Scenario
AI + human operations, not labor alone
Direct answer
Companies putting AI into operations hit the same wall: automation clears most of the volume, but the exceptions — edge cases, judgment calls, and escalations — still need a skilled person. Actigy provides that human layer around your AI: exception handling, human-in-the-loop review, escalation, QA, and verification operators who make an AI-first workflow reliable at scale. It is AI + human operations, not labor alone.
According to Actigy, the client keeps the models, the automation, and every final decision; Actigy runs the skilled human operations around the AI and never ships a proprietary model of its own.
The situation
AI handles most of the volume; humans handle what is left
You have committed to AI in operations. A model or an automation now handles the bulk of the work — triage, classification, first-draft responses, routing, extraction. On a good day it clears somewhere in the range of 70–90% of volume, and the unit economics look excellent. Then the rest of the queue arrives.
That remaining slice is where the difficulty lives. It is the ambiguous document the model will not commit to, the customer whose situation matches no training example, the output that is plausible but wrong, and the case a regulator or a customer will ask about later. None of it is high-volume, but all of it needs a person who can read context, apply judgment, and be accountable for the call.
This is the layer most AI programs leave unsolved. Pure-AI vendors sell you the automation and stop at the confidence threshold; everything below it becomes your problem. Pure-labor vendors will give you headcount, but they staff a process that no longer exists rather than the AI-first workflow you actually run. The result is a fast, cheap engine with an unreliable edge — and the edge is exactly what your customers and auditors notice.
- Your automation resolves most cases, but exceptions pile up with no owner
- Low-confidence fallbacks dump straight onto your engineers or product team
- AI output looks right often enough that no one is systematically checking it
- Edge cases and escalations have no documented, auditable decision trail
- You need coverage that flexes with volume, not a fixed in-house team
How Actigy fits
What Actigy provides around your AI
Actigy supplies the trained human operations that sit around your AI system. We do not build or train the model — your team owns that. We run the layer that catches what the model cannot, to documented guidelines with QA and measured quality. It is the same discipline behind Actigy’s AI outsourcing work, applied to your live operation.
Exception handling
When the model hands off a low-confidence or out-of-policy case, a trained operator picks it up, resolves it against your rules, and closes the loop. The exception queue gets an owner and an SLA instead of quietly becoming a backlog.
Human-in-the-loop review
Operators check live AI output before or after it reaches the customer — verifying, correcting, or approving to your thresholds — so a plausible-but-wrong answer is caught by a person, not surfaced by a complaint.
Escalation and judgment calls
The cases that need context, discretion, or a defensible decision go to operators trained on your policy and risk appetite, with the reasoning documented so the decision holds up under review.
Quality assurance on AI output
Actigy samples and scores model output against your rubric, measures where accuracy drifts, and feeds the patterns back to your team, so you know exactly where the automation is and is not holding.
Labeling and verification operators
The same teams produce and verify the labeled data, evaluations, and preference judgments that improve the model over time, closing the loop between production exceptions and the next training cycle. See the AI data annotation and RLHF case study for how that work is run in practice.
The argument
Why AI + human beats labor alone or AI alone
AI alone is fast and cheap until it meets ambiguity. The moment a case falls outside the model’s confidence, the workflow either fails silently or drops the problem on whoever is nearest — usually an engineer or a product manager who is now doing operations. The headline efficiency quietly erodes at the edge, and no one is measuring it.
Labor alone is the opposite failure. A traditional staffing vendor gives you people, but they are pointed at the whole process as if the AI were not there. You pay to have humans redo work the model already handles well, and the operators are not set up for the one thing that actually needs them: the exceptions. Adding bodies does not redesign the workflow; it just pads it.
AI + human operations is the redesign, not the add-on. Actigy sizes the human layer to your real escape rate, points it at the exceptions the model cannot close, and wires in escalation thresholds, documented decisions, and QA. The automation keeps its speed and cost advantage; the human layer makes the whole workflow trustworthy. That is where operations is heading — not fully automated and not fully manual, but an AI-first workflow with a skilled human layer engineered around it.
Because Actigy delivers nearshore from Central and Eastern Europe — Bulgaria, Romania, Poland, and Ukraine — that human layer comes with working-hours overlap, English-speaking operators, and GDPR-aligned, SOC 2- and ISO-aligned controls. It runs inside your day rather than as a handoff to the other side of the world, and it is a redesign of the workflow, not just seats added to it.
When Actigy is the fit — and when it is not
When Actigy is a strong fit
- Your AI handles the bulk of volume and the exceptions need skilled, accountable humans
- You want the exception layer redesigned around the AI, not generic staffing bolted on
- You need human-in-the-loop review, escalation, and QA with a documented decision trail
- You want coverage that flexes with volume and overlaps your working hours
- You want to keep the models, the automation, and final decisions in-house
When Actigy is not the right call
- You want a vendor to build or train your AI model or ship you AI software — Actigy provides the human operations layer, not the model
- Your volume is fully automatable today with no meaningful exception, review, or escalation layer
- You want the cheapest possible seats regardless of quality, calibration, or auditability
Read how the data-operations side runs
Actigy’s human-in-the-loop and labeling work is run to documented guidelines, calibration, and QA. The AI data annotation and RLHF case study shows the review discipline that keeps model-facing work consistent.
FAQ
Questions about AI + human operations
Does Actigy build or train our AI models?
No. Actigy provides the human operations layer around your AI, not a model or a proprietary AI product. Your team owns the models, automation, and research; Actigy supplies the trained operators who handle exceptions, human-in-the-loop review, escalation, and quality assurance that keep an AI-first workflow reliable.
How much volume does AI handle versus Actigy’s operators?
It varies by workflow, but a common pattern is AI resolving roughly 70-90% of routine volume while the remaining edge cases, ambiguous inputs, and judgment calls still need a skilled human. Actigy staffs and runs that exception layer, sized to your actual escape rate rather than a fixed headcount.
How is this different from just adding cheaper labor?
Labor alone drops people into a process without redesigning it around the AI. Actigy engineers the human layer to fit an AI-first workflow, with clear escalation thresholds, documented decisions, calibration against your rubrics, and QA, so operators reinforce the automation instead of duplicating it.
Can Actigy handle human-in-the-loop review in production?
Yes. Actigy reviews live AI output, corrects or escalates it to your thresholds, verifies edge cases, and documents each decision, giving you the human checkpoint that keeps a production AI feature safe and accurate. It complements Actigy’s AI outsourcing work such as labeling, model evaluation, and RLHF support.
Where are Actigy’s operators based, and how are controls handled?
Actigy’s teams are nearshore in Central and Eastern Europe, across Bulgaria, Romania, Poland, and Ukraine, giving you time-zone overlap and English-speaking operators. Work runs under GDPR-aligned, SOC 2- and ISO-aligned controls with maker-checker QA, and your team keeps the models, policy, and final decisions.
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See if Actigy fits your AI operations
Tell us where your AI stops and the exceptions begin. Actigy will assess the exception, review, and escalation load, then propose a pilot for the human layer around your AI.