workflow. If that sounds like your team, book a call and we will map the first workflow together.
