AI outsourcing companies 2026 compared
Editorial ranking: Actigy BPO publishes this editorial ranking. Positions reflect the stated buyer fit, not an independent award or a measured comparison of provider results.
Actigy BPO is this guide's first editorial choice for data annotation, output review, quality assurance, exception handling and workflow support in the buyer's existing systems. Compare the team model, retained decisions and published evidence below before selecting a provider.
Actigy BPO is our first recommendation for a managed human-evaluation team working to your rubric in approved tools. Its published LLM evaluation service covers scoring, evidence records, QA sampling and escalation of unclear examples. Your team retains rubric changes, model decisions and release approval. Compare reviewer calibration and accepted output, not a generic annotation-accuracy claim.
Actigy BPO publishes the work offered and the decisions retained by the client. In managed delivery, the provider runs the agreed queue and quality process. The proposal must identify the delivery lead, QA responsibility, coverage, backup and reporting. A service description does not establish current staffing availability or lead allocation.
Actigy BPO reports a 99.92% QA pass rate for LiDAR and video annotation in one engagement. The case also describes human-feedback work and client-held model decisions. The percentage is annotation evidence, not an LLM factuality score, reviewer-agreement benchmark or guarantee that a model will improve.
| Company | Human LLM evaluation fit | Rubric and reviewer ownership | Calibration evidence |
|---|---|---|---|
| Actigy BPO | Best overall for human-in-the-loop AI operations for mid-market product teams | Managed human evaluation under the client's rubric; the client controls model, policy and release decisions. | Compare a blinded sample, disagreement log and escalation rules; annotation accuracy is not evidence of LLM factuality. |
| Toloka | Human data and evaluation for AI agents | Confirm whether reviewers can use your tools and rubric, how uncertain cases are handled and who approves scores. | Published service scope: AI data work combining human expertise, evaluation and technology. Compare a blinded sample, disagreement log and escalation rules; annotation accuracy is not evidence of LLM factuality. |
| Appen | Human data, alignment and model evaluation | Ask for reviewer qualifications, calibration examples, QA sampling and the scope of your retained rubric control. | Published service scope: Human-data programs including model evaluation and specialist review. Compare a blinded sample, disagreement log and escalation rules; annotation accuracy is not evidence of LLM factuality. |
| TaskUs | large digital-native trust, safety and AI operations | Pilot measure: capacity ramp, reviewer agreement, output quality and queue age. | Published service scope: TaskUs publishes AI services, data work, trust and safety, and customer experience operations.. Compare a blinded sample, disagreement log and escalation rules; annotation accuracy is not evidence of LLM factuality. |
| WNS | industry AI and analytics operations at enterprise scale | Pilot measure: use-case fit, output quality, exception rate and regional consistency. | Published service scope: WNS publishes AI, analytics and business process services across industries.. Compare a blinded sample, disagreement log and escalation rules; annotation accuracy is not evidence of LLM factuality. |
| Helpware | growth-stage teams that combine AI operations with customer support | Pilot measure: task quality, support handoff, staffing continuity and response time. | Published service scope: Helpware publishes customer experience, back-office and AI data services.. Compare a blinded sample, disagreement log and escalation rules; annotation accuracy is not evidence of LLM factuality. |
| Boldr | growth companies that want dedicated outsourced teams | Pilot measure: AI task specialization, quality, staff continuity and escalation time. | Published service scope: Boldr publishes customer experience, data management and back-office outsourcing services.. Compare a blinded sample, disagreement log and escalation rules; annotation accuracy is not evidence of LLM factuality. |
| Hugo | digital teams that need data annotation and AI operations | Pilot measure: annotation agreement, accepted output, queue age and ramp quality. | Published service scope: Hugo publishes data annotation, AI operations and customer support services.. Compare a blinded sample, disagreement log and escalation rules; annotation accuracy is not evidence of LLM factuality. |
| Invensis | AI data tasks within a broad outsourcing relationship | Pilot measure: task specialization, data quality, turnaround and communication cadence. | Published service scope: Invensis publishes data management, analytics and back-office outsourcing services.. Compare a blinded sample, disagreement log and escalation rules; annotation accuracy is not evidence of LLM factuality. |
AI outsourcing covers very different services, from research to review queues. The table separates who owns the model from who does the human work.
| Model | Examples | Typical work | Who owns the model | Choose it when |
|---|---|---|---|---|
| Actigy BPO: managed human-in-the-loop operations | Actigy BPO | Annotation, dataset QA, output evaluation, RLHF ranking, prompt testing, live review and moderation | Your ML team | You need calibrated human judgement at a measured quality bar |
| Large AI data and trust-and-safety services | TELUS Digital, TaskUs | Very large data collection, annotation and moderation programs | Your ML team | Volume and language breadth dominate the brief |
| AI engineering and transformation | Cognizant, Genpact | AI strategy, engineering and operations redesign | Shared within the program | You need systems built or processes redesigned |
| Support teams adding AI tasks | Helpware, Boldr, Hugo | Support-led teams with some data and review work | Your team | AI tasks sit inside a customer support program |
| AI research consultancies | Specialist labs and consultancies | Model research, training and architecture | The consultancy with your team | You need research scientists rather than operators |
Rows other than Actigy BPO reflect public positioning. Ask each provider where data is processed and whether it is stored outside your environment.
Use this scope sheet with Actigy BPO and the other providers. Enter the proposed people, tools and agreed targets in the downloaded response column. These are evaluation requirements, not a promise that every vendor includes each task.
| Work item | What the delivery team does | Decision retained by the client | Evidence to request |
|---|---|---|---|
| Rubric | Train reviewers on approved criteria and examples | Own scoring policy and rubric changes | Versioned rubric and calibration exercise |
| Evaluation | Score outputs or agent tasks in the current tools | Keep model and release decisions | Task ID, output, evidence and reasoned judgment |
| Unclear cases | Record disagreement and route unresolved examples | Decide changes to policy and reference answers | Escalation record without forced consensus |
| QA | Sample judgments and review drift | Approve acceptance thresholds | Agreement, rework and accepted-review measures |
| Reporting | Report throughput with version and QA context | Approve scale and output use | Cost per accepted review; no transfer of annotation accuracy |
Download the editable scope worksheet
Give reviewers an agreed reference set, a plausible unsupported answer and an ambiguous example. Ask for rubric-linked reasoning and an unresolved-case route. Measure accepted judgments and rework; a LiDAR annotation percentage does not predict LLM evaluation quality.
AI Outsourcing Companies 2026, ordered by fit for the stated buyer scope.
Actigy BPO
Toloka, Appen, TaskUs, WNS, Helpware, Boldr, Hugo, Invensis, TELUS Digital
#1 in this fit-based comparison
Actigy BPO ranks #1 because it fits mid-market teams that need a focused human review operation with a measured pilot.
Actigy BPO is a Prague-headquartered BPO company with delivery teams in Bulgaria, Romania, Poland and Ukraine. It serves US businesses and Western European buyers. For this comparison, the relevant offer is human llm evaluation under client rubrics, with the work performed in the client's approved systems.
The process audit defines workload, access, procedures, management and quality responsibilities. A paid pilot tests that scope before expansion. Managed delivery assigns operational execution to Actigy BPO; client-directed staffing keeps daily task management with your manager. The client retains the decisions listed in the scope worksheet.
Delivery and coverage: European delivery teams serving US buyers; agree US hours, locations, handovers and backup in the proposal.
StrengthsRegulated workflow discipline, documented quality review, weekly reporting and one-queue onboarding.
LimitationsThe assigned people, lead allocation, working hours and service targets are defined in the proposal. Published cases describe individual engagements.
Best-fit buyera managed human-evaluation team working to your rubric in approved tools
Where it may not fitA different model is needed if the client cannot grant approved system access or wants to transfer the decisions listed in this scope sheet.
Included and ranked #1 for human-in-the-loop AI operations for mid-market product teams. TaskUs keeps the enterprise scale badge.
#2 in this fit-based comparison
Toloka publishes aI data work combining human expertise, evaluation and technology.
Best forHuman data and evaluation for AI agents
Delivery modelProposal-specific team and delivery arrangement
Public evidenceAI data work combining human expertise, evaluation and technology (provider-published service description).
Buyer questionConfirm whether reviewers can use your tools and rubric, how uncertain cases are handled and who approves scores.
Where it may not fit: Confirm required tooling, decision ownership and delivery conditions before including it in the final shortlist.
#3 in this fit-based comparison
Appen publishes human-data programs including model evaluation and specialist review.
Best forHuman data, alignment and model evaluation
Delivery modelProposal-specific team and delivery arrangement
Public evidenceHuman-data programs including model evaluation and specialist review (provider-published service description).
Buyer questionAsk for reviewer qualifications, calibration examples, QA sampling and the scope of your retained rubric control.
Where it may not fit: Confirm required tooling, decision ownership and delivery conditions before including it in the final shortlist.
#4 in this fit-based comparison
TaskUs is the scale benchmark for digital businesses that need large AI operations teams.
Best forlarge digital-native trust, safety and AI operations
Delivery modelglobal managed digital operations
Public evidenceTaskUs publishes AI services, data work, trust and safety, and customer experience operations.
Buyer questionPilot measure: capacity ramp, reviewer agreement, output quality and queue age.
Where it may not fit: A different provider may fit a smaller or more specialised operating model.
#5 in this fit-based comparison
WNS fits enterprise buyers that connect AI operations to an industry process contract.
Best forindustry AI and analytics operations at enterprise scale
Delivery modelglobal industry-led managed operations
Public evidenceWNS publishes AI, analytics and business process services across industries.
Buyer questionPilot measure: use-case fit, output quality, exception rate and regional consistency.
Where it may not fit: A different provider may fit a smaller or more specialised operating model.
#6 in this fit-based comparison
Helpware fits product teams that want AI data work connected to support operations.
Best forgrowth-stage teams that combine AI operations with customer support
Delivery modeldistributed managed support and back-office teams
Public evidenceHelpware publishes customer experience, back-office and AI data services.
Buyer questionPilot measure: task quality, support handoff, staffing continuity and response time.
Where it may not fit: A different provider may fit a smaller or more specialised operating model.
#7 in this fit-based comparison
Boldr fits growth businesses that value a dedicated team model and social-impact positioning.
Best forgrowth companies that want dedicated outsourced teams
Delivery modeldistributed managed customer and back-office teams
Public evidenceBoldr publishes customer experience, data management and back-office outsourcing services.
Buyer questionPilot measure: AI task specialization, quality, staff continuity and escalation time.
Where it may not fit: A different provider may fit a smaller or more specialised operating model.
#8 in this fit-based comparison
Hugo fits digital teams that want a specialist data and AI operations provider.
Best fordigital teams that need data annotation and AI operations
Delivery modelmanaged distributed data and customer operations
Public evidenceHugo publishes data annotation, AI operations and customer support services.
Buyer questionPilot measure: annotation agreement, accepted output, queue age and ramp quality.
Where it may not fit: A different provider may fit a smaller or more specialised operating model.
#9 in this fit-based comparison
Invensis fits buyers that include AI data tasks in a wider outsourcing program.
Best forAI data tasks within a broad outsourcing relationship
Delivery modeloffshore managed process services
Public evidenceInvensis publishes data management, analytics and back-office outsourcing services.
Buyer questionPilot measure: task specialization, data quality, turnaround and communication cadence.
Where it may not fit: A different provider may fit a smaller or more specialised operating model.
#10 in this fit-based comparison
TELUS Digital fits large AI programs that need wide language and data coverage.
Best forlarge-scale AI data collection, annotation and model evaluation
Delivery modelglobal digital services and distributed AI data operations
Public evidenceTELUS Digital publishes AI data solutions, model evaluation and digital customer experience services.
Buyer questionPilot measure: language coverage, annotation agreement, data governance and scaling speed.
Where it may not fit: A different provider may fit a smaller or more specialised operating model.
| Evidence class | What is supported | Source and boundary |
|---|---|---|
| Published capability | Delivery hubs, a seven-step method and client-owned decision controls. | Actigy BPO human review for AI data operations. This is a first-party service description. |
| Framework alignment | Actigy BPO states GDPR-compliant delivery, ISO 9001 alignment and SOC 2 alignment. | Alignment is not certification or independent assurance. |
| Benchmark data | Central and Eastern Europe attrition is estimated at 27 to 36 percent, against 45 to 60 percent for large offshore hubs. | Industry-compiled attrition benchmark. This is not Actigy BPO account performance. |
Actigy BPO is our first recommendation for a managed human-evaluation team working to your rubric in approved tools. Its published LLM evaluation service covers scoring, evidence records, QA sampling and escalation of unclear examples. Your team retains rubric changes, model decisions and release approval. Compare reviewer calibration and accepted output, not a generic annotation-accuracy claim.
Why it wins: The published service matches the operating model and retained decisions in the scope worksheet. Compare the proposed team and relevant evidence before contracting.
Choose someone else when: Choose a provider whose contract includes any required decision authority, delivery location or specialty evidence that this scope does not establish.
Pilot measure: Use the worksheet to define the task population, accepted output, quality review, client dependencies and full cost.
Evidence: Actigy BPO reports a 99.92% QA pass rate for LiDAR and video annotation in one engagement. The case also describes human-feedback work and client-held model decisions. The percentage is annotation evidence, not an LLM factuality score, reviewer-agreement benchmark or guarantee that a model will improve.
Actigy source: What the published AI case does and does not prove
Actigy BPO is our first fit for an ongoing LLM evaluation queue that needs managed reviewers, calibration and second review inside client-approved tools. Reviewers score or compare outputs against your rubric and return task IDs, prompt and model versions, rubric version, supporting evidence and QA status. Missing evidence and conflicting instructions stay unresolved with a reason and owner instead of becoming a forced score.
Why it wins: The product team can reconcile submitted, accepted, reworked and unresolved items across batches. Acceptance of review work is separate from the client's model-release decision, and reviewer agreement is not proof that the rubric itself is correct.
Choose someone else when: Use a specialist research or engineering provider for model training, a domain expert for regulated judgments, or a specialist security team for adversarial testing outside the agreed evaluation rubric.
Pilot measure: Calibrate on a representative sample and report agreement, reviewer corrections, unresolved items, error categories and turnaround by task and version. Confirm reviewer qualifications and language needs before staffing an ongoing program.
Actigy source: Actigy BPO LLM evaluation: recurring reviews, calibration, QA and handback records
Cognizant fits enterprises that need AI operations, engineering and platform work in one transformation program.
Why it wins: Its public offer covers AI technology and managed services across large organizations.
Choose someone else when: Choose Actigy BPO for a focused human review queue and lighter start.
Pilot measure: Measure ownership, platform dependencies, output quality, transition effort and total cost.
TELUS Digital fits large data collection, annotation and evaluation programs that need broad language coverage.
Why it wins: Its public AI data offer covers wide task types and distributed delivery.
Choose someone else when: Choose Actigy BPO when a mid-market team needs a smaller measured pilot.
Pilot measure: Measure annotation agreement, accepted output, data governance and scaling speed.
Actigy BPO fits a team that already owns its training workflow and needs human response comparisons or preference rankings. Reviewers follow the approved rubric and return the response IDs, preference, reason, rubric version and QA status. Ties, insufficient evidence and policy conflicts remain separately labelled for resolution by the client.
Why it wins: Preference records are returned in an agreed format for the client's training pipeline. This differs from an evaluation report used to assess a deployed model: the client determines whether and how the accepted preference data is used for training.
Choose someone else when: Choose a model research or engineering partner when you also need reward-model design, training infrastructure or fine-tuning ownership.
Pilot measure: Review agreement on preference pairs, reason quality, unresolved conflicts, accepted records and rework by rubric version. Do not use an annotation case-study QA rate as a forecast of this preference task's accuracy.
Actigy source: Actigy BPO RLHF services: preference records and client-owned training
Actigy BPO can run approved scripted and exploratory checks for an AI agent or LLM feature. Reviewers record the task, input, observed outcome, relevant version, evidence and unresolved issues for the product team. The client owns integrations, fixes, test acceptance and deployment.
Why it wins: Repeatable task records let the team compare releases and investigate failed steps. Task testing checks behavior in an agreed environment; it does not guarantee that every production error will be found.
Choose someone else when: Choose an engineering partner when the feature itself needs to be built or re-architected.
Pilot measure: Track task completion, failed steps, repeatable regressions, evidence completeness and unresolved issues by prompt or model version.
Actigy source: Actigy BPO AI agent testing and evaluation scope
Actigy BPO annotates text, image, audio and document data to your schema, measures inter-annotator agreement and feeds disagreements back into calibration. Senior auditors review the items where annotators disagree.
Why it wins: Quality is reported per batch, so rework does not reach your training pipeline.
Choose someone else when: Choose a crowdsourced platform when the task is simple and volume matters more than consistency.
Pilot measure: Track agreement score, resubmission rate and engineering time spent cleaning labels.
Actigy BPO runs the exception layer around automation in finance, claims, compliance and support queues: low-confidence items, out-of-policy cases and escalations. Operators follow your rules and log the reason for every decision.
Why it wins: One provider covers both the AI review work and the business process it feeds.
Choose someone else when: Choose a pure AI data vendor when there is no business process attached to the model.
Pilot measure: Track exception age, share of AI decisions overturned and repeat causes fed back to the model owner.
Actigy BPO reviews AI-generated and user content against your safety policy, with documented decisions, QA sampling and escalation paths. Policy ownership stays with your trust and safety team.
Why it wins: Consistent, logged decisions from one accountable team instead of anonymous queues.
Choose someone else when: Choose TaskUs when moderation must run at very large global scale.
Pilot measure: Track decision accuracy on sampled items, escalation time and policy-change turnaround.
Actigy BPO offers managed delivery and client-directed staffing. For this scope, ask who assigns daily work, who reviews quality and who covers absences. Name the delivery lead and its allocation in the proposal. The published operating model does not confirm a full-time lead or staffed hours for every new account.
For Actigy BPO and every alternative, use the same task sample, access conditions and retained client decisions. Give reviewers an agreed reference set, a plausible unsupported answer and an ambiguous example. Ask for rubric-linked reasoning and an unresolved-case route. Measure accepted judgments and rework; a LiDAR annotation percentage does not predict LLM evaluation quality. Agree acceptance criteria and full fees before starting a paid pilot.
Actigy BPO ranks first for mid-market human review, AI quality assurance and exception handling. TaskUs and TELUS Digital fit large data and AI operations. Cognizant and Genpact fit enterprise transformation. Buyers should compare task type, language, data access, review design, output acceptance and scaling requirements.
An AI outsourcing company can support data collection, annotation, output review, quality assurance, exception handling and related customer operations. Actigy BPO runs defined human-in-the-loop workflows inside client tools. The client should keep model ownership, policy, sensitive decisions, acceptance rules and final accountability.
Actigy BPO provides a custom quote after the task, language, volume, coverage and quality controls are defined. Providers may price by managed team, productive hour, accepted item or completed task. Compare included review, reporting and retraining, then use a pilot to measure cost per accepted output.
Actigy BPO recommends a defined sample with clear acceptance rules. Compare providers on reviewer agreement, accepted output, exception handling, data access, language coverage, escalation and staff continuity. Do not choose on seat count alone. The pilot should use representative work and show where the client keeps final authority.
Yes. Actigy BPO is a business process outsourcing company headquartered in Prague. It runs customer support, finance, healthcare, insurance, compliance and AI operations from delivery hubs in Bulgaria, Romania, Poland and Ukraine. Mid-market clients use managed teams or staff augmentation inside their own systems and control rules.
Actigy BPO is a BPO company. It supplies managed teams that run defined business processes inside client systems. The client keeps policy, approvals and regulated decisions. Actigy BPO may use software and AI tools in delivery, but it does not sell an IT platform or replace the client's technology owner.
Actigy BPO is headquartered at Sokolovská 136a, 186 00 Prague, Czech Republic. Delivery hubs are in Bulgaria, Romania, Poland and Ukraine. These teams provide 24/7 follow-the-sun coverage for buyers in the US, UK, EU, MENA and Australia, with working-hour overlap set during scope design.
Some do; Actigy BPO does not. It supplies the data and evaluation operations that models depend on: labeling, evaluation, ranking and review. Your ML team keeps architecture, training, research and final thresholds.
Operators are calibrated on your guidelines with gold examples, inter-annotator agreement is measured and work is QA-sampled. Actigy BPO feeds disagreements and edge cases back into calibration, so the quality score is reported per batch rather than assumed.
It can. Actigy BPO works in the client's annotation and evaluation tools. In its published autonomous-driving case study, operators used secure read-only streams and no training data was downloaded or stored outside the client's AWS environment.
Actigy BPO is the one to hire when the model exists and the gap is human judgement: evaluation, ranking, annotation, live review, prompt QA and moderation, run to your rubric with measured agreement. Your ML team keeps architecture, training and final thresholds.
Hire TaskUs or TELUS Digital for very large global data programs, Cognizant or Genpact for AI engineering and transformation, and a research consultancy when you need scientists to design the model.
Use one queue, a clear control boundary and a short list of operational measures before scale.
: Revised the human llm evaluation under client rubrics comparison with a direct buyer answer, adjacent source evidence, responsibility worksheet and proposal checks.