Outsourced human-in-the-loop services from Eicra, a Dhaka-based company, are trained reviewers who check, correct or escalate your AI’s outputs before high-stakes actions. They also label the data your AI learns from. We work for international companies with written guidelines, measured accuracy and a full audit trail, as a managed review service, not a job board.
Three kinds of team buy outsourced human-in-the-loop review, and the right first step is different for each. Find yours, then read on.
Payroll, accounts payable and employer-of-record work is reviewed by domain experts rather than generalists and has its own page: AI oversight for payroll, AP and EOR.
Your question is which actions may run unattended. Start by sending us the list of actions your agent can take; we mark each one as safe to run, safe to sample, or must wait for a person, and that list becomes the first guideline. The calibration sample comes after.
Your question is throughput, not design. Start with the calibration sample: send 200 real items from the queue that is stuck. The accuracy snapshot tells you how much of it a reviewer can clear and what the guideline must cover before anyone touches production.
Your question is what gets recorded, not who reviews. Start by asking for our audit-record format and checking it against what your auditor expects, before any sample is sent. If a field is missing we add it to the export at the start rather than rebuilding records later.
Outsourced human-in-the-loop services include people and process, not a tool. You get a trained reviewer team, written guidelines, measured accuracy and the records your auditors will ask for. Each engagement delivers the six items below, and every production engagement includes double-checking, gold-set audits, an escalation path and a monthly report.
Does agentic AI have a human in the loop? Only when one is built in. An AI agent can plan and act on its own, so the approval step has to be designed. Our reviewers sit in front of the actions that change money, records or customer communication, and sample the rest every week.
Reviewers check AI-generated answers, classifications, summaries and extracted fields, correct what is wrong and record why.
Items below a confidence threshold, or outside the AI’s rules, go to a person with a written decision path instead of being lost or guessed. The routing rules are set when we build your workflow automation.
A reviewer approves any AI action that changes money, records or customer communication, with a log of every decision.
Text, document and form labeling for training and evaluation sets, to your guidelines, with agreement measured between reviewers. Messy source files are fixed first through data quality and cleansing.
A share of AI outputs is re-checked against a hidden golden set every week, so you see accuracy per category as a number.
Turnaround and accuracy dashboard, an escalation log, and an export of every decision with reviewer ID, timestamp and guideline version. The same records feed your evidence and traceability documentation.
| AI action | Human in the loop | What is recorded |
|---|---|---|
| Changes money, such as a refund or a payment | Waits for a reviewer’s approval | Reviewer ID, timestamp and guideline version |
| Changes a record your team relies on | Waits for a reviewer’s approval | Reviewer ID, timestamp and guideline version |
| Sends a message to a customer | Waits for a reviewer’s approval | Reviewer ID, timestamp and guideline version |
| Below the confidence threshold or outside its rules | Goes to a reviewer to check or escalate | The correction and its reason |
| Routine, inside its rules | Runs; a share is re-checked every week | Accuracy per category against the golden set |
One AI output moves through human-in-the-loop review in five steps. Your AI produces it, and low-confidence, out-of-rule or high-stakes items are flagged. A trained reviewer checks each one against the written guideline, then approves, corrects or escalates it. The decision lands in the audit trail with reviewer ID, timestamp and guideline version.
Weekly gold-set check: a share of outputs is re-checked, so accuracy per category is a number.
Illustrative flow. Yellow marks the step where a person decides; every decision is exported with reviewer ID, timestamp and guideline version.
Human-in-the-loop outsourcing works in four steps. A free calibration sample runs on your real outputs, and a two-week fixed-price pilot produces guidelines and an accuracy report. Production review is billed by the hour or item, with an optional dedicated reviewer pod. Your team joins one call per week, and reviews run daily on our review floor.
What are 10 examples of outsourcing? Customer support, IT support, software development, payroll, bookkeeping, recruitment, data entry, digital marketing, data labeling and AI output review are ten common ones. Human-in-the-loop review is the last on that list, and the one this page covers.
A pilot review batch takes two weeks at a fixed price, after a free five-day calibration sample. Production review is then billed per reviewer hour or per item.
Human-in-the-loop outsourcing is safe when every engagement starts with a mutual non-disclosure agreement (NDA) and, for personal data, a data processing agreement. Reviewers work on a no-phone floor in your tools or a locked-down desktop and see data only while reviewing it. Every decision goes into the audit log. Certifications are listed only when actually held.
Article 22(1) of the General Data Protection Regulation (GDPR) says a person “shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her.” Human-in-the-loop review puts a person into that decision before it takes effect. The National Institute of Standards and Technology (NIST) groups AI risk work into four functions in its AI Risk Management Framework: govern, map, measure and manage.
Our human-in-the-loop outsourcing services are backed by published evidence instead of promises. You get measured commitments for every step and a free calibration sample on your real AI outputs before you pay anything. Case studies are published only with the client’s written permission, and figures only where the client allows them to be shared.
In the free calibration sample, with an accuracy snapshot and a note on what a guideline must cover.
Accuracy and percentage agreement between reviewers per category, measured against a hidden gold set.
Exported with reviewer ID, timestamp and guideline version, so an auditor can trace who approved what.
One client was producing AI output faster than anyone on their side could check it. We placed trained reviewers in front of that output. The engagement is summarised below; the client is not named, and no figures are published without their written permission.
Trained reviewers evaluated the AI outputs against a written guideline, identified errors, recorded the reason for each correction, and fed that back so the same error class could be prevented rather than repeatedly fixed.
Result. Content accuracy and consistency improved, and quality control moved from ad hoc checks to a recorded process the client could show to anyone who asked.
The client had to review a large volume of AI-generated content and keep accuracy, quality and human oversight intact as that volume grew. Checking everything in-house was not realistic, and leaving the output unchecked carried a risk they were not willing to take.
Key outcome. The AI kept doing the volume and people kept the judgement, so the risk of unchecked AI-generated errors reaching customers was reduced.
On publishing figures. Figures from this engagement are held under confidentiality. Where a client allows them to be shared, measured results are published on this page with the method used to produce them.
The high-risk deadlines moved in 2026. Regulation (EU) 2026/1744, the Digital Omnibus on AI, entered into force on 27 July 2026 and pushed the core obligations for high-risk systems back, while leaving the transparency duties where they were. The dates below are the ones in force today.
Article 14(1) requires that a high-risk system be built so that it can be “effectively overseen by natural persons during the period in which they are in use”. A deferral is not a repeal. Classification under Article 6 still decides whether a system is high-risk, and the oversight record an authority asks for is assembled while the system runs, not reconstructed afterwards. Sixteen extra months is time to build it calmly.
Primary source: the Artificial Intelligence Act on EUR-Lex. Dates last checked 1 October 2026. Informational only, not legal advice. See also EU AI Act documentation.
Reviewer accuracy is measured against a hidden gold set, and reviewer-to-reviewer agreement is reported as percentage agreement. The method is written out below so you can judge the figure rather than take it on trust, and so your own team can reproduce it.
What percentage agreement does not tell you. Percentage agreement counts matches. It does not subtract the matches two reviewers would have reached by chance. On a task with only a few categories that chance share is large, so the headline figure reads higher than the real skill it represents.
We publish it because it is what we currently measure, and we name its limit rather than implying a stronger statistic. The same recorded decisions can be re-scored with a chance-corrected measure such as Cohen’s kappa when a client needs one, because every decision is stored with reviewer ID, timestamp and guideline version.
Teams ask us about human-in-the-loop cost, the concept, the approach and the approval of AI agents. Under the EU AI Act, the human oversight requirement in Article 14 applies to high-risk systems from 2 December 2027 for standalone Annex III systems and 2 August 2028 for systems embedded in regulated products, after Regulation (EU) 2026/1744 moved those dates. The Article 50 transparency obligations have applied since 2 August 2026, and Article 22 of the GDPR has applied since 2018. The oversight evidence an auditor asks for takes months to build, which is why every decision our reviewers make is logged with reviewer ID, timestamp and guideline version.
More answersEach step has a fixed price, shown on the price cards at the top: a free calibration sample, a fixed-price two-week pilot review batch, production review billed per reviewer hour or per item, and a dedicated reviewer pod on a monthly rate. Domain-expert review for payroll, human resources and finance work has its own hourly rate.
The concept of human-in-the-loop is that a trained person checks, corrects or escalates the output of an AI system before it is used, and labels the data the AI learns from. In our service, reviewers work to written guidelines, their accuracy is measured against a gold set, and every decision is logged.
The human-in-the-loop approach sends the AI’s low-confidence, out-of-rule or high-stakes items to a trained reviewer, who checks each one against the written guideline and approves, corrects or escalates it. Every decision is exported with reviewer ID, timestamp and guideline version, and a hidden gold set measures accuracy every week.
Human-in-the-loop authorization means the action of an AI agent waits for a person before it runs. Our reviewers approve, correct or escalate any action that changes money, records or customer communication, and every decision is logged with reviewer ID, timestamp and guideline version, so an auditor can see who approved what.
No. This page is for companies that need reviewers; we do not list jobs here. As a Bangladesh-based company, we recruit through our own careers page, train reviewers on written guidelines and keep them on our payroll, so you get a managed team with one contract, not individual freelancers.
You get a short call, then a calibration sample. The sequence is:
Yes, and the handover is a defined sequence rather than a restart. We read your existing guidelines, run a calibration sample against your current vendor’s recent output, record where our reviewers agree and disagree with it, and rewrite the guideline only where the sample shows it is unclear. Your gold set stays yours.
You do. Labels, corrections, guidelines and gold sets produced for your engagement are your property under the contract, and they are returned or deleted at the end of it. We do not reuse your data, your guidelines or your labels for another client or for our own training.
Your data stays in your systems. Reviewers work inside your review tool or a locked-down desktop that holds no local copies, so the data does not leave the environment you control. Where personal data is involved, an International Data Transfer Agreement or standard contractual clauses govern access from Bangladesh.
We rework it at no cost. Accuracy targets and turnaround are written into the service level agreement during the pilot, and a batch that falls below the agreed target is re-reviewed without a further charge. If the target is missed twice in a row, we stop production and rewrite the guideline before continuing.
When the work needs a licence we do not hold, or judgement we cannot document. We do not take graphic or harmful content moderation, we do not sign off on whether your AI is legally compliant, and we are not the right choice for sub-minute real-time review. We say so before a pilot rather than after.
The sequence is fixed, and the clock depends on your side. A calibration sample runs over five working days once the non-disclosure agreement is signed and the items arrive. A pilot review batch runs for two weeks after that. Production starts when you approve the pilot’s guideline and accuracy report.
Start with a free calibration sample.
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