R&D Velocity Audit
How fast a company translates R&D into shipping product, benchmarked against peers.
The R&D Velocity Audit measures how fast a named company translates R&D investment into shipping product. The audit joins patent filing cadence and concept-cluster mapping (USPTO) to code, package, and product surfaces (GitHub, PyPI, public release notes) and reports a median filing-to-ship half-life benchmarked against named peers.
The deliverable is a quantitative read: how fast the company is actually moving, on what concept clusters, with what peer-relative trajectory, and how the public R&D narrative reconciles with financial disclosures. Base scope covers the target plus a baseline peer set; additional peer benchmarks are +$1,499 each.
Built for equity analysts, hedge-fund research, and corporate strategy teams pressure-testing R&D claims against shipping evidence.
How fast does this company actually translate R&D into shipping product?
What buyers actually ask.
How fast does this company actually translate R&D into shipping product?
The R&D Velocity Audit reports a median filing-to-ship half-life: how long from a patent filing on a named concept to evidence of that concept reaching customers (release notes, GitHub commits, public product surfaces). The number is reported with peer benchmark context.
How is "shipping" defined for a software target?
Public release notes, GitHub commits referencing the concept cluster, package version increments tied to the cluster, and customer-visible product-surface changes. The audit names which signal is the dominant ship indicator per cluster.
What does concept-cluster mapping do?
Patent claim text is clustered semantically so the audit groups filings by what they are actually about, not by USPTO classification alone. Concept clusters are then traced into the code, package, and product surfaces.
How is this different from a patent-counting analysis?
Patent counting tells you filing cadence. Velocity tells you whether filings turn into product. A company filing aggressively but shipping slowly looks identical to a patent counter and very different to a velocity audit.
Can I use this for biotech or hardware?
Yes for hardware where the public release surface is real (firmware updates, public spec sheets). Biotech velocity uses clinical-trial milestones in place of code commits and is scoped as a custom variant rather than the standard SKU.
How do peer benchmarks change the read?
A median filing-to-ship half-life of 18 months reads differently against a peer median of 12 months than against a peer median of 24. The audit always carries at least the baseline peer set; additional peers are +$1,499 each.
How long does the engagement take?
Ten to fourteen business days from intake confirmation. Patent-text clustering and code-surface joining are the long-pole steps.
The deliverable, in detail.
- Patent filing cadence and concept-cluster mapping at the target — filings normalized, clustered semantically, and tracked quarter-by-quarter and year-over-year.
- Code, package, and product surfaces tied to filings — GitHub commits, PyPI version increments, release notes, hardware spec changes joined to concept clusters.
- Median filing-to-ship half-life with peer benchmark, computed at the cluster level and reported with confidence tagging.
- R&D narrative cross-checked against financial disclosures — 10-K MD&A, conference-call transcripts — with divergences surfaced.
How the report is built.
The R&D Velocity Audit pulls patent filings for the named target from USPTO PatFT and AppFT. Claim text is normalized and clustered semantically so the audit reasons about concept clusters rather than raw filings. Filing cadence is computed at the cluster level — quarter-by-quarter and year-over-year.
For software targets, GitHub repositories owned by the target are pulled (public corporate organizations) along with PyPI packages and public release notes. Cluster-to-code joining matches concept clusters to commit messages, issue topics, and release notes via embedding similarity. Median filing-to-ship half-life is computed at the cluster level: from the filing date of a representative patent in a cluster to the first customer-visible evidence of that cluster shipping.
For hardware and infrastructure targets, the ship surface shifts to firmware update logs, public spec sheets, FCC filings, or comparable public release surfaces. The audit names which ship surface it used per concept cluster.
Peer benchmarking adds the same methodology against named comparators so the target's velocity is read against industry baseline. The R&D narrative the company publishes (10-K MD&A, annual reports, conference call transcripts) is cross-checked against the velocity read; divergences are surfaced.
A senior analyst reviews the joined dataset and writes the cluster-by-cluster narrative. The Counter-Signal Pass surfaces alternative explanations for any velocity slowdown — open-source dependency reuse, license-driven secrecy, classified work that does not surface publicly.
Counter-Signal Pass is included on every report. The full Foragentis methodology is documented in The State of AEO and GEO in 2026.
What this report does NOT do.
Procurement-grade reports scope themselves. The work below is adjacent and important — and is not in this SKU.
The R&D Velocity Audit reads public ship surfaces. Companies whose product is delivered behind closed-source enterprise contracts will read as slower than they are; the audit flags this and adapts the surface where it can.
Patent filings have a publication lag (typically eighteen months from filing to publication for non-provisional applications). Recent filings underrepresent the company's current activity by definition; the audit names the lag explicitly.
Concept-cluster joining is a model-driven step. The clusters are reproducible but interpretive — a domain expert should sanity-check the cluster definitions for any concept cluster the audit's thesis turns on.
What the engagement costs.
The Counter-Signal Pass — every thesis stress-tested against its strongest opposing case — is included on every report at no extra cost. See the Counter-Signal block on the catalog hub →
Methodology preview on request.
A redacted public sample for this SKU is in production. To preview the methodology now, email forintel@foragentis.com and we will send the methodology one-pager. The published methodology white paper — The State of AEO and GEO in 2026 — covers the underlying analytical framework.
Adjacent reports.
Commercial Diligence Brief
Pre-LOI telemetry on a named acquisition target — un-gameable signals on growth, churn, and adoption.
Technographic & Traffic Teardown
Tech-stack archeology, WHOIS history, 24-month traffic trajectory, same-stack cohort, and historical SERP rank for any named domain.
About Foragentis.
Foragentis is an AI research and product company based in Sacramento, California. ForIntel is the business-intelligence research arm — producing custom dossiers across four buyer lanes: Search & AI Visibility, Markets & Locations, Capital & Innovation, and Specialty.
Every claim in a ForIntel report traces to a public source. Findings are re-verified before delivery. The Adversary/Analyst architecture pairs a senior analyst with a counter-signal pass on every thesis. Anything below our statistical thresholds is labeled directional rather than validated.
Methodology is documented in The State of AEO and GEO in 2026 — a 9,900-word, 42-page public study with effect-size statistics across four frontier AI engines.
Ready to commission the report?
Intake takes under five minutes. We confirm scope, timeline, and cost within one business day.