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Technical analysis and feasibility testing
Reduce uncertainty with a measurable prototype.
A demanding idea contains untested technical dependencies, making it difficult to justify investing in full development. Define a testable question, success criteria and a time-limited experiment together. Build the smallest meaningful prototype, record results and recommend continuing, changing direction or stopping. A research prototype requires further work before it becomes a production service.
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When it helps
A demanding idea contains untested technical dependencies, making it difficult to justify investing in full development.
What we do
- Requirements, constraints and solution assessment
- Prototype or proof of concept
- Cost, quality and risk comparison
How it works
Define a testable question, success criteria and a time-limited experiment together. Build the smallest meaningful prototype, record results and recommend continuing, changing direction or stopping. A research prototype requires further work before it becomes a production service.
What you receive
- Prototype and experiment report
- Recommendation and conditions for the next phase
Illustrative example
Illustration: test whether a new processing approach meets the required response time before committing to a complete rebuild.
How we measure success
- Achievement of technical criteria
- Estimated costs and unresolved dependencies
After launch
Handover includes results and reproducible steps. Production readiness, hardening, security, documentation and maintenance form a separately agreed next phase.
Application and database performance
Improvements guided by measured bottlenecks.
An application slows down under load, but the contribution of its database, code and infrastructure is not yet understood. Set representative scenarios and a measured baseline. Test hypotheses one at a time, address the most consequential bottlenecks and repeat measurements. Track resource use and correctness as well as speed, with a clear route to reverse a change.
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When it helps
An application slows down under load, but the contribution of its database, code and infrastructure is not yet understood.
What we do
- Profiling and query assessment
- Load tests in an agreed environment
- Caching, concurrency and improvement validation
How it works
Set representative scenarios and a measured baseline. Test hypotheses one at a time, address the most consequential bottlenecks and repeat measurements. Track resource use and correctness as well as speed, with a clear route to reverse a change.
What you receive
- Bottleneck report and implemented improvements
- Repeatable scenarios and before/after measurements
Illustrative example
Illustration: a report investigation identifies a slow query, then validates an index change against a realistic data volume.
How we measure success
- Response time and throughput
- Resource use and result correctness
After launch
Handover includes results and reproducible steps. Production readiness, hardening, security, documentation and maintenance form a separately agreed next phase.
Mathematical optimisation of schedules and resources
Better decisions within real constraints.
Scheduling employees, vehicles, production or stock involves interacting constraints and repeated manual adjustments. Define the objective and the rules that must always hold. Check input quality, build a model and compare results with the existing process. Make infeasible scenarios visible, explaining their causes and the constraints that would need to change.
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When it helps
Scheduling employees, vehicles, production or stock involves interacting constraints and repeated manual adjustments.
What we do
- Modelling of objectives, constraints and rules
- Optimisation algorithms and scenario comparison
- Explainable results and manual adjustments
How it works
Define the objective and the rules that must always hold. Check input quality, build a model and compare results with the existing process. Make infeasible scenarios visible, explaining their causes and the constraints that would need to change.
What you receive
- Optimisation prototype or integrated tool
- Documented rules and scenario comparison
Illustrative example
Illustration: a weekly schedule respects shifts, employee qualifications and customer order deadlines.
How we measure success
- Objective improvement against the baseline
- Constraint compliance and computation time
After launch
Handover includes results and reproducible steps. Production readiness, hardening, security, documentation and maintenance form a separately agreed next phase.
Simulations and digital twins
Explore consequences before changing the real system.
Changing a process or asset is expensive, while direct experiments disrupt operations or cannot fairly compare scenarios. Define model boundaries, required inputs and the questions to answer. Calibrate against observed measurements and document uncertainty. Plan an actively connected digital twin only when the available sources and expected value justify maintaining that connection.
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When it helps
Changing a process or asset is expensive, while direct experiments disrupt operations or cannot fairly compare scenarios.
What we do
- Process, capacity and event modelling
- Connections to measurements where available
- Scenario, sensitivity and assumption analysis
How it works
Define model boundaries, required inputs and the questions to answer. Calibrate against observed measurements and document uncertainty. Plan an actively connected digital twin only when the available sources and expected value justify maintaining that connection.
What you receive
- Simulation model with assumptions
- Scenario comparison and recommendation
Illustrative example
Illustration: simulate how an additional workstation affects queues and changes the location of bottlenecks.
How we measure success
- Agreement with observed behaviour
- Sensitivity and differences between scenarios
After launch
Handover includes results and reproducible steps. Production readiness, hardening, security, documentation and maintenance form a separately agreed next phase.
Traceability, blockchain and cryptographic solutions
Choose technology around the trust requirement.
Several parties need a verifiable change history, but it is unclear whether a distributed ledger is necessary or appropriate. Compare a conventional database, signed records and a distributed ledger before selecting an architecture. Define identity and key management, costs and privacy limits. Scope advanced cryptographic designs and independent review with the relevant specialists.
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When it helps
Several parties need a verifiable change history, but it is unclear whether a distributed ledger is necessary or appropriate.
What we do
- Assessment of trust, participants and data
- Comparison of signatures, audit trails and blockchain
- Origin and integrity verification prototype
How it works
Compare a conventional database, signed records and a distributed ledger before selecting an architecture. Define identity and key management, costs and privacy limits. Scope advanced cryptographic designs and independent review with the relevant specialists.
What you receive
- Justified architecture and prototype
- Documented assumptions and validation plan
Illustrative example
Illustration: partners verify whether an approved document has changed since signing without publishing its contents.
How we measure success
- Verifiability of record integrity
- Operating cost, response time and manageability
After launch
Handover includes results and reproducible steps. Production readiness, hardening, security, documentation and maintenance form a separately agreed next phase.
Experimental AI, edge deployment and advanced models
Test demanding approaches through bounded research.
A specialist task exceeds established model performance or requires constrained hardware with uncertain quality and cost. Time-limit the research, assess data rights and quality, and establish a baseline model. Measure quality, resource use and costs. Training a foundation model from scratch requires a separate assessment of datasets, specialist team, infrastructure and funding.
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When it helps
A specialist task exceeds established model performance or requires constrained hardware with uncertain quality and cost.
What we do
- Architecture and specialist neural-network comparison
- Experimental training, compression and edge deployment
- GPU experiments, reproducibility and production assessment
How it works
Time-limit the research, assess data rights and quality, and establish a baseline model. Measure quality, resource use and costs. Training a foundation model from scratch requires a separate assessment of datasets, specialist team, infrastructure and funding.
What you receive
- Research prototype and reproducible experiment
- Results, limitations and next-stage decision
Illustrative example
Illustration: compare a vision model running on a server with a version deployed beside the production line.
How we measure success
- Quality on an independent evaluation set
- Latency, memory and computation costs
After launch
Handover includes results and reproducible steps. Production readiness, hardening, security, documentation and maintenance form a separately agreed next phase.
Scope and delivery options
Research delivers measurements and a recommendation, including when a hypothesis is not supported. Mining and cryptographic work is scoped against verifiable technical possibilities.
Technologies and approaches
- Profiling / benchmarks
- Measuring time and resources under comparable conditions.
- Mathematical optimisation
- Choose linear or combinatorial optimisation or heuristics to fit constraints and the problem.
- Simulations
- Testing scenarios on a model with explicit assumptions.
- Blockchain / digital signatures
- Verifiable transactions and cryptographic data verification.
