language-ai-and-data
Custom machine translation and post-editing
Custom machine translation and post-editing services focus on building and operating translation workflows that combine domain trained models with professional human review. Providers start by analyzing existing bilingual or multilingual corpora from an organization, such as manuals, support content or legal templates, and use these materials to adapt machine translation engines to specific terminology and style. Engines can be statistical, neural or part of a broader language platform, but in all cases they are configured and tested on representative samples. Output is then checked and corrected by trained linguists who follow defined quality levels, ranging from light post-editing for internal use to full editing for publication. Terminology management and style guides are applied consistently across projects. Measurement frameworks track productivity, error types and correction effort so that models and processes can be refined over time. This combination helps organizations shorten turnaround times for large volumes while maintaining control over accuracy and tone in their main working languages.
custom-machine-translation-and-post-editing
Multilingual chatbots and voicebots with retrieval
Multilingual chatbots and voicebots with retrieval capabilities provide automated assistance across several languages by combining conversational interfaces with access to structured knowledge sources. Text or speech input is recognized, analyzed and mapped to intents or questions, and the system retrieves relevant passages from documentation, product data or policy repositories. Responses are generated or assembled in the user’s language, often with mechanisms that control terminology and prevent unsupported statements. For voice channels, speech recognition and synthesis components are configured for each language, and telephony or messaging platforms are integrated for delivery. In regulated sectors, logging and configuration options allow organizations to document how responses were generated and constrain access to sensitive information. Training data may include real customer interactions, but it is processed to comply with privacy policies. These systems help reduce response times in service desks and information portals while still allowing escalation to human staff for complex or exceptional cases in any supported language.
multilingual-chatbots-and-voicebots-with-retrieval
LLM fine-tuning, RLHF and safety evaluation
LLM fine-tuning, reinforcement learning from human feedback and safety evaluation services concentrate on adapting large language models to specific domains, policies and multilingual contexts. Providers gather representative prompts, documents and interaction patterns from a client environment and use them to guide model behavior, either through supervised fine-tuning or preference based methods. Human reviewers label responses according to quality, faithfulness to source materials and compliance with organizational or regulatory rules. Safety evaluation covers topics such as the handling of harmful content, sensitive personal information and instructions that should be declined, with particular attention to how these issues appear in different languages and scripts. Test suites and benchmark tasks are built to monitor changes over time when models or prompts are updated. Documentation explains configuration choices, limitations and residual risks, so decision makers understand how the system behaves. This work helps organizations deploy language models that support their objectives while aligning with internal governance and external expectations.
llm-fine-tuning-rlhf-and-safety-evaluation
Language data collection and annotation
Language data collection and annotation services provide the text and speech datasets that language technologies rely on. Projects may include building corpora for under represented languages or specialized domains, where existing resources are limited. Providers design collection protocols that respect consent, privacy and data protection requirements, and they recruit contributors who record speech, transcribe audio or supply written material under clear conditions. Annotation tasks can range from basic segmentation and transcription to labeling parts of speech, entities, intents, sentiment or discourse relations. For speech data, annotators may also describe speaker characteristics, acoustic conditions or pronunciation variants when relevant and permitted. Quality assurance combines training, calibration tasks and systematic review of a sample of annotations. The resulting datasets are suitable for training and evaluating machine translation, speech recognition, classification and other language models. Well designed projects support both technological goals and responsible handling of participant contributions.
language-data-collection-and-annotation
Speech technologies and live translation systems
Speech technologies and live translation systems bring together automatic speech recognition, speech synthesis and translation components to support spoken communication across languages. Services in this area include configuring models for specific languages and acoustic conditions, integrating them with conferencing platforms, call center software or devices, and designing user interfaces that make real time translation practical. For recognition, language models and vocabularies are adapted to domain specific terms so that medical, technical or institutional names are captured reliably. For synthesis, voice characteristics, clarity and latency are tuned to match use cases such as announcements, assistance or dialogue. When translation is added, providers monitor how delays and error patterns affect understanding for participants. Systems can be set up to display transcripts, play synthesized audio or both, sometimes alongside human interpreters who monitor and correct output. These solutions support meetings, training and service interactions where participants do not share a common language but need timely access to spoken content.
speech-technologies-and-live-translation-systems
Cross-lingual NER, OCR and document structuring
Cross-lingual named entity recognition, optical character recognition and document structuring services focus on extracting usable information from multilingual documents at scale. Optical character recognition converts scanned pages or images of passports, forms, reports and other materials into machine readable text, taking into account different scripts and layout patterns. Named entity recognition models then identify people, organizations, locations, financial instruments or other relevant categories across languages. Additional processing groups fields into logical records, such as identifying account numbers, dates or reference codes that appear in different positions depending on the document type and country. Where required, transliteration routines convert names or addresses between writing systems while preserving traceability to the original. Quality checks combine automated confidence measures with human review for critical workflows. These capabilities support compliance checks, search and retrieval, analytics and operational processes that depend on consistent, structured information derived from diverse language sources.
cross-lingual-ner-ocr-and-document-structuring
Human-in-the-loop AV localization automation
Human-in-the-loop audiovisual localization automation combines automated components with professional review to produce subtitles and dubbed audio in multiple languages. The workflow typically starts with automatic speech recognition to create a source transcript and segment it into timed units. Machine translation or adapted language models generate draft subtitles in target languages, taking reading speed and line length into account. For dubbing, synthesized voices or guide tracks may be produced to support casting and recording decisions. Human linguists and technicians then refine text, timing and performance, checking consistency with terminology guidelines and content policies. Quality control includes spot checks, full reviews for high profile content and technical verification of file formats and metadata. Automation reduces repetitive steps and speeds up processing of large catalogs, while human expertise ensures that result meets expectations for clarity, cultural appropriateness and platform specific standards in each language.
human-in-the-loop-av-localization-automation
AI writing assistants for legal and business communication
AI writing assistants for legal and business communication provide structured support for drafting, reviewing and adapting texts such as contracts, correspondence, memos and reports. These tools use language models that have been configured to follow domain specific patterns, for example by suggesting clause formulations that align with organizational templates or by highlighting missing elements in a contract outline. They can help users rephrase content to match defined tone levels, summarize long documents, or generate alternative wording while keeping key information intact. When multilingual capabilities are included, assistants may offer draft translations or rewrites that preserve legal meaning across languages, subject to professional review. Interfaces are often integrated into existing document editors or workflow systems so that users do not need to switch tools. Audit logs, access controls and configuration options allow organizations or individuals to understand and manage how the assistant contributes to the drafting process.