FACTIVUMStrategy & architecture
Choose a useful starting point. Assess your data, systems and constraints before investing in a build.
Use-case priorities · architecture · delivery planAI engineering.
From data
to deployment.
Build AI around your data, systems and workflows. From a focused assessment to implementation and handover.
Work with your current systems. No platform purchase required.
One connected project
01 / FoundationData & systems
Documents · databases · APIs
02 / IntelligenceRetrieval & models
RAG · fine-tuning · custom ML
03 / WorkApplications & agents
Answers · workflows · reviewed actions
Access controls · evaluation · human oversight
Project architecture, tailored to your needs.Where we can help
Choose the work
your team needs.
Improve an existing AI system or build a new one.
RAG & enterprise search
Connect AI to internal knowledge. Build retrieval with source citations, document permissions and checks for answer quality.
Knowledge pipelines · search · grounded answersModel development & training
Prepare datasets, fine-tune models and develop task-specific ML. Compare performance against a baseline and held-out data.
Data preparation · fine-tuning · distillation · custom MLAI applications & agents
Build assistants and agents around your team's work. Connect APIs and tools, with clear limits and human review for consequential actions.
Assistants · API integration · approval workflowsDocument & multimodal AI
Extract and classify information from documents, images or recordings. Route uncertain results to people for review.
Document processing · extraction · classificationAI security & governance
Test how AI handles hostile inputs and sensitive information. Review data access, tool permissions and oversight; address the gaps.
Threat modeling · adversarial testing · access controlsEvaluation & AI operations
Find out where your AI succeeds and fails. Build repeatable evaluations, trace production behavior and detect regressions.
Evaluation suites · observability · operating runbooksDeployment & cost optimization
Fit AI to your environment. Assess cloud, private or hybrid options and measure the trade-offs in quality, latency and cost.
Deployment architecture · model selection · inference costsWe agree the approach, deliverables and success measures before work begins. Model training starts with dataset suitability and a compute budget.
A practical first step
One clear project.
A useful result.
Assess an existing AI system
Review one use case, establish a baseline and leave with a prioritized improvement plan.
Build a focused RAG pilot
Start with one knowledge collection, defined users, citations and agreed quality checks.
Review security & quality
Test one application or agent workflow. Identify gaps, prioritize fixes and verify the changes.
Define the scope. Build and test. Document the work so your team can maintain it.
Let’s scope the work
What should AI
help you do?
Tell us the outcome you need, the systems involved and what is getting in the way. We’ll review the fit and discuss a defined project.
Improve what you already have.Start with a focused assessment or build.Agree scope and success measures first.