What changed
FACT (per input hypothesis text, itself labeled inference beyond the announcement): Microsoft is rolling a new default frontier model (GPT-5.6) into M365 Copilot tenant-wide. HYPOTHESIS: this causes material, undiagnosed output drift in Copilot-dependent business workflows. No signal or demand evidence was supplied to this analysis — the entire opportunity rests on an untested transfer of the 'invisible enforcement failure' pattern.
Why now
The model-default switch is rolling out mechanically to every Copilot tenant now, and (HYPOTHESIS) no first-party per-workflow regression surface exists — the drift-minting window is open only until Microsoft ships model pinning or change-preview diagnostics, which is a plausible near-term first-party move.
Converging signals
The input contains ZERO ingested signals and ZERO demand_evidence items. The only 'signal' is the referenced GPT-5.6 default rollout (signal 1078, not included). Convergence here is a constructed hypothesis, not an observed collision of independent signals — scored accordingly.
Customer pain
HYPOTHESIS: businesses with Copilot-embedded processes (contract summaries, spreadsheet transforms, email triage) see outputs change format/tone/edge-case handling after the swap and misattribute losses to staff or data. Zero complaint threads, tickets, or forum posts were provided as evidence that anyone is currently feeling — or even noticing — this pain. The pattern's cruelest failure mode cuts both ways: if the drift is invisible to victims, they also don't know to shop for a fix.
Who pays
HYPOTHESIS: M365 tenant admins / IT ops leads at Copilot-licensed orgs (200–5,000 seats) with revenue-touching Copilot automations. Identifiable persona, but willingness-to-pay is entirely unproven, and this buyer sits inside IT procurement with security review for any third-party app requesting delegated Graph/Copilot access.
Solved today
FACT-level framing from input: M365 admin center, DLP, and audit logs record that Copilot ran, not output quality. External LLM-eval tools (LangSmith, Braintrust, PromptLayer) instrument apps you build on raw model APIs, not the closed Copilot surface. So today: nothing, or ad-hoc manual spot-checks.
Why current solutions are bad
Audit tooling is invocation-centric, not quality-centric; eval tooling can't reach inside the M365 walled garden. TRUE — but 'nobody solves this' may mean 'nobody has the problem acutely enough to pay,' which is the null hypothesis until the testable prediction is run.
Proposed product
Two-part offer: (1) per-incident drift diagnosis — 'your invoice-summary flow broke on the June swap; here is the failing prompt class and a mitigation prompt/config' at a fixed fee; (2) subscription canary — customer-specific golden-prompt suite replayed daily through their tenant's Copilot with diffed, scored outputs and a change-alert feed keyed to Microsoft model/config rollouts.
MVP version
A single-tenant proof: 50-prompt golden suite replayed via delegated access before/after a model change, semantic+format diff report, and an alert email. CRITICAL UNVERIFIED ASSUMPTION: that Microsoft 365 Copilot exposes programmatic, license-compliant replay of the same Copilot surfaces users depend on (Excel/Word in-app Copilot behavior is likely NOT reachable via Graph APIs; only Copilot chat/retrieval endpoints are, and some are preview). If in-app surfaces are unreachable, the product monitors a proxy of the thing that breaks, which guts the diagnosis claim.
30-day build
Spend ~$1–2k: stand up a Copilot-licensed test tenant, attempt programmatic replay (this is the go/no-go technical gate), run the 50-prompt pre/post suite, and simultaneously mine Microsoft Tech Community + r/sysadmin for post-rollout 'Copilot changed behavior' threads. Kill immediately if replay is API-impossible or drift <10% of prompts or <5 organic complaint threads exist.
60-day build
If gates pass: package the diff harness, recruit 5 design-partner admins from the complaint threads with a free drift audit of their top 3 Copilot workflows, convert audits into paid per-incident diagnoses ($750–1,500) to prove willingness to pay before building the subscription.
90-day revenue plan
3–5 paid incident diagnoses + first 2–3 canary subscriptions at ~$400–800/mo/tenant. Realistically revenue lands day 90–150 given IT security review of delegated-access consent — acceptable under the founder's current runway (lesson, confidence 0.9, applied: do not penalize the ramp itself).
Distribution path
Direct outreach to authors of drift complaint threads (Tech Community, r/sysadmin, Spiceworks), an open-source 'Copilot drift test kit' as lead magnet, and eventually Microsoft AppSource — which adds marketplace/consent approval risk.
Pricing hypothesis
$750–1,500 per incident diagnosis; $400–800/mo per tenant canary; enterprise tier only if inbound. Anchors against the cost of one silently-corrupted contract summary, but that anchor is rhetorical until a real loss story exists.
Technical difficulty
Moderate-to-high, concentrated in one place: sanctioned programmatic access to the actual Copilot surfaces customers use. The diffing/scoring/scheduling layer is squarely within the founder's automation strengths; the walled-garden access problem is not — it is Microsoft's to grant and revoke.
Legal / regulatory risk
Automated replay against Copilot may brush ToS/acceptable-use limits; handling customer prompts/outputs means processing tenant business data (DPA needed). Manageable, not disqualifying.
Platform dependency
SEVERE and structural — the product exists in the gap Microsoft left, reaches its data only through Microsoft's APIs, and dies the day Microsoft ships tenant model pinning or change-preview diagnostics (explicitly named as the falsifier in the input). This is a rented wedge on the landlord's roadmap.
Founder fit
Weak-to-moderate. No forced-buyer government mandate (the high-confidence heuristic lesson about portal-mandate fit, 0.8, does NOT apply here — Microsoft is a platform vendor, not a regulator, and nobody is compelled to buy monitoring). Buyer is IT within enterprises the founder prefers to avoid; sale requires security-review trust cycles, not demonstrated-value quick closes. His automation/diffing skills fit the build; the channel and dependency profile fit him poorly.
Breakout potential
If real, generalizes to a cross-platform 'AI feature drift observatory' (Google Workspace Gemini, Salesforce, etc.) — a genuine category. But that scale attracts the LLM-eval incumbents, who already have the harness tech and enterprise logos.
Final recommendation
DO NOT BUILD YET. This is a well-formed hypothesis with a cheap, decisive falsification test and a currently empty evidence file. Spend ≤$2k and ≤3 weeks running the stated testable prediction (replay suite + complaint-thread census + API-access gate) before writing a line of product. If all three gates pass, proceed to paid incident diagnoses as the wedge; if any fails, archive. Grade as-is: C — interesting pattern, severe platform dependency, no observed demand.
Next action
Run the falsification bundle: (1) verify whether Microsoft 365 Copilot chat/retrieval APIs permit scheduled programmatic replay under delegated permissions in a test tenant; (2) count post-GPT-5.6-rollout drift threads on Microsoft Tech Community and r/sysadmin (threshold ≥15 per the input's own prediction); (3) execute the 50-prompt pre/post diff. Only pilot outreach if all three clear.