Make document-heavy work faster and more reviewable
Find evidence across internal documents, prepare recurring content and keep sources visible for human review.
Independent AI, data science & statistical consulting
Building practical AI for ambitious businesses
Binastel helps SMEs and research-led organisations assess AI opportunities, automate document-heavy work, improve forecasts and make better decisions from complex data—without unnecessary enterprise complexity.
- Independent judgement
- Proportionate methods
- Validation and clear uncertainty
01 / Practical problems
Technical depth, directed towards a useful decision.
Binastel primarily helps organisations with complex data, document-heavy work or analytical decisions that do not need—or do not yet have—a large in-house specialist team.
Where the work begins
Start with the decision or workflow—not a preferred technology.
Plan with a clearer view of demand and capacity
Use historical data, honest uncertainty and practical scenarios to support forecasting and operational decisions.
Allocate limited resources more effectively
Model real constraints to improve staff, equipment, stock, budget or project scheduling decisions.
Evaluate whether a change genuinely works
Design reliable experiments or suitable alternatives before investing in a wider rollout.
Starting engagements
What you can hire Binastel to deliver
Four focused ways to begin, each scoped around a defined question, practical deliverables and explicit evaluation.
Document intelligence and reporting automation
Build a reviewable AI-assisted workflow that finds evidence across internal documents and helps prepare proposals, reports, technical responses or other recurring content. Begin with one defined workflow, evaluate retrieval quality and source accuracy, and establish a practical route towards wider implementation.
Potential deliverables
- Document ingestion and preparation
- Semantic search or retrieval-augmented generation
- Source-linked answers and draft content
- Reusable report or proposal templates
- Retrieval and output evaluation
- Human-review workflow
- Implementation and handover documentation
A pilot or implementation engagement. Production hosting, security, authentication, monitoring and ongoing support are agreed separately where required.
Forecasting and planning
Use historical business data to forecast demand, sales, workload, revenue or capacity. Compare appropriate models with simple baselines, quantify uncertainty and turn the results into practical planning scenarios.
Potential deliverables
- Demand, sales, workload or capacity forecasts
- Staffing, inventory and cash-flow scenarios
- Forecasts by product, service, location or customer segment
- Backtesting against simple baselines
- Uncertainty ranges and scenario analysis
- Clear communication of limitations
The approach and useful forecast horizon depend on data quality, coverage, seasonality and the decision being supported.
Resource allocation and scheduling optimisation
Turn operational objectives and constraints into a practical allocation or scheduling model. Evaluate how staff, equipment, stock, budgets or project capacity can be deployed more efficiently.
Potential deliverables
- Staff or technician scheduling
- Consultant-to-project allocation
- Equipment and production scheduling
- Inventory or replenishment decisions
- Budget allocation and project selection
- Scenario comparison and documented model assumptions
For example, assign staff or technicians to jobs based on skills, availability, location, deadlines, capacity and cost, while minimising travel, overtime and missed commitments.
Experiment design and causal evaluation
Design and analyse reliable tests of changes to pricing, communications, products or operational processes. Define the hypothesis, outcome measures, sample requirements and decision criteria before implementation.
Potential deliverables
- Experiment design and hypothesis definition
- Outcome, guardrail and decision metrics
- Sample-size or power analysis
- Randomisation strategy
- Statistical analysis and interpretation
- Decision recommendations
A/B testing requires sufficient customer, transaction or operational volume. Where randomisation is not possible, an appropriate observational or quasi-experimental design may be considered.
Selected work
Evidence behind the work
Binastel’s consulting approach is grounded in peer-reviewed experience applying statistical, machine-learning and computational methods to difficult real-world data.
Comparing the effectiveness of two rheumatoid arthritis treatments
Analysed a nationwide, prospective real-world cohort to compare treatment response between abatacept and adalimumab and examine whether genetic differences altered their relative effectiveness.
Identifying treatment-response biomarkers using machine learning
Used whole-blood transcriptomic data, machine-learning classifiers and targeted statistical analysis to investigate biomarkers associated with treatment response in rheumatoid arthritis.
Working approach
Independent judgement from question to handover.
Methods are selected to match the decision. Assumptions, limitations and uncertainty remain visible, with validation designed around realistic use.
See the full approach01Understand the decision
02Assess data and feasibility
03Define success and boundaries
04Build the smallest credible solution
05Validate results and failure modes
06Document, communicate and hand over
About the consultant
Scientific rigour. Technical fluency. Commercial clarity.
Dr Chuan Fu Yap is an AI, data science and statistical consultant with a research background in computational biology and bioinformatics. He applies scientific validation, reproducible computation and statistical reasoning to commercial, technical and research-led problems.
- Peer-reviewed machine-learning and treatment-effectiveness research
- Clinical, genomic and transcriptomic data experience
- Reproducible analytical workflows
- Machine learning, statistical modelling, NLP and scientific computing
What types of organisations do you work with?
Binastel primarily works with SMEs and research-led organisations that have complex data, document-heavy work or analytical decisions but do not need—or do not yet have—a large in-house specialist team. Specialist experience includes biotechnology, healthcare, universities and organisations working with complex scientific or regulated data.
What does an initial engagement look like?
Most work begins with a focused scoping discussion or feasibility review. The question, available data, constraints, deliverables and decision points are agreed before implementation begins.
Can Binastel build and deploy AI systems?
Depending on the project, Binastel can provide feasibility assessment, prototyping, implementation, evaluation, documentation and technical handover. Production hosting, security, integration and ongoing support requirements are agreed explicitly as part of the project scope.
What data is needed for forecasting?
Useful forecasting normally requires a consistent history of the outcome being forecast, relevant dates and any important explanatory factors. Data quality, coverage, seasonality and the decision horizon are assessed before a model is recommended.
When is A/B testing appropriate?
A/B testing is most useful when there is sufficient customer, transaction or operational volume and a change can be applied consistently to comparable groups. Where randomisation is not feasible, alternative evaluation designs can be considered.
Start a conversation
Have a decision, workflow or analytical problem worth improving?
Share what data or documents are available, how the work is handled today and what a useful outcome would change.