Knowledge workers reclaiming hours
An internal copilot that answers questions about contracts, policies, technical manuals. Employees stop searching shared drives and start asking.
§ 01 / Area
Applied AI
Artificial Intelligence.
We build models, conversational agents and copilots embedded in your processes. From idea to production.
60%
Reduction in helpdesk response time
8 wks
Average time-to-value for PoV
12+
LLMs mastered
§ A
Manifesto
Artificial intelligence isn't a product you buy, it's a capability you build. Mobox designs AI systems that learn from your domain, respect your constraints and deliver measurable value from the very first quarter.
We work with frontier LLMs, computer vision, predictive models and autonomous agents. The common thread: every solution must be inspectable, governable and integrated into existing processes — not a technological island.
No demos that shine and then die. We build for production, with continuous evaluation, cost monitoring and clear ownership of who maintains what.
§ B
The 4 capabilities
01.01
LLM & RAG
Conversation, search and reasoning over your company's data.
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01.02
Autonomous Agents
Software that reasons, decides and acts on your systems.
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01.03
Computer Vision
We teach machines to see, count and inspect.
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01.04
MLOps
ML models in production, governed and reliable.
Read more →
§ C
Typical scenarios
Customer service that scales without hiring
Conversational agents handling first-line tickets across email, chat and voice, escalating only the cases that need empathy or human judgement.
Model-driven operational decisions
Demand forecasting, churn prediction, lead scoring, predictive maintenance. Models embedded in existing systems, not in separate dashboards.
§ D
Methodology
M1
AI readiness assessment
We map available data, candidate use cases and organisational maturity. We tell you what's ready to go and what isn't.
M2
Proof of value
One use case, six weeks, KPIs declared upfront. If the numbers don't add up, we stop.
M3
Industrialisation
MLOps, continuous evaluation, guardrails, cost and quality monitoring. The model enters production with clear SLAs.
M4
Scaling & governance
From the first use case to the company's AI portfolio, with an internal AI Center of Excellence and approval processes.
§ E
Stack & framework
GPT-4ClaudeLlama 3Azure OpenAIAWS BedrockVertex AILangChainLlamaIndexPyTorchHugging FacePineconeWeaviateMLflow
§ F
Synergies with other areas
Big Data
Without quality data there is no AI. Big Data pipelines feed training and retrieval.
Cybersecurity
AI models in production must be protected against prompt injection, model extraction and data leakage.
Automation
AI makes the decisions, automation executes them. Together they close the loop.
§ G
Frequently asked questions
Q/01How long until the first AI use case is in production?+
Typically 8–12 weeks from kickoff. The initial PoV is 6 weeks; industrialisation adds another 4–6 depending on integration.
Q/02Can we avoid sending our data to OpenAI?+
Yes. We work with open-source models (Llama 3, Mistral) on-premise or in private cloud, or with dedicated Azure OpenAI instances where data never leaves your tenant.
Q/03How do you handle hallucination risk?+
RAG architectures with mandatory source citation, output guardrails, automated evaluation on golden-answer datasets and fallback to a human operator on low-confidence cases.
Q/04Do we need an internal data scientist team?+
Not to start. Yes to scale. We build the first solutions and progressively train the internal team, all the way to full handover if desired.
Next step
Let's talk about artificial intelligence.
A 30-minute call to understand your context. No commitment.