Case Study #5
Our first IDE test: the audit pipeline reviewed its own expansion.
We wired MegaLens into the editor and ran it on a real task: our own expansion from three audit styles to five. It reviewed the design before a line of code was written, then audited the finished diff. This was the first end-to-end run on production-bound work.
~50k
Est. host-IDE context saved
$0.21
Consultation cost
3 of 4
Risks caught pre-code
49/49
Regression pass rate
Specific engine names and the reviewer combinations behind each tier are proprietary to MegaLens. We refer to them by role.
The setup
The task was narrow but real. We were expanding MegaLens from three audit styles to five — adding one research-shaped style and one diff-shaped style.
The founder was working inside his IDE. Instead of opening the web app and pasting context into a separate chat, the coding assistant in the editor called MegaLens through the connector and got back a structured consultation. We used that path twice: once before any code existed, once after the implementation was done.
We ran it this way to measure three things: host-assistant context savings, blind-spot coverage, and raw execution metrics.
Before code was written
Design consultation from inside the editor. The goal: flag structural risks before they ship.
The pre-code pass surfaced 3 of the 4 load-bearing risks in the design. These were not cosmetic notes. They were the kind of mistakes that send implementation down the wrong path and make the later review more expensive. One of them would have led us to apply an obvious-looking fix that was actually wrong.
We changed the implementation plan before touching a file. Independent council review later caught one more risk in the next round — exactly the layered catch our system is designed to deliver.
What the pre-code pass actually caught
A drift risk between the public product surface and what the IDE path exposed.
Two declared capability sets had to stay in sync. The pre-code pass flagged the drift pattern before the new styles were routed, forcing a single source of truth for both.
A classification mistake that would have routed design requests the wrong way.
Without this catch, the obvious fix would have been to relabel the style type. The pre-code pass pointed at the routing layer instead, which was the actual defect.
A guardrail gap where design-from-scratch work could slip past the intended boundary.
The new style could have been used for unbounded design work. We tightened the guardrail before the capability was live, not after.
After the diff was written
Audit consultation on the finished implementation. Front-line reviewers first, judge tier second.
The front-line reviewer tier surfaced a set of issues. Then the judge tier reviewed the same diff and added 5 findings that all three front-line reviewers had missed— including the one real high-severity defect in the review set.
The defect was a rubber-stamp pattern. Under a specific input shape, a diff with no meaningful changes could have been approved without the layer actually verifying the target file was present. The kind of false-positive approval that only surfaces when something downstream quietly depends on it being correct.
Diff-review cross-check
5
Judge-tier findings the front-line tier missed
1
Real high-severity defects caught
3
Council review rounds to ship
49 / 49
Regression cases passing
The judge tier was the quiet hero of this session. Not the first read. The read after the first read. That tier is the reason the high-severity defect did not reach ship.
Efficiency in the IDE
How much context the IDE connector offloads from the host coding assistant.
Label: these savings are engineering estimates from one session, not instrumented telemetry. The live measurement layer is running now; future case studies will cite measured numbers.
In the old workflow, the host coding assistant reads a lot of source, forms its own view, iterates, re-reads, and spends a large slice of its own context window doing review work that could be delegated.
With the connector, the assistant asks MegaLens for the heavy review and gets a structured consultation back. It does not need to re-derive the review from raw files.
200–240k
Typical host spend without the connector
~50k
Est. host context offloaded
108,613
Consultation exchange (input + output)
The numbers
108,613
Consultation tokens (input + output)
$0.214
Raw provider cost
~$2.07
Approx. billed PAYG cost
3 of 4
Pre-code risks caught
5
Judge-tier extra catches
49 / 49
Regression pass rate
Two latency numbers are worth naming. The pre-code consultation took 153 seconds. The diff audit took 327 seconds. Staff-engineer review speeds — designed for the moments that matter: before a branch, before a ship, at the inflection points of a change.
What this changed our mind about
The IDE path saves real host-assistant context.
Offloading an estimated ~50k host-assistant tokens on one real task is not a small number. It means the coding assistant can spend more of its own window on implementation work instead of re-deriving a review the audit pipeline could have delivered.
A reviewer tier above the first pass is load-bearing.
The judge tier caught 5 issues that three independent front-line reviewers missed, including the one real high-severity defect. This is the cleanest signal in the whole session. The blind spots are not theoretical. They surface on our own code too.
Layering works. That is the operating model.
The pre-code pass caught 3 of 4 load-bearing risks. Council review caught the fourth in the next round. That layered catch is the shape of the system working as designed — each tier doing its job.
Limitations
One session, one product change. Ratios from a single run do not generalize to every task. We are publishing this because it was our first end-to-end IDE run, not because it is a universal benchmark.
The IDE savings are engineering estimates from this session, not instrumented telemetry. The live measurement layer is running now and future case studies will cite measured numbers.
The meta note:the product reviewed its own expansion. If our methodology cannot improve the tool that implements the methodology, it does not work. On this session, it did. Host-IDE context offloaded. Structural risks surfaced before code existed. A high-severity defect caught after code existed that the first reviewer pass missed. Council review layered on top for the final cross-check — the way we want every serious ship to go.
Implementation details, tool names, and architecture specifics are intentionally omitted. We share the process and the numbers, not the blueprint.
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