RFP Checklist: Must-Have Requirements for AI Localization

Selecting an AI localization platform is a high-stakes procurement decision, and most RFPs fail because they ask the wrong questions. Generic vendor questionnaires miss the capabilities that separate enterprise-grade platforms from glorified translation APIs: multimodal coverage, quality automation, security posture, and integration depth. This guide gives you a structured, ready-to-issue RFP framework with weighted scoring criteria, pointed vendor questions, and proof-of-concept success benchmarks. Whether you are localizing product documentation, marketing video, in-app strings, or live customer support, the checklist below ensures your evaluation captures what actually matters, and exposes what vendors would rather you not ask about.

If you are already evaluating platforms and want to see how a multi-agent, multimodal localization system handles these requirements in practice, request a walkthrough with the Ollang team.

Why Your AI Localization RFP Needs a Structured Checklist

Enterprise localization has moved well beyond text-only translation. Today's content ecosystems span product UIs, knowledge bases, video tutorials, audio support lines, legal contracts, and live multilingual events, often within the same release cycle. An RFP that treats localization as a single-format problem will surface vendors that solve only a slice of the workflow, forcing you to stitch together point tools, manage multiple contracts, and absorb integration risk yourself.

A structured checklist does three things. First, it levels the playing field by requiring every vendor to respond to the same capability dimensions, making scoring objective. Second, it forces internal alignment: stakeholders from engineering, legal, content, and procurement agree on weights before evaluations begin, not after. Third, it creates an audit trail. When the CISO asks why you chose a particular vendor six months post-contract, you can point to documented scores rather than anecdotal impressions.

The Localization Industry Standards Association (LISA) and its successor bodies have long advocated structured evaluation frameworks for translation technology. The checklist below adapts that principle for the current generation of AI-powered, multimodal localization platforms.

Scope and Modality Coverage

Text, Document, and Software Localization

Your RFP should require vendors to enumerate every content type they handle natively, not through third-party connectors or manual workarounds. At minimum, demand coverage for:

• Structured text: JSON, XLIFF, PO, YAML, Android XML, iOS .strings
• Documents: PDF, DOCX, PPTX, INDD, and Markdown, with layout and formatting fidelity preserved post-translation
• Software strings: resource files pulled from code repositories, with context metadata (screenshots, character limits, pluralization rules)
• Legal and regulatory documents: contracts, terms of service, compliance filings, where terminology precision and formatting integrity are non-negotiable

Ask vendors to demonstrate batch processing of large document sets (hundreds of files per job) and to show how they handle complex page structures like multi-column PDFs with embedded tables and diagrams. Ollang handles document localization across these formats within a single system, maintaining layout fidelity without requiring manual desktop publishing cleanup, a critical efficiency gain for teams managing high-volume technical or legal content.

Sample vendor question: "Provide three examples of complex document formats (e.g., multi-column PDF with embedded charts) you have localized at scale. Show the source, target, and any layout corrections required."

Video, Audio, and Live Speech Translation

Video and audio localization is where most vendor claims fall apart under scrutiny. Your RFP should distinguish between:

• Subtitling and captioning: SRT/VTT generation, timing synchronization, on-screen text translation
• Voiceover and dubbing: AI-generated or human-narrated audio tracks, lip-sync alignment, speaker diarization
• Audio-only content: podcast episodes, IVR prompts, training recordings
• Live speech translation: real-time interpretation for webinars, customer calls, or multilingual meetings

Require vendors to describe their pipeline end to end: transcription, translation, timing, synthesis, and quality review. Platforms that treat these as separate products, or outsource audio/video to a partner, introduce handoff latency and quality gaps.

Ollang's multi-agent, multimodal system coordinates these steps within one platform, so a video localization job flows from transcription through translated voiceover without manual file shuttling between disconnected tools.

Sample vendor question: "Describe how a 30-minute training video in English is localized into four target languages, including dubbed audio. What is the end-to-end turnaround, and which steps are automated versus manual?"

Quality Assurance Requirements

MQM Scoring, LLM-Based QE, and Human Review Layers

Quality is the dimension where AI localization vendors most frequently over-promise. Your RFP should require vendors to support or align with the Multidimensional Quality Metrics (MQM) framework, the industry standard for granular translation error typology. MQM categorizes errors by type (accuracy, fluency, terminology, style) and severity (critical, major, minor), producing a composite score that is comparable across languages and content types.

Beyond MQM, ask about automated quality estimation. LLM-based quality estimation (LLM-QE) uses large language models to predict translation quality without requiring human reference translations, a significant efficiency gain for high-volume pipelines. Vendors should explain how their QE models are calibrated, how often they are updated, and what correlation they achieve against human MQM scores.

Your checklist should also require a clear human-in-the-loop policy: which content types trigger mandatory human review, how reviewers are selected and qualified, and how review feedback loops back into the system to improve future output.

Translation Memory, Glossary Enforcement, and Bias Controls

Translation memory (TM) and terminology management are not legacy features, they are essential consistency mechanisms, especially for enterprises with years of approved translations and strict brand terminology.

Require vendors to explain:

• How previously approved translations are stored, matched, and reused across projects and revisions
• How glossaries and termbases are enforced during AI translation, not just flagged after the fact
• How the platform handles incremental content updates (e.g., a product manual where 90% of the content is unchanged between versions) to avoid re-translating, and re-reviewing, stable segments

Bias and sensitivity controls matter too. Ask how the platform detects culturally inappropriate content, gendered language defaults, or terminology that violates regional norms. This is particularly important for markets with strong regulatory expectations around inclusive language.

Ollang's translation memory and terminology management layer ensures that approved terms and past translations carry forward across documents, revisions, and content types, reducing review cycles and preventing the consistency drift that plagues teams using disconnected AI translation tools.

Sample vendor question: "How does your platform enforce glossary terms during translation, and what happens when a term conflict is detected between the glossary and the AI model's output?"

Integration and Interoperability

CMS, Git, CI/CD, Helpdesk, DAM, and Video Platforms

An AI localization platform that cannot plug into your existing content and engineering infrastructure is a manual bottleneck, no matter how good its translation quality. Your RFP should map your integration requirements across these categories:

Require vendors to distinguish between native integrations (built and maintained by the vendor), partner integrations (maintained by a third party), and API-only access (you build the connector). The distinction matters for support, reliability, and upgrade compatibility.

API Architecture and Automation Capabilities

For engineering teams, the API is the product. Your RFP should require detailed API documentation, ideally a public reference, and should evaluate:

• RESTful or GraphQL architecture, authentication methods, rate limits
• Webhook support for asynchronous job completion, quality alerts, and error notifications
• Batch and streaming modes for high-volume and real-time use cases
• SDK availability for major languages (Python, Node.js, Java, Go)

Ollang provides translation API integration for programmatic, automated localization, so localization fits into existing content, documentation, and release pipelines rather than running as a manual side process. This is the difference between localization as a workflow step and localization as a project.

Sample vendor question: "Provide your API documentation. Show a working example of a CI/CD pipeline that triggers localization on merge to main, waits for quality validation, and commits translated files back to the repository."

Security, Compliance, and Data Handling

SOC 2, ISO 27001, GDPR, CCPA, and HIPAA Requirements

Security is a gate, not a feature. Your RFP should require vendors to provide current certification documentation, not just claim compliance. The baseline for enterprise procurement:

• SOC 2 Type II: Requires an independent audit of controls over a sustained period, not a point-in-time snapshot
• ISO 27001: The international standard for information security management systems
• GDPR and CCPA: Data processing agreements (DPAs), data subject access request (DSAR) procedures, and lawful basis documentation
• HIPAA: If your content includes protected health information, require a Business Associate Agreement (BAA) and evidence of PHI handling controls

Ask vendors to provide their most recent SOC 2 report (or a summary letter), their ISO 27001 certificate with scope statement, and a completed GDPR data processing addendum.

Data Residency, Encryption, and Proof-of-Handling Artifacts

Data residency is increasingly a regulatory requirement, not a preference. Your RFP should ask:

• In which regions is content processed and stored?
• Can you guarantee that content never leaves a specified geographic boundary?
• What encryption standards are applied at rest (AES-256 minimum) and in transit (TLS 1.2+)?
• How is content purged after project completion, and what is the retention policy?

Proof-of-handling artifacts to require in vendor responses:

1. Data flow diagram showing where content is ingested, processed, stored, and deleted
2. Encryption key management policy
3. Subprocessor list with geographic locations
4. Incident response plan summary
5. Penetration test summary (date and scope, not full report)

Sample vendor question: "Provide a data flow diagram for a document containing PII that is submitted for translation. Show every system, service, and subprocessor that touches the data, and the encryption state at each stage."

Governance and Access Controls

Role-Based Access, Audit Logging, and Content Redaction

Enterprise localization involves multiple stakeholders, translators, reviewers, project managers, engineers, legal, and compliance, each of whom should see and do only what their role requires. Your RFP should evaluate:

• Role-based access control (RBAC): Predefined and custom roles, permission granularity (project-level, language-level, content-type-level), and SSO/SAML integration
• Audit logging: Immutable logs of every action, who submitted, reviewed, approved, or modified content, and when. Logs should be exportable and retainable per your compliance requirements
• Content redaction: The ability to automatically detect and mask PII, financial data, or other sensitive content before it enters the translation pipeline, and to restore it in the target output

These controls are not optional for regulated industries. Even outside regulated sectors, audit logs are essential for quality disputes, vendor accountability, and internal compliance reviews.

Sample vendor question: "Describe your RBAC model. Can a compliance officer view audit logs for all projects without having edit access to translation content? Show a sample audit log entry."

If governance and security controls are a priority in your evaluation, see how Ollang's platform addresses these requirements in a live demo.

Performance and SLA Expectations

Latency, Throughput, and Uptime Benchmarks

Performance requirements should be specified in measurable terms, not vague promises. Define your expectations across three dimensions:

Require vendors to disclose their uptime history for the past 12 months, their incident communication protocol, and their SLA remedy structure (credits, escalation paths, termination rights). A platform that cannot commit to measurable SLAs is not enterprise-grade.

For live speech translation, where latency directly affects user experience, require vendors to specify end-to-end delay from spoken input to translated output, including transcription, translation, and synthesis steps.

Sample vendor question: "What was your platform uptime over the past 12 months? Provide incident reports for any downtime exceeding 15 minutes."

Pricing Transparency and Total Cost of Ownership

Opaque pricing is a red flag. Your RFP should require vendors to break down costs across every dimension that affects total cost of ownership:

• Per-word or per-character pricing: Does the rate vary by language pair, content type, or quality tier?
• Platform fees: Monthly or annual subscription, per-seat charges, minimum commitments
• Overage charges: What happens when you exceed contracted volume?
• Integration costs: Are API calls metered? Are connectors included or add-on?
• Storage and retention: Is translation memory storage included? Are there charges for long-term TM retention?
• Support tiers: What is included in base pricing versus premium support?

Ask vendors to price a representative scenario: for example, 2 million words of text, 50 hours of video, and 20 hours of live interpretation per quarter across 10 language pairs. Compare not just unit costs but the total workflow cost, including any manual steps the platform does not automate.

Sample vendor question: "Price the following annual scenario and itemize every line: 8M words of mixed content (docs, UI strings, help articles), 200 hours of video subtitling and dubbing, 80 hours of live event interpretation, 10 target languages, API integration with GitHub and Contentful, dedicated support."

Scoring Rubric and Weighting Framework

Not every requirement carries equal weight. The scoring rubric below provides a starting framework, adjust weights based on your organization's priorities.

Score each vendor on a 1-5 scale per category, multiply by the weight, and sum for a composite score. Require at least a 3 in Security & Compliance and Quality Assurance as a hard gate, no vendor advances regardless of composite score if they fail these thresholds.

POC Success Criteria and Evaluation Timeline

A proof of concept separates marketing claims from operational reality. Structure your POC around these success criteria:

1. Content fidelity: Submit a representative sample across your content types (documents, UI strings, video, audio). Evaluate output quality using MQM scoring with a minimum threshold (e.g., fewer than 5 major errors per 1,000 words).
2. Integration validation: Connect the platform to at least one production system (CMS, repository, or CI/CD pipeline) and run a full round-trip: content extraction, translation, quality check, delivery.
3. Performance under load: Submit a batch job at your expected peak volume and measure throughput and latency against your SLA requirements.
4. Security review: Have your security team review the vendor's SOC 2 report, data flow diagram, and DPA. Conduct a lightweight architecture review.
5. Workflow simulation: Run a realistic project with multiple roles (requester, translator, reviewer, approver) to evaluate RBAC, notifications, and audit logging.

Set a POC timeline of 2-4 weeks, with defined milestones and a go/no-go review at the end. Require the vendor to provide a dedicated POC support contact, not just a self-service trial.

Frequently Asked Questions

How many vendors should we include in an AI localization RFP?

Invite three to five vendors to respond. Fewer than three limits competitive pressure; more than five creates evaluation fatigue without meaningfully expanding your options.

Should we require a proof of concept from every RFP respondent?

No. Use the RFP scoring to narrow to two finalists, then run POCs with both. POCs are resource-intensive for both sides, and running too many dilutes attention and delays decisions.

How do we evaluate AI localization quality without in-house linguists?

Require vendors to provide MQM-scored sample translations as part of their RFP response, and ask for access to their automated quality estimation scores alongside human review results. Platforms with built-in translation quality review, including integrated quality layers that surface errors and confidence scores within the workflow, reduce dependence on external reviewers.

What is the biggest mistake companies make in AI localization RFPs?

Treating localization as a text-only problem. If your content spans documents, software, video, audio, and live events, your RFP must evaluate multimodal coverage as a first-class requirement, not an afterthought.

Next Steps: Issue Your RFP with Confidence

You now have a complete framework, scope requirements, quality criteria, integration checklists, security gates, governance controls, performance benchmarks, pricing templates, a weighted scoring rubric, and POC success criteria. Customize the weights and sample questions to your organization's content mix and regulatory environment, then issue with a clear response deadline and evaluation timeline.

See a Unified, Multimodal Workflow in Action

Want to pressure-test these requirements against a platform built for enterprise-scale, automated localization across text, documents, video, audio, and live speech, with APIs, TM/terminology, and quality review built in? Book a Demo