trigsight

An AI assistant that cannot make a claim about my work it can't prove.

Every factual statement it produces is bound at build time to the exact sentence that supports it. Clicking a citation scrolls the source page to that sentence and highlights it. If a cited passage does not exist in its source document, the build fails — an unverifiable claim is not shippable.

Why this exists

Portfolio chatbots make claims about their author's competence. A reader has no way to check them, and good reason not to try: measured citation error rates for general AI search sit above 60%, and only 51.5% of generated sentences are fully supported by their cited sources. So the claims get discounted and the feature is decoration.

The usual fix is to ground the model in a corpus and hope. That helps with hallucination but not with verification — the reader still gets a link to a whole page and has to hunt for the sentence backing the specific claim.

This inverts the trust model. The model never writes a link. It names a passage; the build resolves it against an index of what the documents actually say, or refuses to ship.

Measured results

	Comparable portfolio	trigsight
Lighthouse performance (mobile)	62	100
Accessibility · Best Practices · SEO	100 · 100 · 100	100 · 100 · 100
Largest Contentful Paint	3.9 s	1.9 s
Total Blocking Time	1,540 ms	~20 ms
Initial JavaScript (brotli)	380.9 KB	133.9 KB
WebGL scene	none	shipping
Citations verified against source	—	34 / 34

2.8× less JavaScript with a 3D scene running. Both figures are real on-the-wire brotli transfer from the deployed sites, measured by the same harness (bench/baseline/measure-payload.sh), three runs each — see docs/05-results.md for why measuring the two sides different ways overstated this as 3.4×.

Retrieval, 30 hand-written golden queries at k=5:

Config	recall@5	MRR
Lexical only (BM25)	0.933	0.729
Hybrid (BM25 + local stand-in, RRF k=60)	0.967	0.831
Hybrid (BM25 + real embeddings)	0.967	0.889

Real embeddings are text-embedding-3-small at 1536 dims, cosine, via Upstash Vector. Three identical runs. Note what changed: recall did not improve, ranking did. At this corpus size real embeddings do not find more relevant chunks, they rank the ones they find higher — which is the metric that matters, since only the top six reach the model.

How the guarantee works

flowchart TB
  subgraph build["BUILD"]
    MDX["content/*.mdx"] --> N["normalise: collapse whitespace, casefold"]
    N --> IDX["passage index"]
    IDX --> VER{"every cited passage present exactly once?"}
    VER -->|no| FAIL["EXIT 1 — build fails"]
    VER -->|yes| OK["allowlist.json"]
  end
  subgraph run["REQUEST"]
    Q["question"] --> R["hybrid retrieve: BM25 + vector, RRF"]
    R --> M["model"]
    M -->|emits a cite token| RES{"in allowlist?"}
    OK --> RES
    RES -->|yes| CHIP["chip with text-fragment deep link"]
    RES -->|no| DROP["dropped"]
  end

Loading

The model has no mechanism for producing a URL, so it cannot produce a wrong one. Prompt instructions are a request; an absent capability is an invariant.

The part that was harder than it looks

A browser matching #:~:text= compares against rendered text, and each part of the directive must sit inside a single element. Two consequences that cost real debugging:

• Comparing against raw source fails 3 of 7 passages a browser matches fine — passages crossing a newline, containing collapsed whitespace, or differing in case. A verifier that produces false failures trains you to disable it.
• A passage spanning inline markup renders as separate DOM nodes and cannot be matched at all, even though flattened text contains it. Two of 34 citations were in this state: reported bound, unmatchable in practice. Caught only by fetching every built page and searching its text.

Both are now enforced, with regression tests. The verifier also requires each passage to appear exactly once, disambiguating with a prefix when it does not — otherwise the browser silently scrolls to the wrong occurrence, a correctness bug that looks like success.

Quickstart

npm ci
npm run dev          # http://localhost:3000

No credentials needed. Without AI_GATEWAY_API_KEY the chat endpoint returns the retrieved context instead of an answer, so retrieval is inspectable on its own.

Verify the claims yourself

npm run verify:citations                 # the gate: exits 1 on any unbound claim
npx tsx bench/citations/verify.ts --demo # watch it reject a fabricated passage
npm run eval:retrieval                   # recall@5 and MRR over the golden set
./bench/payload/measure.sh 3990 150      # initial JS against the budget
npm test                                 # 91 tests

Every number above comes from one of those. bench/ is committed and never gitignored — an uncommitted harness turns a real result into an unverifiable claim.

MCP server

POST /api/mcp — stateless Streamable HTTP, spec 2026-07-28. Four read-only verification tools rather than description tools:

Tool	Answers
list_work	What is documented, with metrics flagged verified or not
find_evidence	What passages support a claim — or plainly that none do
check_stack	Whether a technology is discussed in prose or merely listed in a stack
read_work	Full text of one case study

find_evidence originally confirmed "quantum cryptography research on ion traps". A vector leg is nearest-neighbour search: it always returns its closest chunks however unrelated. An agent consuming that would repeat a fabricated credential. It now requires genuine lexical overlap, calibrated to 8/10 true positives and 0/6 false positives against hand-written claim sets.

Limitations

Stated plainly, because a limitations section that reads as marketing is worthless.

• The margin over lexical-only is one query wide. At 34 chunks and 30 golden queries, one query is worth 0.033 of recall — so the 0.034 recall gap between lexical and hybrid is a single query. Do not read the table as proving hybrid superior in general. It shows hybrid is not worse and ranks better on this corpus.
• A prediction of mine was wrong, and the reason is instructive. I predicted real embeddings would fix the one missed query. They did not. Probing the vector leg in isolation showed the correct chunk never enters its top 5 — because that chunk is 1,340 characters covering five topics, and the answer is one clause 871 characters in. Its single vector is an average of five ideas. That is a chunking problem, not an embedding problem, and no model fixes it. Recorded rather than patched, because changing the chunker now would invalidate the comparison the table exists to make. See bench/retrieval/results/real-embeddings-2026-08-21.md.
• Without credentials the vector leg degrades to a deterministic stand-in that is not a semantic model. Retrieval still answers via BM25, but the quality is genuinely lower — the harness names the backend on every run so a number can never be quoted without it.
• Text fragments are fragile by design. Rewording a cited sentence breaks its link. That is the point of the build gate — you cannot ship the breakage — but it does mean editing prose sometimes means updating a citation.
• Citations must sit inside one block element. A claim best supported by text spanning a table row and a paragraph cannot be cited as a single passage.
• The corpus is small (6 documents, 34 chunks). Retrieval numbers here should not be read as predictions for a large corpus.
• No streaming citation resolution. Chips render after a message completes, not mid-stream.

Stack

Next.js 16.3.1 · React 19.2.8 (pinned — R3F 9 peers >=19 <19.3) · TypeScript · Tailwind v4.3.3 · Velite + Zod · three 0.185.1 + R3F 9.7.0 (pinned) · Vercel AI Gateway

Licence

MIT — see LICENSE.