AI strategy and use case discovery.
Assess a workflow, identify practical opportunities and define the inputs, integrations and success criteria for a pilot. Establish the current baseline before selecting a model or implementation approach.
Services · AI, data and automation
Connect the intelligence in your data to the work of your business.
Businesses generate information through documents, transactions, media, conversations and operational systems. Its value depends on whether people can find it, understand it and use it at the right moment. AI becomes more useful when that information has a clear structure and a defined role in the workflow.
What we make.
UnboxedAI brings applied AI, data integration and workflow development together around specific business tasks. We begin with the process and its inputs, assess what the data can support and scope an implementation that can be evaluated. The aim is useful work: clearer access to information, more consistent reporting and fewer repetitive steps.
Generative AI and knowledge workflows.
Develop scoped assistants, summarization and draft-generation workflows using approved information sources. Retrieval-based approaches can be assessed where answers need to connect to a defined document collection.
Document, audio and media intelligence.
Turn suitable documents, recordings and broadcasts into searchable information, structured records and reviewable reports. Combine extraction, transcription and human review around the intended use.
Data engineering, big data and analytics.
Assess data sources, volumes, quality and processing needs. Scope integration, transformation, reporting and analytical workflows that support decision-making and prepare information for AI.
Workflow automation and integrations.
Connect systems and reduce repeated handoffs through defined actions, approvals and notifications. Select conventional automation or AI according to the nature of each step.
AI assistants, enterprise search and document intelligence.
Build assistants and search experiences around approved information sources. Connect extraction, classification, retrieval and draft preparation to business workflows with defined permissions and evaluation.
Computer vision and predictive intelligence.
Develop image and video analysis, forecasting, anomaly detection and decision-support workflows. Establish suitable data, evaluation criteria and deployment requirements around the business question.
Data engineering, big data and analytics
Build a more useful foundation for decisions and AI.
Operational data is often spread across applications, documents and disconnected reporting processes. Before it can support dependable analysis or AI, teams need to understand its sources, meaning, quality and access requirements. Data work starts with those foundations rather than the choice of a platform.
UnboxedAI connects data integration, engineering, reporting and analytical development. Engagements can range from a focused dashboard or information workflow to data platforms, streaming pipelines and distributed processing. The architecture follows the volume, frequency, complexity and business purpose of the information.
Capabilities
Data assessment and architecture.
Identify the source systems, data ownership, quality issues, access constraints and processing needs. Define what information the intended decision or AI workflow actually requires.
Data integration and pipelines.
Scope extraction, transformation and movement between agreed systems. Specify validation, refresh frequency, failure handling and operational ownership.
Business intelligence and dashboards.
Translate operational questions into defined measures, filters and reporting views. Connect the dashboard to the decisions its users need to make and document how each measure is calculated.
Data preparation for AI.
Prepare suitable documents and structured information for retrieval or model workflows. Assess permissions, metadata, search quality and refresh processes before adding an assistant.
Big data processing.
Assess batch or streaming requirements, storage choices and distributed processing only when the use case and scale justify them. Benchmark representative data before making throughput commitments.
Predictive analytics.
Develop forecasting, anomaly-detection and analytical models that support planning and operational decisions. Define the target outcome, prepare representative data and evaluate model behavior under the intended operating conditions.
Business value
The intended result is a clearer and more reusable view of business information: consistent measures, less repeated report preparation and data that can support future application or AI needs. Establish these benefits against a baseline and assess them through the initial scope.
Deliverables
Deliverables can include a source and data-flow map, data definitions, pipelines, analytical views, predictive models, evaluation results and operating documentation. Integration, scale, performance and ongoing ownership are defined for the project rather than tied to a fixed product template.
Generative AI and agentic AI.
Generative AI solutions
Create, understand and transform information around your business workflows. We develop generative AI applications for document preparation, enterprise knowledge, content production, image and audio workflows, and personalized digital experiences. The solution connects suitable models with your information, interfaces and review process so the output has a defined purpose and a clear route into everyday work.
Capabilities include retrieval-augmented generation (RAG), enterprise search, document extraction and summarization, multimodal applications, prompt and context design, image and video workflows, and model evaluation. Model adaptation or fine-tuning can be assessed where the task, data and expected benefit justify it.
Relevant technologies: OpenAI model APIs; Claude; Gemini; suitable Llama, Qwen and DeepSeek models; Hugging Face; FLUX and other visual models; ElevenLabs.
Agentic AI and connected workflow automation
Connect AI reasoning with the tools and steps needed to complete a defined task. We design assistants and agent workflows that can retrieve information, plan a sequence, call approved functions and pass work between systems. Applications can support enquiry qualification, document processing, knowledge retrieval, operational coordination and reporting.
Capabilities include tool and API integration, workflow orchestration, retrieval, task routing, state and memory design, human approval steps, exception handling, evaluation and monitoring. Multi-agent coordination can be used when a brief benefits from distinct task roles. Autonomy is scoped to the task, with clear permissions and escalation behavior.
Relevant technologies: LangChain / LangGraph; n8n; supported OpenAI, Claude and Gemini interfaces; ElevenAgents for voice-led interactions; custom Node.js or Python orchestration.
Generative AI produces or transforms outputs; agentic systems coordinate steps and actions. They can be combined in one solution, but each has a different role.
What you get.
The business value depends on the task. A pilot may assess information retrieval quality, reporting effort, processing time or review cost. Those measures provide a basis for deciding whether the solution should be expanded.
Deliverables can include a use-case brief, data-flow map, focused pilot, evaluation results, integration requirements and operating documentation.
The selected data and AI capabilities are established in discovery.
Relevant work.
Unboxed Insight provides a relevant example of AI-supported media reporting and candidate detection. Communication prototypes show areas for further validation rather than established production deployments.
Unboxed Insightlive
Daily media reports and radio ad evidence. A person plays back and confirms each detection.
WaSightprototype
Summarises group chats, transcribes voice notes and answers questions from recent messages.
One Lingoprototype
Voice translation with bilingual chat, push-to-talk and call modes.Need the AI to stay on known machines? See Private AI.
Illustrative screens from demo data, composed with AI.
How we work
AI proof of concept
Test a focused use case using representative examples and agreed measures for quality, usefulness and operating cost.
4-6 weeksFixed-price project
Deliver an agreed application, website, film or visualization package against defined deliverables and review stages.
Set in the quoteSupport and care
Maintain the agreed system or website through defined hosting, updates and ongoing change support.
MonthlyTools and technologies relevant to your project.
AI creates value when the model, workflow and delivery method fit the business need. Our service approach brings together AI-assisted development, language and multimodal models, voice technology, generative production and private deployment options. The technology selection is guided by the task, available data, integration requirements and quality expected from the result.
- OpenAI
- Claude
- Google Gemini
- Hugging Face
- PyTorch
- ElevenLabs
The ecosystem below identifies relevant technologies for project selection and integration. It is not a declaration that every listed platform is currently deployed by UnboxedAI. Final choices are defined within each engagement. Logos identify technologies only and remain the property of their owners. No partnership, certification or endorsement is implied.
See the full list
AI models and machine learning
- OpenAI models and APIs Hosted model ecosystem
- Language, multimodal, embedding, image and speech model options for application integration. ChatGPT is the user-facing workspace; APIs connect supported model capabilities to custom systems.
- Anthropic Claude models Hosted model family
- Language, reasoning and tool-use options for assistants, document workflows and knowledge applications. Evaluate the specific model against the intended task.
- Google Gemini Multimodal model family
- Supports tasks spanning language and other supported input types. Relevant to information extraction, assistants, content analysis and connected applications.
- Hugging Face Model and machine-learning ecosystem
- Supports discovery and evaluation of models and related libraries. Relevant to model selection, experimentation and integration; each model has its own terms and requirements.
- PyTorch Machine-learning framework
- Supports custom model development and inference workflows. Relevant to predictive analytics, computer vision and specialist model work where appropriate data is available.
- TensorFlow Machine-learning framework
- Another option for model development and deployment workflows. Framework choice follows the model, application environment and operating requirements.
- LangChain / LangGraph Application orchestration frameworks
- Help structure model calls, tools, retrieval and agent workflows. Relevant when a solution requires controlled multi-step behavior and connections to business systems.
- n8n Workflow automation platform
- Connects applications, events and AI steps. Relevant to lead handling, document routing, reporting and operational workflows with defined review and exception handling.
Voice, conversation and multilingual experiences
- ElevenLabs Voice and audio AI platform
- Relevant to voice-over, speech generation, transcription, dubbing, audio production and conversational agents. Connect capabilities to approved scripts, business workflows and review steps.
- Eleven voice models and Scribe Speech-generation and transcription models
- Voice model families support different quality and latency needs; Scribe supports speech-to-text. Select models around the language, audio conditions and intended experience.
- ElevenAgents Conversational agent platform
- Relevant to customer enquiries, information assistants and appointment workflows. Define knowledge sources, permitted actions, handover behavior and evaluation before deployment.
- OpenAI speech and realtime models Speech and conversational model options
- Relevant to transcription, audio interfaces and real-time voice applications where the selected API supports the required flow. Specify latency, turn-taking and system integrations.
- Gemini multimodal capabilities Multimodal application models
- An option for applications combining language with supported audio and visual inputs. Model and interface selection follows the required interaction and evaluation scope.
Data processing
- Apache Spark Distributed data processing
- An option for large-scale batch and analytical processing where the workload justifies distributed execution.
- Apache Kafka Event-streaming platform
- An option for event-driven data movement and streaming architectures with defined reliability and ownership requirements.
- PostgreSQL / MySQL Relational databases
- For structured business records, application data and reporting. Select schemas, migration methods, access patterns and backup arrangements around the system.
How we select and integrate AI
We define the task and success criteria, compare suitable models and tools, and test representative inputs before selecting an implementation. The assessment covers output quality, integration needs, data handling, hosting, latency and operating cost. Human review, system testing and clear responsibility remain part of delivery.
Explore capabilities across our services.
Explore integrated capabilities within our six core services, from brand and launch communication to interactive experiences, digital engineering, voice and simulation.
Brand and campaigns
Develop a clear identity, practical brand guidance and launch assets that connect the product to its market.
Games and interactive
Create browser experiences, playable concepts and interactive product demonstrations around a defined audience and purpose.
3D CAD and print
Develop product models, CAD geometry and print-ready outputs with agreed references, dimensions and file requirements.
Voice and dubbing
Support communication through voice-over, transcription, subtitles and dubbing. Language coverage and review are established for the brief.
Simulation and virtual environments
Develop aviation, medical, industrial and scenario-based simulations for training, analysis and decision support. Combine interactive environments, computational models and visualization around the intended users and objectives.
Questions.
Should the data be organized before adding AI?
Often yes. Source quality, permissions and structure affect how reliably the workflow can operate.
Do you provide big data platforms of any scale?
Scale is assessed from the data volume, update frequency, processing needs and operating requirements. Architecture and capacity are confirmed before a commitment.
What should we unbox next?
Tell us what you want to improve, build, test or launch. We will help identify the requirements and a suitable starting point.
Discuss an AI or data opportunity