Most agencies ship pre-built tools with a thin layer of customisation. Our Applied Research team designs, benchmarks, and validates novel approaches — delivering systems that measurably outperform the standard.
Off-the-shelf AI is a starting point, not a solution. For problems where accuracy, reliability, and performance genuinely matter, the difference between a standard implementation and a researched one is the difference between something that sort of works and something you can depend on.
Every system we build is validated against recognised industry benchmarks. We don't estimate performance — we measure it. You get hard numbers, not vague promises.
Our research methodology isn't trial and error. It's a disciplined process: measure the bottleneck, design targeted solutions, validate at scale. Each improvement is evidence-based.
Research that stays in a lab is worthless. Everything we build is designed for deployment — cost-efficient, reliable, and maintainable. Research rigour with production reality.
A five-phase methodology refined through real-world engagements. Each phase builds on the last, ensuring every decision is grounded in evidence, not assumption.
Establish a baseline. We benchmark your current approach (or the best available off-the-shelf solution) against recognised standards so we know exactly where we stand.
Find the real bottleneck. We systematically categorise every failure, rank them by impact, and identify the single highest-leverage point for improvement.
Design targeted solutions. Rather than tweaking the same approach, we develop orthogonal strategies — independent approaches that attack the problem from different angles.
Test at scale, not on cherry-picked samples. We validate across multiple conditions and datasets. Only improvements that hold up under rigorous testing move forward.
Ship with confidence. The validated system is optimised for production — cost-efficient inference, monitoring, and graceful degradation built in from day one.
Real research engagements. Real benchmarks. Real results.
A client needed a knowledge retrieval system that could answer complex questions requiring information from multiple documents — not simple keyword lookup, but genuine multi-step reasoning. Standard RAG (Retrieval-Augmented Generation) implementations were returning incomplete or incorrect answers roughly 30% of the time, particularly on questions requiring cross-document inference.
We benchmarked against HotPotQA — a widely recognised academic benchmark for multi-hop question answering with over 113,000 questions specifically designed to require reasoning across multiple Wikipedia articles.
After 50+ benchmarking runs, we discovered that 60% of failures weren't extraction errors — they were retrieval errors. The system was reasoning correctly over the wrong information. The bottleneck was input quality, not output quality.
Rather than improving one pipeline, we designed three independent retrieval strategies — each attacking the problem from a fundamentally different angle. An information-theoretic approach, a graph-based traversal method, and an optimised baseline. Orthogonal signals with independent error distributions.
A tiered system that routes each query to the most appropriate strategy based on confidence scoring. Fast path for straightforward questions, deep analysis for complex ones. Front-loaded computation at ingestion time keeps query latency low.
"The key insight was counterintuitive: we spent weeks optimising the extraction layer before realising the retrieval layer was the real bottleneck. Once we fixed what we were feeding the model, accuracy jumped immediately."
Build an autonomous trading system for cryptocurrency markets that could make intelligent, risk-managed decisions without human intervention. The system needed to operate 24/7, adapt to changing market conditions, and maintain strict risk controls — all while keeping operational costs under control. Previous rule-based approaches had produced a thin edge (profit factor of 1.06) that was difficult to scale or maintain through regime changes.
We validated against 2.5 years of historical market data across multiple cryptocurrency pairs, covering bull markets, bear markets, and sideways consolidation — over 750 trades worth of evidence.
The rule-based system's edge was real but narrow — a profit factor of 1.06. Error analysis revealed the core limitation: rigid rules couldn't adapt to regime changes. What worked in trending markets failed during consolidation. The system needed contextual awareness, not more indicators.
We designed a multi-strategy architecture combining nine independent approaches — from information-theoretic lead-lag detection to behavioural exhaustion scoring. Each strategy has independent error distributions, and a tiered cost structure ensures the system runs efficiently around the clock.
A four-tier decision pipeline: continuous low-cost monitoring for market events, medium-cost analysis for opportunities, high-cost reasoning for trade decisions, and a dedicated risk and execution layer with circuit breakers, correlation guards, and position limits.
"The breakthrough wasn't adding more indicators. It was building genuinely independent strategies with different error profiles, so when one fails the others compensate. The edge comes from diversification of approach, not optimisation of a single signal."
The hard-won rules that guide every Applied Research engagement.
Most teams optimise the wrong layer. We diagnose first — categorising every failure by type and impact — before writing a single line of solution code.
Small tweaks at the same layer produce correlated errors. Independent approaches with different failure modes compound into a system far stronger than any single pipeline.
Small test sets are illusions. We've seen improvements look promising on 10 examples and completely regress at 50. Every claim we make is backed by statistically significant evidence.
New capabilities are additive. We don't replace proven components — we build alongside them. The system only activates new pathways when it's confident they'll improve the outcome.
We shift heavy computation to preparation time rather than query time. The result: systems that are both more thorough in their analysis and faster in their response.
Research-grade performance doesn't require research-grade budgets. We design tiered architectures that use lightweight processing for most cases and reserve expensive computation for the cases that need it.
Applied Research is for problems where standard solutions aren't good enough. If off-the-shelf AI gets you 80% of the way and that's acceptable, a Quick Win or Bespoke Build is more appropriate. If the remaining 20% represents significant business value — accuracy in high-stakes decisions, reliability in critical workflows, performance in competitive environments — that's where Applied Research delivers.
Engagements are scoped individually based on complexity and duration. We provide a detailed proposal after an initial discovery session. Book a call to discuss your specific requirements.
It's happened — and it's part of the process. Our methodology is designed to fail fast on unproductive directions and pivot quickly. We're transparent about progress throughout, and we won't recommend deploying something that doesn't demonstrate a clear, validated improvement over the alternative.
Yes. All custom research, code, and systems developed during your engagement are yours. We build for you, not for a portfolio. Full code ownership, documentation, and knowledge transfer are included in every engagement.
Tell us what you're trying to solve. We'll tell you honestly whether Applied Research is the right approach — or whether a simpler solution will do the job.
No pressure. We'll recommend the right tier for your problem.