From Chatbot to Co-Pilot: How Our AI Diagnostic Pipeline Works
- Aug 18
- 2 min read
Last week we introduced the diagnostic question that should precede every deal thesis. This week: the tool that helps us answer it at scale.
We should be transparent about how this content series was built. The market analysis underlying these articles — the operator profiles, the financial stress-tests, the behavioural-friction patterns — was produced by a diagnostic pipeline that combines generative AI platforms with structured human judgement. The AI is a tool within a human-led advisory process — the same way a financial model is a tool, not the advice. The human judgment, validation, and strategic interpretation are the product. The AI compresses the data-gathering phase.
Here is how the pipeline evolved, and what each stage actually produces:
V1 — Information retrieval. Broad queries: "list rural UK Altnets and their coverage figures." Output: generic, indistinguishable from published industry reports. Useful as a structured starting point. Not useful as analysis.
V2 — Contextual synthesis. Geographic filters (Ofcom Area 3 classifications, BDUK Project Gigabit contracts), regulatory overlays, specific audience framing. Output: segmented operator profiles, platform-appropriate formatting. Materially better, but still descriptive rather than diagnostic.
V3 — Financial diagnostics. Prompts demanding analysis of capital structures: debt-to-equity ratios, interest coverage under high-rate scenarios, ARR vs. build costs. Output: financial stress indicators that highlighted operators with structural funding gaps visible in Companies House filings. The AI's contribution was speed and systematic coverage across dozens of operators simultaneously — compressing weeks of analyst work into hours.
V4 — Problem diagnostics. The AI shifted from answering "who should we buy?" to surfacing the questions we hadn't thought to ask. This is where the diagnostic-question framework emerged — from the interaction between constrained AI analysis and human strategic judgement, not from either alone.
The lesson is not that AI replaces deal teams. It is that AI, properly constrained, compresses the diagnostic cycle by surfacing patterns in fragmented datasets that human analysts would take weeks to synthesise manually. But "properly constrained" is doing real work in that sentence — the prompt architecture is the product, not the model.
We now maintain a structured prompt registry as core advisory infrastructure, calibrated by deal type and sector.