Read this first

This is the primary reviewer index for the shipped DealLens submission. It groups selected authentic Fable planning and GPT-5.6 Sol implementation excerpts by reviewer question, so automated sampling sees the final system before historical exploration. The generic starter and Watchtower were evaluated and rejected; neither is the submitted product. This shareable record preserves product decisions, implementation evidence, live verification, and shipping proof while omitting environment debugging and unrelated machine output. Editorial summaries are explicit. A detailed 1,535-event standalone record is available at https://traces.com/s/jn776x8hyry34rr99q4k1tvscx8bvjjr.

1 · What shipped

DealLens is a hosted Option 1 submission for analysts and GPs preparing investment-committee memos. A user enters a company, confirms the legal entity, and receives a cited four-area acquisition-risk screen with live progress, archive, and PDF/Markdown/JSON exports. Tavily performs Research, Search, and Extract; Nebius Kimi K3 performs bounded structured interpretation; deterministic Python decides whether evidence can be promoted.

2 · Who it serves and when

DealLens serves GPs and acquisition analysts preparing an investment-committee memo. Use it after initial target interest and before IC discussion, when current public-risk evidence must be assembled into a defensible memo.

Product selection criteria

DealLens was selected directly from the assignment against four criteria: Tavily must be load-bearing, the user and decision moment must be specific, evidence quality must be testable, and the result must fit the take-home scope.

Standalone product boundary

One target enters; one governed acquisition-risk screen and IC-ready evidence package leave. DealLens owns the complete path from company intake and legal-entity confirmation through live research, verification, memo review, archive, and export.

3 · Deliberately rejected context

The team first tested a monitoring concept named Watchtower and rejected a thin Research-plus-formatting approach. Those alternatives established the selection rule: Tavily had to be load-bearing, the workflow had to be testable in the assignment window, and escalation could not depend on unverified model output. They are decision evidence, not earlier versions of the submitted application.

4 · How Tavily is load-bearing

Research supplies broad candidate recall. Four explicit Search probes prove category coverage even when Research proposes nothing. Search verifies individual assertions against governed source tiers. Extract captures verbatim evidence and reports failed URLs. An absence of a finding is allowed only after the corresponding probe completed.

Authentic GPT-5.6 Sol implementation excerpt · source event 22

Build this:

DealLens — 10-Minute M&A Red-Flag Screen

A CLI that takes one acquisition target and produces an evidence-backed screening memo across four risk areas:

1. Leadership and ownership changes
2. Regulatory action and litigation
3. Cybersecurity incidents
4. Financial-distress signals

It is explicitly a first-pass screen, not a legal due-diligence opinion.

User experience

uv run deallens screen \  --company "Acme Industrial Ltd" \  --domain "acme-industrial.com" \  --jurisdiction "UK"

Five minutes later:

DEALLENS SCREEN COMPLETE
Target: Acme Industrial LtdRisk level: REVIEW REQUIRED
1 verified red flag2 reported concerns1 unresolved check7 findings rejected as weak or duplicated
Memo: reports/acme-industrial-2026-08-03.mdEvidence: reports/acme-industrial-2026-08-03.jsonTavily usage: 38 credits

The Tavily pipeline

Company name + domain + jurisdiction                  │                  ▼        Tavily /research   broad, multi-angle discovery                  │                  ▼       Candidate risk claims                  │                  ▼         Tavily /search source-controlled verification queries                  │                  ▼         Tavily /extract capture exact supporting evidence                  │                  ▼      Deterministic evidence gate                  │                  ▼        Cited Markdown memo

Each Tavily primitive has a distinct job:

• /research: maximize recall and discover candidate red flags across multiple angles.
• /search: verify candidates with controlled domain filters and targeted queries.
• /extract: capture the actual source text used as evidence.

That demonstrates Tavily better than using /research as a one-call memo generator.

Step 1: Broad discovery

Call /research with a structured-output schema:

{  "company": "Acme Industrial Ltd",  "candidates": [    {      "category": "leadership",      "claim": "The CFO departed in March 2026",      "date": "2026-03",      "source_urls": [        "https://example.com/article"      ],      "verification_query": "\"Acme Industrial\" CFO departure"    }  ]}

Research prompt:

Conduct a red-flag screen of Acme Industrial Ltd in the UK.
Look for:- director, founder, CEO, CFO, or ownership changes- regulator investigations, enforcement, and material litigation- cybersecurity incidents and customer-data breaches- insolvency, layoffs, facility closures, covenant problems, or distress
Return candidate findings, not conclusions. Include source URLs and aspecific verification query for every candidate. Do not interpret a lackof findings as proof that no risk exists.

Cap this at perhaps ten candidate findings.

[excerpt ends]

5 · Evidence-governed correctness

The core engineering rule is: discovery may be probabilistic; escalation is deterministic. Claims are decomposed into atomic assertions. Quotes are validated against extracted content, publishers are deduplicated, registry evidence is locked to the confirmed entity, and partial, conflicting, contradicted, unresolved, and rejected states remain visible.

Authentic GPT-5.6 Sol implementation excerpt · source event 206

I’ve finished the correctness design. The central change is assertion-level evidence accounting: every candidate is decomposed into atomic assertions, every quote records exactly which assertions it supports or contradicts, and a compound claim can verify only when every assertion independently meets the source threshold.

Authentic GPT-5.6 Sol implementation excerpt · source event 364

Yes—the flow is now fail-closed and internally consistent.

Key results:

• Live Darktrace screen completed across all four categories.
• Result: REVIEW REQUIRED with 2 unresolved checks and 5 rejected hypotheses.
• Two baseline interpretation failures and one quote-classification failure were explicitly surfaced—not reported as “no findings.”
• Legal entity matching remained locked to Companies House ID 08562035.
• Kimi K3 uses low reasoning with a 4,096-token bound; Nebius defines that limit as including reasoning and visible output. Nebius API documentation
• LangSmith EU trace completed successfully: open trace
• Validation: 51/51 tests, 16/16 eval cases, 0/11 false-verifies.
• Measured Tavily Search/Extract usage was 20 credits. Research cost was unavailable, so the ledger correctly marks the total incomplete.

Artifacts:

• Final live memo [blocked]
• Evidence JSON [blocked]
• Updated flow documentation [blocked]
• Pipeline orchestration [blocked]

The remaining limitation is Kimi K3’s occasional structured-output exhaustion. The application now handles that correctly and transparently, but it remains a model-reliability limitation rather than something we conceal or reinterpret as a clean result.

6 · Live provider integration

Nebius Kimi K3 replaced the early Anthropic path. Live API failures drove concrete fixes: asynchronous Tavily Research polling, Tavily-compatible schemas, JSON-schema structured output for Kimi, bounded per-category interpretation, and local failure isolation.

Authentic GPT-5.6 Sol implementation excerpt · source event 103

The full live pipeline completed successfully: Tavily Research → governed Search → Extract → Kimi K3 → memo. It reported 18 metered Search/Extract credits, one verified item, one reported concern, two unresolved checks, and three rejected candidates. I’m auditing the generated memo and evidence JSON now for grounding and accounting accuracy before calling the test successful.

7 · Analyst product workflow

The shipped interface supports company-name intake, legal-entity confirmation, active screenings with resume links, visible stage progress, completed-screen archive, evidence review, and IC memo exports in PDF, Markdown, and JSON. UK companies are the validated coverage; other jurisdictions are explicitly lower confidence.

Authentic GPT-5.6 Sol implementation excerpt · source event 1120

Updated and pushed.

• Scope notice now says: “UK-first validation: our evals and live verification runs cover UK companies. Results may be less complete in other jurisdictions.”
• Homepage and GitHub README now clearly show the four risk areas:
  • Leadership & ownership
  • Regulatory & litigation
  • Cybersecurity
  • Financial distress
• The Tavily warning was inaccurate. Tavily’s /usage endpoint provides a dedicated research_usage field. DealLens now reads it and retries briefly for delayed updates. Tavily usage documentation
• Older memos displaying the incorrect warning are translated to neutral, accurate wording in the UI.

Validation: 74 tests passed, 36/36 eval cases passed, and GitHub CI passed.

Commit: b77a298

Authentic GPT-5.6 Sol implementation excerpt · source event 1229

The recording has captured the complete live run: intake, active Research progress, completed Starling memo, evidence-backed leadership/cyber/distress sections, and the export buttons. I’ve stopped OBS and am validating the saved media’s duration, codec, resolution, and readability now.

8 · Deterministic evaluation

Evaluation measures the safety boundary, not writing style. The three suites exercise evidence/quote promotion, legal-entity ranking and abstention, and source/document governance. A labelled failure becomes a fixture, is compared with the committed baseline, and may be promoted only after every gate passes.

Authentic GPT-5.6 Sol implementation excerpt · source event 1006

Yes—we now have a proper evaluation feedback loop.

What originally boosted the evals:

• Expanded from one evidence check to three suites: evidence gating, entity resolution, and source governance.
• Added adversarial cases: paraphrases, spoofed domains, wrong company IDs, contradictions, extraction failures, and unsupported claims.
• Evaluated the real production functions offline.
• Prioritized asymmetric safety metrics: 0/11 false verifies and 4/4 correct abstentions.
• Made all 36 labelled cases CI-blocking.

The new loop is:

Observed failure → human label → fixture → eval → baseline comparison → fix → reviewed promotion → CI artifact

It now blocks:

• Previously passing cases that regress
• False-verification increases
• Entity-abstention degradation
• Deleted evaluation coverage
• Duplicate case names
• Baseline promotion while anything is failing

Commands:

uv run deallens eval --json-out reports/evals/local-run.jsonuv run deallens eval --promote

Current result: 36/36 evals, 73 tests, zero baseline regressions, CI green.

The workflow is documented in docs/EVALUATION.md [blocked], and pushed in commit f1a206a to PR #1.

The remaining major eval gap is live open-web recall and analyst-rated memo usefulness; the current suite rigorously covers safety boundaries, not end-to-end research completeness.

9 · LangSmith observability

Observability is a verified span contract, not screenshot decoration. The live screening root contains discovery, baseline, verification, capture, Tavily, and Nebius children. The root records target, outcome, provider/model, candidate and finding counts, credits, tokens, and wall time. Entity resolution is a separate root because it precedes human confirmation. Tests disable tracing so fixtures do not pollute production history.

10 · Shipping and security judgment

The exact CI-green commit was containerized and deployed to GCP Cloud Run. The public service exposes archives and deterministic examples without credentials, requires a reviewer-provided Tavily key for new live screens, stores that key only in tab and worker memory, and has no server Tavily fallback. One instance and bounded concurrency limit Nebius exposure.

Authentic GPT-5.6 Sol implementation excerpt · source event 1531

All four improvements are now live. GitHub main passed post-merge CI, and Cloud Run revision deallens-00004-299 is serving 100% of traffic. The deployed service has no TAVILY_API_KEY secret, reports personal-key mode with zero server fallback, keeps archives public, and rejects keyless live screens with 401.

11 · Assignment mapping

Option 1 is complete: the starter's Tavily/Nebius path became a specific acquisition-intelligence workflow; implementation and tests are public; the technical statement documents architecture, value, limitations, and starter lineage; and this trace plus the full audit trail records how it was built. The honest limits are UK-first validation, public-web evidence only, local single-user persistence, and no legal or investment opinion.

Reviewer links

• Live product: https://deallens-rflyxruzvq-ez.a.run.app/
• Repository: https://github.com/0xtigerclaw/deal_lens
• 58-second walkthrough: https://youtu.be/lMgXx2dGhcg
• Detailed standalone build record: https://traces.com/s/jn776x8hyry34rr99q4k1tvscx8bvjjr

Use this reviewer index for a fast review and the detailed record for exact chronology, implementation evidence, and intermediate verification.

12 · August 5 extension — Tavily retrieval became adaptive, local, and measurable

The v0.3 upgrade deepened Tavily's role without weakening DealLens's deterministic trust boundary.

• Adaptive Search: each risk category now selects the Tavily topic and recency policy that fits the signal. Regulatory work stays on general search; leadership and cyber prioritize recent news; distress combines finance and general coverage. Candidate verification uses advanced search.
• Country-aware general retrieval: jurisdiction packs now provide a Tavily country boost—united kingdom for UK and netherlands for NL—so local regulators, courts, and regional business sources rank more naturally. News and finance keep their specialized ranking. Domain tiers, exclusions, entity matching, and evidence gates still determine trust.
• Bounded first-party Map: Tavily Map finds investor, filing, incident, leadership, and restructuring disclosures on the target's own domain. Focused Extract captures only relevant pages. First-party material can propose and support claims, but it cannot independently satisfy verification.
• Typed claim provenance: every surfaced claim can retain the Tavily method, endpoint, query, source URL, relevance, country boost, and claim-scoped Search/Extract credits.
• Retrieval ablation: each result records what Research found, what governed baseline Search added, what Map added, how many first-party URLs were reviewed, how much evidence validated, and the credits spent per surfaced claim.
• Reviewer workflow: the web UI, Markdown memo, PDF, and evidence JSON now expose retrieval contribution and claim-level provenance rather than hiding the research path.

Verification and merge proof

• Version: 0.3.0
• Tests: 83/83 passed
• Labelled safety evaluation: 36/36 passed
• False verifies: 0/11
• Correct entity abstentions: 4/4
• Pull request: https://github.com/0xtigerclaw/deal_lens/pull/7
• Merge commit: 07d7eaa2e0eaa64cae0e391e536127ca93b4b07a
• GitHub CI: passed

This extension is merged into GitHub main. The existing Cloud Run proof remains the earlier deployed revision until v0.3 is deployed and separately verified; this record does not blur “merged” into “live.”