Service · AI research & advisory
Technical research & advisory
Being designed. Not yet offered — register interest via Work with us.
At a glance
Status
We are designing this service line and are not taking engagements for it yet. The approach and deliverables on this page describe how we intend to run it and may change before it opens. If its status changes, the new status will be shown here and on the services index.
- Who it’s for
- Enterprises · Investors and research groups · AI vendors
- Practice
- AI research & advisory
Contents
The problem
Decisions about AI strategy, vendors and architectures are often made on marketing claims rather than evidence.
Approach
How we intend to run this work, step by step.
Step 1: Frame the question
What decision the research informs and what evidence would change it.
Step 2: Review the evidence
Primary sources — papers, documentation, reproducible results — with every claim cited.
Step 3: Run targeted experiments
Where the literature is silent, small controlled experiments on the relevant tasks.
Step 4: Report with uncertainty
Findings, confidence and open questions, written for the people making the decision.
Deliverables
What an engagement is designed to produce. The final list is fixed in the written proposal.
- Written report with cited sources
- Experiment code and results where applicable
- Briefing session with your team
What this is not
- Not yet offered. This page describes a planned service line, not a service you can book today.
- Not a fixed package. Scope, deliverables and acceptance criteria are agreed in writing for each engagement.
Related research & insights
Paper breakdowns and guides on the topics this service draws on: Evaluation · Large language models · Scaling.
Analysis ·
How to read an AI benchmark table without fooling yourself
Eight questions to ask before you trust a row on a leaderboard: who ran it, which version, under what settings, whether the benchmark still separates models, whether the model has seen it, what the grader checks, how big the noise is, and which harness produced it.
Paper breakdown ·
Chinchilla: how to split a compute budget between model size and training data
A technical reading of the 2022 paper that concluded large language models were undertrained — the three ways it estimated compute-optimal scaling, the Chinchilla-versus-Gopher test, and why later work revisits the recipe.
Paper breakdown ·
Attention Is All You Need: the architecture behind modern language models
A technical reading of the 2017 paper that replaced recurrence with self-attention — what the Transformer does, why it trains faster, and what the paper did and did not show.
Paper breakdown ·
Chain-of-Thought Prompting: eliciting step-by-step reasoning with a few examples
A technical reading of the 2022 paper showing that few-shot examples with written-out reasoning steps improve large language models on arithmetic, commonsense and symbolic tasks — and why the effect appeared only at large scale.
Paper breakdown ·
LoRA: fine-tuning large models by training small low-rank updates
A technical reading of the 2021 paper that freezes pre-trained weights and learns low-rank update matrices instead — the maths, why it adds no inference latency, what the authors report against full fine-tuning, and what remains open.
Paper breakdown ·
Retrieval-Augmented Generation: pairing a language model with a searchable memory
A technical reading of the 2020 paper that introduced RAG models — how a dense retriever and a sequence-to-sequence generator are trained together, the difference between RAG-Sequence and RAG-Token, and what the results do and do not show.
Questions
Can we engage you for this now?
Not yet. This service line is planned: we are designing it and are not taking engagements for it. You can still register interest and describe what you need — it creates no obligation on either side.
Do you publish prices or timelines?
No. Scope, deliverables, acceptance criteria and any timing are agreed in a written proposal before work starts. We do not publish delivery service levels we have not committed to.
Can we share confidential material in an enquiry?
Please don’t. Describe the problem in general terms; no confidentiality obligation arises until an NDA is signed.