NEED AI CAPACITY?
See AI carry one consequential piece of work.
See how evidence, uncertainty, human judgment, authority and outcome verification work together.
Try a Case Synthetic Case · simulated approvals · no external writesAI CAPACITY FOR REAL BUSINESS WORK
Bring AI capacity to the work your team already does.
Field Runtime supplies AI workers or connects the agents you already use—then carries the work across agents, systems and people to a verified outcome.
Try before connecting anything. No production access required.
Available today: Case Lab and Replay with five recorded synthetic Cases. Operations Lab previews the broader product; broad agent connectivity is planned.
START WHERE YOU ARE
Need AI capacity—or already have agents?
ALREADY USING AGENTS?
See whether the work is working.
Keep the AI you already use. Field Runtime shows what is progressing, what is stuck, where people are needed, what actually completed and what useful work costs.
See Operations Illustrative product vision · Broad agent connectivity is planned.FOR SERVICE BUSINESSES
Own the leverage.
The models may be concentrated. The leverage doesn’t have to be.
You already own the scarce assets: your customers, your people, your expertise, and the knowledge of how the work really gets done.
AI should make those assets more valuable — not make your company less yours.
Don’t automate the expert. Automate the work around the expert.
Put AI where it creates capacity. Keep people where judgment matters. Let the operating intelligence your company creates compound inside your company.
More clients. Faster service. Better advice. Less burnout. Higher margins.
Read: Own the LeverageTHE WORK BETWEEN THE AGENTS
AI can do the tasks.
Your people are still carrying the work.
People still move context, manage handoffs, chase evidence, obtain approvals, decide what happens next, retry failures and verify whether the intended result happened.
The human has become the middleware.
Agents + systemsField RuntimeVerified outcome
People enter for: Judgment · Authority · Missing evidence · Genuine exception
OPERATIONAL OBSERVABILITY
Know whether the work is working.
Agent traces show what software did. Field Runtime shows what happened to the work—what is moving, what is blocked, where people are needed, what actually finished and whether the intended business outcome was verified.
- Cases
- 25
- ready
- 13
- need a person
- 6
- resolved
- 6
ATTENTION
The contribution needed.
Evidence, access, judgment or authority—each hold has a reason.
OUTCOMES
$18,000 cash collected
Receipts checked in this synthetic operating snapshot.
ECONOMICS
Cost of accepted work.
AI spend and human attention, measured against useful outcomes.
See the work at the level that matters.
- WORK
- What is moving, waiting or stuck?
- HUMAN ATTENTION
- Where are judgment, authority or evidence actually required?
- VERIFIED OUTCOMES
- What actually happened—not merely what an agent reported?
- ECONOMICS
- What did one verified unit of useful work cost?
Work Human Attention Verified Outcome Economics
VERIFIED OUTCOMES
An agent saying “done” isn't an outcome.
Field Runtime separates what an agent proposed or reported from what the source system independently showed and what the business ultimately accepted.
- PROPOSED
- AUTHORIZED
- ATTEMPTED
- OBSERVED
- ACCEPTED
Reliable later contradiction REOPENED
ECONOMICS
Know what useful work costs.
The unit is not cost per token. The unit is cost per verified outcome.
Baseline$37.50
Later cohort$25
Operating cost / accepted outcome
AI + runtime: $150 → $240 · Human attention: 10h → 6h · Accepted outcomes: 20 → 24 · Operating cost: $750 → $600. 4 person-hours released.
Illustrative comparison. Cohort comparability and causality are not established.
Human attention valued at $60/hour. Setup, support and fees are unknown.
See the full economics viewTRY IT ON YOUR WORK
Start with five completed payment exceptions.
See where AI could take work off your team, where people’s judgment and authority remain essential, and how to verify the result.
Together with your operator, we reconstruct the evidence, decisions, and handoffs. You receive a clear recommendation for the next bounded test—including the work AI should carry and the measures that would justify continuing.
Work Map
What actually happened.
AI Capacity Map
What AI could carry.
Human Attention Map
Where people truly mattered.
Outcome Map
What completion should mean.
The maps support one practical decision: what to test next, who remains responsible, and what evidence would justify continuing.
Start with a conversation about the workflow. We agree scope and evidence handling before you share records.
CASE LAB
See what happens when AI work meets a real exception.
Follow one synthetic Case through conflicting evidence, a human contribution, an authority boundary and independent outcome verification.
- 01
Evidence → Hold
Arrival was on time. Unloading completed later. Retain both records and hold the unsupported conclusion.
- 02
Human contribution → Authority
Clarify which delivery event the agreed term uses. Route any credit proposal to an eligible approver.
- 03
Verification
Check the receiving system independently before accepting the Case outcome.
Evidence → Hold → Human contribution → Authority → VerificationTry a Case
Synthetic Case · simulated approvals · no external writes
Models change. The work shouldn't.
Use Field Runtime workers or the intelligence you already have. The worker can change while the evidence, authority, Case history, outcomes and reviewed learning remain with the company.
Replace the worker. Retain the knowledge.
Product direction · Broad agent connectivity is planned.
More agents should mean less work for people—not more agents for people to manage.
Field Runtime is the operating layer for that work.
START WITH REAL WORK
Show us 5 completed Cases.
We'll show you where AI can carry the work, where people still matter, what a verified outcome should mean and which operating metrics are worth establishing.