AI that knows when to stop and ask a human

ML algorithms

The parts of the business that are noisy but learnable. We train, evaluate, and ship models that earn their keep — and we set the monitoring that tells you when they stop earning it.

• Lead scoring — ranking inbound and outbound leads by likelihood to convert, so the sales team works the top of the list instead of the freshest of the list.
• Price prediction and calculation — pricing models for real-estate and e-commerce, including fee, margin, and discount logic that the finance team can reconcile.
• Production-workflow checks and presets — algorithmic validation of complex production workflows: which combinations of presets, parameters, and inputs are valid before a job starts running.

AI workflows with Human in the Loop (HITL)

The parts of the business that are messy enough that a single model cannot answer them end-to-end. We build agentic workflows that do as much as they can confidently, then hand off — to a reviewer, an approver, or a domain expert — when they shouldn’t decide alone.

• Pipeline and workflow assistance — AI-assisted validation inside production pipelines: catching the bad batch before it ships, suggesting fixes a human approves.
• Research agents for real estate — agents that pull property data, score the lead, and generate custom reports in bulk, then queue anything ambiguous for a researcher to confirm.
• Customer-success workflows (HITL) — triage, drafting, and routing for CS teams; the agent does the typing, the human signs off before it sends.
• Bulk document processing (HITL) — invoices, contracts, property documents — auto-processed above a confidence threshold, escalated to a reviewer below it. No silent low-confidence approvals.
• Scheduling and appointment confirmation — automated outreach, reschedule handling, and confirmation flows that close out without a human in the path until they need to be.

How the HITL pattern works

flowchart LR
    A[Input: documents, leads, prices] --> B[Model / Agent]
    B --> C{Confidence ≥ threshold?}
    C -->|yes| D[Auto-process]
    C -->|no| E[Human reviewer]
    E --> D
    D --> F[Downstream system]

Every workflow has an explicit confidence threshold. Above it, the system runs on its own. Below it, the work goes to a person. That number is tuned per workflow and shown on a dashboard, not buried in code — so the team can move it as they get comfortable.

Where ML and AI fit in the rest of the stack

ML models and AI agents only work if the data underneath them does. We build them on top of the same warehouse, pipelines, and applications we already deliver — so the model has clean inputs, the agent has a sane API, and the result lands back in the system the team actually uses (HubSpot, the operational app, the BI tool — wherever the decision actually gets made).