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Requester documentation
Whether you ship models, mobile apps, marketplaces, or internal tools, you eventually need many small human judgments—rankings, verifications, edge-case labels, beta flows, red-team prompts, or moderation samples—done under rules you can defend. This guide explains how teams fund escrow-backed microwork, freeze rubrics, review at throughput, and export an audit-ready trail for finance, risk, and procurement. Workers should read the worker guide instead.
Is LIKIOMO a fit? — checklist Public FAQs — payouts & safety Email partners@likiomo.co.uk
Governed human intelligence: one brief version per HIT, structured approvals, appeals with evidence, and exports that attach reviewer context—not anonymous crowdsourced noise. If your roadmap says “we need hundreds or thousands of consistent human judgments every week,” you are in the right place.
LIKIOMO is built for high-volume, well-specified units of work where humans still outperform automation—or where automation needs a human ground truth layer next to it. That includes model and product evaluation, multilingual QA, policy interpretation, fraud and integrity sampling, receipt-level verification, beta and launch testing, and any operational checklist that must leave a replayable record for your own risk, legal, and finance teams.
Each HIT should express a single atomic outcome (one row, one screenshot bundle, one labeled asset, one survey completion, one pairwise judgment) so workers know exactly when they are done, reviewers apply one bar, and your exports stay joinable to internal IDs (user IDs, model versions, build numbers, ticket URLs).
We are deliberately not a substitute for long-form consulting, bespoke agency retainers, or open-ended research interviews. We are the operational layer when you need repeatable microwork at scale with escrow, rubrics, and structured outcomes—the same pattern whether you are an AI lab calibrating evaluators, a consumer team shipping a new app build, or a fintech sampling high-risk flows.
Why companies standardize here
When a regulator, auditor, enterprise customer, or your own board asks how a number or label was produced, “we emailed a vendor” is weaker than “here is the funded batch, the frozen rubric, each submission, rejection codes, appeals, and payout events in one system.” LIKIOMO is designed so that second answer is feasible without rebuilding a custom workflow stack every time you launch a new model, locale, or product surface.
Teams use the same primitives—batch, rubric, escrow, review, export—across different industries. Below is how that usually shows up in practice. Your exact workflow is configured in the product; this section is so first-time visitors can pattern-match quickly.
Preference and safety rankings, side-by-side model comparisons, red-team prompt batteries, rubric-scored responses, annotation for edge cases, and human review queues sitting next to automated classifiers. Typical goal: stable eval bars across model versions and locales.
Beta and soft-launch task journeys (onboarding, checkout, settings), screenshot evidence packs, copy and UI checks per locale, and “does this build match the spec?” passes before app store submission or a major marketing push.
Severity triage samples, policy interpretation with written rationale, marketplace fraud patterns, payments risk sampling, and evidence suitable for internal investigations—always within the rules you publish to workers.
Structured extraction, receipt and document transcription, taxonomy alignment, duplicate detection assists, and back-office verification where each row must be provable to downstream systems.
Think of capabilities as templates your team clones: each template encodes instructions, proof types, reward bands, eligibility, and review rules. Over time you build a library of approved patterns—so a new launch or model cut does not restart from a blank spreadsheet.
These are recommended patterns, not one-size-fits-all contracts. Enterprise programs can add private cohorts, NDAs, SSO-aligned onboarding, and custom procurement paths—start with a pilot that proves turnaround and rubric stability before you widen eligibility or raise weekly caps.
Goal: reproducible human judgments that track model versions and locales. Week 0–1: freeze a short rubric (what “safe,” “helpful,” or “on-brand” means in your terms), add gold examples and counterexamples, run 200–500 overlapping judgments to measure inter-rater agreement. Week 2+: widen cohorts only after rejection reasons stabilize; attach each batch to a model ID and prompt template ID in exports so ML teams can slice metrics without re-joining spreadsheets.
Goal: catch UX, copy, and broken flows before public launch or store review. Week 0–1: define journeys (install → sign-up → paywall → key action) with explicit success screenshots and device constraints. During beta: rotate workers across strata (OS version, locale) so you do not only see power users. At launch: keep a thin standing queue for regression checks on each release candidate.
Goal: defensible samples next to automation. Design: pair every automated score with a human-readable rubric clause; rejection and escalation codes map to policy sections. Scale: start with highest-severity categories; add velocity caps for new workers on sensitive queues; use duplicate and device signals to reduce collusion risk before you increase spend.
Goal: clean training or operations data with audit trail. Design: single outcome per row, explicit handling for “unclear” and “skip,” attachment rules for source documents, and reviewer metadata in exports. QA: gold items and periodic spot checks; quarantine ambiguous rows instead of forcing a label that will poison the dataset.
Honest scope boundary
If your work requires clinical diagnosis, legal advice, regulated investment recommendations, or handling of highly sensitive credentials, it may be unsuitable or may require bespoke legal review and restricted cohorts. Say so upfront when you contact us—we will tell you plainly rather than oversell.
Every stage below produces objects your org can point to: a batch ID, a funded escrow line, a submission ID, a rejection code, an appeal thread, a payout event, and an export row. That is the difference between “we did some QA” and “here is the chain of custody for this metric.”
You can pause, resize, or retire batches without losing history—critical when legal asks what happened in a closed quarter, or when you need to prove which build or model version a label set belonged to.
Most quality failures trace to moving targets: instructions that change mid-batch, reviewers who apply a different bar than the brief, or missing edge-case rules. At small volume you can paper over that with hero reviewers; at company scale you cannot. Strong batches read like a test plan—every ambiguous situation has a written default.
Escrow aligns incentives: workers are not asked to donate time, and you are not asked to pay for work that fails the rubric—provided the rubric matches how you review. If reviewers are harsher than the written spec, you will see appeals, churn, and slower throughput. If reviewers are looser than the spec, your downstream systems inherit garbage labels. Treat review calibration as part of the product launch, not an afterthought.
Configure approval windows and auto-release rules appropriate to your risk tolerance; document them for internal stakeholders so support tickets do not invent new policy mid-quarter.
Fair treatment of workers is not only ethical—it improves completion quality on your next batch because reputation and retention compound.
Trust for enterprises is not a marketing slogan—it is evidence under time pressure. LIKIOMO is structured so your team can show: what was funded, who could see the work, what instructions they saw, what they submitted, how it was reviewed, why it was rejected or approved, whether an appeal ran, and when funds moved. That is the bar for AI teams publishing evals, for marketplaces defending integrity decisions, and for any org that might face a serious internal or external review.
Reputation and integrity signals gate who can see sensitive batches. For higher assurance, combine cohort filters, invite-only private pools, NDAs where applicable, and SSO-aligned onboarding for enterprise teams (enabled per account program—ask when you book a discovery call).
Rejections should reference instruction clauses, not informal tone. Appeals carry attachments and timestamps so risk and legal can replay a decision without joining a thread. Arbitrary approvals or rejections erode marketplace trust and can trigger platform review—design your rubric as if it will be read by someone outside your team, because eventually it will be.
Transparency we commit to in conversations
We will be direct about fit, timeline, and limits: what the self-serve product does today, what needs an enterprise program, and what may be out of scope for policy or legal reasons. We do not invent fake statistics or guaranteed SLAs on this marketing page—your security questionnaire and contract stage are the right place to pin exact commitments.
Quality is a system design problem: rubric clarity, reviewer training, sampling strategy, and fraud resistance. Heroic manual review does not survive a 10× spike in volume the week you launch in a new country or ship a new model.
Run a pilot on a narrow cohort with frozen instructions. Measure turnaround, rejection mix, inter-rater stats, and appeal rate for at least two review cycles before you treat numbers as stable. When metrics hold, widen eligibility, add gold coverage, and connect exports to your warehouse—the worker-facing task text stays consistent because you version briefs instead of editing live prose.
First 30 days (typical company path): (1) Pick one high-value workflow and shrink it to atomic HITs. (2) Fund a small batch and fix rubric holes exposed by real submissions. (3) Assign a named reviewer owner and rejection code dictionary. (4) Wire exports to one downstream consumer (ML pipeline, BI table, or ops queue). (5) Only then expand parallel batches or locales.
For regulated workloads, route procurement questions, DPAs, and custom MSA paths through the contact channels below; self-serve posting remains available for teams that already cleared vendor review.
Keep sensitive handoffs inside LIKIOMO where the product supports it so access and retention stories stay coherent. Do not request credentials, off-platform payment, installation of unknown binaries, or circumvention of your own security policies.
You remain responsible for the lawfulness of requested work and for classifying data that may leave your organization. If briefs could contain customer PII, regulated health or financial data, or minors’ data, involve your legal team before you publish—use private cohorts, redaction rules, and access controls when available, and avoid tasks that would force workers to handle illegal or unsafe content.
If workers flag content that violates law or platform policy, treat those signals as first-class operational input—they protect your users and your brand as much as they protect the marketplace.
Accepted rows export in structured formats with reviewer metadata so ML pipelines, labeling QA tools, fraud investigation notebooks, and finance systems ingest the same truth. Plan retention and deletion in line with your internal policies; quarantine sensitive submissions until legal sign-off when needed.
Most mature teams attach stable internal keys to every HIT (experiment ID, model version, build number, ticket URL) at composition time so exports join cleanly in Snowflake, BigQuery, Databricks, or on-prem stores—without workers ever needing access to your warehouse.
Use this table honestly with your PM and legal partner. If most answers land on the right, you will move faster and get cleaner data; if many land on the left, we may still help after scoping—but expectations should be set before money moves.
| Strong fit | Needs extra scoping |
|---|---|
| Work splits into clear atomic HITs with written acceptance criteria. | Work is open-ended research, therapy, or bespoke consulting per session. |
| You can describe prohibited content and edge cases in advance. | Workers would need unfettered access to production customer accounts. |
| You want escrow-backed completion and structured review. | You only want informal feedback with no funding or rubric model. |
| You are willing to invest in gold items and rejection codes. | Instructions change daily and cannot be versioned. |
| Exports with metadata matter for ML, finance, or investigations. | No one internally will own review quality or calibration. |
If you are unsure, book a short discovery thread with your stack (where data lives, regions, weekly volume). We would rather scope correctly once than reverse a rushed batch.
Whether you are a seed-stage team validating a beta checklist or a global org routing human review next to production ML, pick the channel that matches urgency and depth. We route enterprise conversations separately from self-serve traffic so your thread keeps context across legal, security, and finance questions.
Before you write: include company name, regions in scope, approximate weekly HIT volume, data classification (none / internal / customer PII / regulated), and whether you need private cohorts, NDAs, or SSO. That single paragraph accelerates answers more than a generic “let’s chat.”
Email partners@likiomo.co.uk with timezone, three preferred slots (UTC), and agenda—e.g. AI eval volume, app beta coverage, compliance regions, or export targets (warehouse, lakehouse, on-prem). We reply with a calendar hold or clarifying questions—typically within two UK business days for qualified enterprise inquiries. If you are under NDA with your own customers, say so; we can suggest a mutual NDA path where appropriate.
Request a meetingUse the secure contact form for structured requests—technical support, account issues, partnership, or high-level hiring questions. Select the closest topic so your ticket lands with the right team. Attach a one-pager or link to internal docs only if your security policy allows it.
Open contact formFor in-product bugs, payouts, or account recovery, use support@likiomo.co.uk. For general inquiries, info@likiomo.co.uk. Include account email, batch or job IDs if applicable, and steps to reproduce—support resolves faster with IDs than with screenshots alone.
Email supportFor vendor onboarding (MSA, DPA, insurance, subprocessors, data residency questions), start with partners@likiomo.co.uk with your vendor portal link or questionnaire attached. We consolidate answers so your legal team does not get conflicting replies from different channels.
Email procurementWhy companies forward this page internally: it names the operating model (escrow, rubric, review, appeals, export), names honest boundaries, and gives each function (product, finance, legal, procurement) something concrete to react to. Forward it with your own note: what you want to pilot in the next two weeks, and which rubric owner will sign off.
Do you guarantee worker supply in every region?
No—marketplace throughput varies by reward, difficulty, and season. Pilots surface real latency before you commit launch dates.
Can LIKIOMO replace our full-time moderators or labelers?
Usually not alone; it replaces unstructured crowdsourcing and spreadsheet chaos with governed microwork. Many teams blend internal reviewers with external pools.
Will you sign our NDA / MSA?
Enterprise programs can follow your paper or ours—start via partners email with legal contact and timeline.