Moat.
Speed.
Allocation.
The platform moat that survives 2028 is being chosen this year.
This briefing tells you which platform moats survive 2028 in specialist research and intelligence as AI rewrites build economics. Read it before your R&D allocation locks for the decade.
The 10-page briefing. Worth 20 minutes.
One email. One PDF. Worth twenty minutes of your week.
We send it once. Work emails only.
Tuesday 10:45, client QBR. Your research director is running the quarterly pipeline with your biggest FS subscriber. Their research director mentions, casually, that they have been running your reports through Claude on their own data and can reproduce about seventy percent of the analytical depth in-house at one-tenth the subscription cost. He is not angry. He is asking what you deliver in the other thirty percent. Your renewal is in November. Your VP Engineering's last senior data scientist left for a Perplexity competitor three weeks ago.
You are not running one R&D function. You are running two, and only one is on your scorecard. One funds the subscription platform and the client portal. The other funds what has to exist by 2028: the indexed corpus of twenty years of project files, the data rights you have not renegotiated, the forecast telemetry that ties every published prediction to its realised outcome.
The middle is the moat. Between the client doing it themselves and the AI-native shipping at cost, the thirty percent your analysts deliver on top of your proprietary pipe is the only thing that is defensible.
This is the question your CEO is already asking. The briefing below is what you want in your hand before the next R&D review.
Build Velocity. Product Defensibility. R&D Capital Allocation.
Three questions every research CTO is tracking. The second is the crux. The first and third are how you earn the right to answer it.
Is your engineering team shipping the platform or fighting integration debt?
Forty engineers, a fifteen-year-old platform, a client portal untouched since 2022, an analyst workbench built on Sharepoint. The AI-native has twelve engineers and a six-month-old stack. The real bottleneck is backlog dominated by CMS debt and pipeline remediation. Buy what the tool layer carries. Retire the debt on a fixed schedule. Route freed hours into the pipe.
What does your product do that a client with Claude and an AI-native at ten percent cost cannot copy?
Above, your clients do seventy percent internally. Below, AI-natives ship synthesis at one-tenth your unit economics on public sources. What remains is the thirty percent your analysts deliver on top of twenty years of proprietary project files and forecast-accuracy telemetry. Today it sits in PDFs and three analysts' heads.
Is your R&D budget one instrument or two?
One funds the subscription platform at lower cost. The other funds the indexed corpus, the retrieval stack, the data-rights renegotiation, and the forecast telemetry. On one hurdle rate the first wins every quarter. On one scorecard the second does not exist. The CTO who walks in with one budget runs the same programme every peer is running.
What you get when you download
An 11-page report for CTOs, CPOs, and CPTOs at mid-market European specialist research and intelligence firms. Designed to be read in one sitting before your next R&D review.
Your industry, your R&D function, and why they are one problem
What is happening to mid-market research firms: enterprise clients producing the analysis in-house, AI-natives shipping synthesis at one-tenth your unit economics, the feature layer commoditising every eighteen months. What is happening inside your R&D function: a backlog dominated by integration debt, a corpus stored for distribution not retrieval, a senior bench under AI-native recruiting pressure, and a board AI-strategy ownership list your seat is not on.
Four moves across build engine, platform and data, product thesis, and R&D bench
Retire integration debt on a fixed schedule to free the team for the pipe. Ship an indexed, retrieval-ready corpus across twenty years of project files with model-agnostic embeddings, renegotiated data rights, and forecast telemetry. Stand one insight-advisory line priced on outcome on protected P&L. Rebuild the junior pathway around senior and agent pairing on synthesis work.
Five questions for your next R&D review
Is your R&D budget one instrument or two, and what is the kill criterion on each? Name the AI-native in your category and the percent of your unit economics they quote at. How many months to reconstruct your senior analysts' pattern-recognition if two of them leave? What does your product deliver that the client cannot self-serve in Claude? Is your Q1 boundary agreement with the CEO written, or waiting until after the CDO shortlist?
Calibrated for each seat at the table.