Field Discovery
Bring us one workflow with weight.
The best starting point is repetitive, expensive, frustrating, consequential, measurable—and owned by a team willing to improve it.
FR / START
Start the conversation
Map the work before choosing the technology.
Tell us what the team is trying to accomplish, where the process breaks, who is affected, and what a successful change would make possible.
Email Field Runtime MAE@FIELDRUNTIME.AIA STRONG STARTING POINT
- A workflow the team knows is painful
- Enough volume or consequence to matter
- People who perform and own the work
- Accessible systems, data, and approvals
- An outcome that can be measured
THE FIRST CONVERSATION
- Understand the operation and recurring pain
- Separate rules, AI judgment, and human authority
- Test whether a build is justified
- Frame Field Discovery and its decision gates
- Leave room for “no build” when that is the right answer
ENTERPRISE FAQ
Questions to ask early.
Before an agent receives responsibility.
Which models and systems do you use?
Open models, OpenAI, Anthropic, Gemini, Hermes, OpenClaw, Exo, Buzz, or another best-fit component. The work, privacy, cost, latency, and real-task evals determine the architecture.
Can you deploy in our cloud or on-premises?
Yes. The deployment environment follows the organization’s security, data residency, infrastructure, and ownership requirements.
How are employees involved?
The people doing the work map reality, define exceptions, set authority boundaries, evaluate outputs, test the system, and learn to operate it.
How do you prevent unsafe actions?
Identity, least privilege, deterministic checks, allowlisted tools, evals, approval gates, evidence logs, pause controls, escalation, and rollback are designed into the system.
How is ROI measured?
We baseline cycle time, throughput, cost, waiting, errors, rework, risk, and team friction before implementation, then track the same measures after deployment.