AI & RAG services
Agents that expand what you are able to cover.
Agentic AI, gen AI and RAG applied to the parts of research that scale worst — deciding what to cover next, publishing at catalogue scale, and making years of archive usable again.
- 01 Market discovery
- 02 Page generation
- 03 RAG
- 04 Agentic research
- 05 Datasets
Market Discovery Agent
Know which markets to publish next.
- Runs continuously
- Out ranked candidate markets
- Every claim sourced
Deciding what to cover is usually a judgment call made from experience and whatever competitors happen to be publishing. That works, but it is slow and it misses the markets that are forming right now.
The discovery agent scans continuously for emerging markets, sub-segments and adjacent categories, then assembles a ranked candidate list — each with a proposed segmentation structure, a coverage rationale and the sources behind it. Your team decides what to commission; the agent makes sure the option was on the table.
- Continuous scanning for emerging markets and sub-segments
- Candidate markets ranked by opportunity and evidence of demand
- Proposed segmentation structure for each candidate
- Gap analysis against what your catalogue already covers
- Every recommendation traced back to the sources behind it
- Feeds straight into estimation and report generation once approved
What it saves Catalogue expansion stops depending on how much scanning time your team can spare. The judgment call stays yours; the search that precedes it does not cost you analyst weeks.
Marketing & Article Page Generation
Demand that scales with your catalogue.
- In your structured data
- Out pages, articles, collateral
- Scale thousands
Inbound leads come from ranked pages, and the number of pages you can maintain is limited by how many people you have writing them. That is a hard ceiling on pipeline, and it has nothing to do with how good your research is.
We generate market pages, articles, press releases and collateral in bulk from the same structured data that drives your reports. Because the pages are generated from that data rather than written against it, they stay accurate when the underlying numbers change.
- Market pages generated across your entire catalogue
- Articles, press releases and marketing collateral produced in bulk
- Structured markup so search engines and AI answer engines can read them
- Internal linking and topical structure built in, not bolted on
- Pages that update automatically as the underlying data changes
- Lead capture wired into the systems your sales team already uses
What it saves Page count stops being capped by writer headcount. Demand generation scales with the catalogue instead of with the marketing team you can afford to hire.
RAG Over Your Archive
Years of work, finally searchable.
- In your document history
- Out cited answers
- Deploy private
Every established research firm is sitting on an archive worth more than it realises — past reports, estimation models, client deliverables, methodology notes. Almost none of it is findable, so analysts rebuild work that already exists somewhere on a shared drive.
Retrieval-augmented generation turns that archive into something your team can actually query. Answers are retrieved from your own documents and cited back to the exact source, so an analyst can verify a claim in one click. When the archive does not contain an answer, the system says so rather than inventing one.
- Private, permissioned search across your full document history
- Every answer cited to the source document, page and passage
- Analyst copilots grounded in your own methodology and definitions
- Prior work surfaced and reused instead of rebuilt
- Client-facing variants where you want to expose it as a product feature
- Deployed inside your own environment where confidentiality requires it
What it saves Work that already exists gets found instead of rebuilt. For firms with a deep archive this is often the fastest payback of anything on this page.
Agentic Secondary Research
The gathering, not the thinking.
- Mode supervised multi-step
- Out structured, sourced values
- Conflicts escalated
Secondary research is a large share of every project and most of it is retrieval — finding filings, extracting figures, pulling company details, reconciling numbers that disagree between sources. It is necessary, repetitive and expensive when a senior analyst does it.
We build agents that carry out that work as a supervised multi-step process: plan, gather, extract, structure, check, and escalate anything ambiguous to a human. Your analysts review a structured, sourced result instead of assembling it themselves.
- Multi-step gathering across filings, publications and public sources
- Structured extraction into the format your models expect
- Cross-checking where sources disagree, with the conflict surfaced
- Human checkpoints wherever judgment genuinely matters
- Full audit trail — every figure traceable to where it came from
- Output that drops directly into your estimation model
What it saves The retrieval half of every project moves off senior analysts. They review a sourced, structured result instead of assembling it — which is the expensive way to do it.
Dataset Creation & Enrichment
New products from data nobody has structured yet.
- In unstructured sources
- Out canonical dataset
- Tracked versioned lineage
The most defensible thing a research firm can own is a dataset competitors do not have. The reason more firms do not own one is that building it means processing volumes of unstructured source material by hand.
We build those datasets — extracting, normalising and validating at a scale manual work cannot reach. Company universes, capacity and pricing tables, trade flows, product catalogues, regulatory records. Once structured, the same dataset feeds your reports, your platform and your marketing pages.
- Structured datasets built from unstructured and semi-structured sources
- Entity resolution so the same company or product is never counted twice
- Harmonisation of inconsistent units, names, categories and formats
- Enrichment of datasets you already hold but cannot fully use
- Validation rules that hold as the dataset grows
- Versioning and lineage, so every value has a traceable provenance
What it adds A proprietary dataset you can sell, built at a scale manual work cannot reach. Unlike the rest of this page, this one creates an asset rather than recovering a cost.
How the agents run
Plan, act, verify — with a human at the gate.
Every agent on this page runs the same loop. It plans the work, acts across sources, verifies its own output against them — and anything that needs judgment stops at your analyst instead of being guessed.
How we apply AI
Grounded systems, not confident guesses.
In a business where clients pay for accuracy, a plausible wrong answer is worse than no answer. Everything we build is designed around that constraint.
Retrieved, not invented
- Answers come from your sources
- Citations to document and page
- Silence where support is missing
Supervised by design
- Explicit limits on autonomous decisions
- Conflicts escalated, never guessed
- Analysts review rather than assemble
Auditable end to end
- Every step and source recorded
- Any client figure traceable to origin
- Versioned so results are reproducible
Start with whichever one costs you most.
For most firms it is either the pages that bring in demand or the archive nobody can search. Tell us which, and we will show you what it looks like automated.