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% SPID — SEC Project Intelligence Database : Potential & Pilot Results
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\fancyhead[L]{\footnotesize SPID — SEC Project Intelligence Database}
\fancyhead[R]{\footnotesize Pilot \& Potential}
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\title{\vspace{-1.2cm}\textbf{\Large SPID — A SEC Project Intelligence Database}\\[4pt]
\large Reconstructing the strategic project portfolio of public companies\\ directly from raw SEC filings}
\author{UQO EDGAR Research DB \\ \small Proof-of-concept report}
\date{\today}

\begin{document}
\maketitle
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\begin{abstract}\noindent
SEC filings are routinely mined for \emph{financial} signals. SPID treats them instead as a
massive, untapped source of \emph{project intelligence}: every 10-K, 10-Q and 8-K is parsed for
the concrete initiatives a company is pursuing --- acquisitions, plant and data-center builds,
R\&D and drug pipelines, AI deployments, ERP and cloud migrations, capex programs, partnerships,
energy-transition projects, and more. Extracted records are de-duplicated across filings into
\emph{projects with lifecycles}, linked into a \emph{knowledge graph}, embedded for semantic
search, and exposed through analytics. The result is a home-grown analogue of a
Capital~IQ / PitchBook / Gartner platform, but \emph{project-centric}. This report documents a
pilot run on \Ncompanies{} large-cap issuers that produced \textbf{\Nprojects{} resolved
projects} from \Nmentions{} extractions using \texttt{gpt-4o-mini}, for roughly \$2.50 of
compute --- and quantifies the path to the full corpus.
\end{abstract}

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\section{The idea: filings as project intelligence}
A 10-K is usually read for revenue, margins and risk factors. But buried in the Business
section, the MD\&A and 8-K event filings is a continuous, dated, legally-vetted narrative of
\emph{what every public company is actually building}. No commercial database reconstructs this
at the level of individual projects across the whole market. SPID does exactly that, in four
moves: (i)~\textbf{extract} project records from filing text; (ii)~\textbf{resolve} the same
project mentioned across many filings into one entity with a timeline; (iii)~\textbf{link}
projects to companies, technologies, locations and partners in a knowledge graph; and
(iv)~\textbf{serve} the result through structured, semantic and graph queries.

\begin{keybox}
\textbf{Pilot at a glance.}\quad \Nprojects{} resolved projects $\;\cdot\;$ \Nmentions{} raw
extractions $\;\cdot\;$ \Ncompanies{} companies $\;\cdot\;$ \Ntypes{} project types $\;\cdot\;$
\$\Ncapex{}\,bn of associated capital identified $\;\cdot\;$ \Ntimeline{} dated timeline points
$\;\cdot\;$ knowledge graph of \Nkgnodes{} nodes / \Nkgedges{} edges.
\end{keybox}

% ---------------------------------------------------------------------------
\section{Data foundation and pipeline}
SPID is built on top of the existing UQO EDGAR corpus: 134{,}124 filings and 127{,}800
pre-segmented sections for 500 companies (88~GB of raw filings on disk). Because the upstream
parse only sectioned 10-Q and 8-K filings, SPID additionally \emph{ingests the raw 10-K HTML},
cleaning and segmenting the project-dense Items (1~Business, 1A~Risk Factors, 2~Properties,
7~MD\&A). All section text flows through a single pipeline:

\begin{center}
\small
\textsf{raw 10-K HTML / parsed 10-Q,8-K} $\;\rightarrow\;$ \textsf{extract (gpt-4o-mini \emph{or}
rules)} $\;\rightarrow\;$ \textsf{resolve} $\;\rightarrow\;$ \textsf{graph $\cdot$ embeddings $\cdot$
analytics}
\end{center}

\noindent
Extraction is \textbf{backend-pluggable}. A deterministic rule engine gives a free, high-precision
first pass; the default \texttt{gpt-4o-mini} backend (OpenAI-compatible, so it also targets a local
cluster gateway) returns named projects with amounts, status, objective, technologies, partners and
a confidence score in strict JSON. Per-call cost is bounded by sending only project-relevant
\emph{windows} of each section to the model.

% ---------------------------------------------------------------------------
\section{What the pilot found}
Across \Ncompanies{} issuers, SPID resolved \Nprojects{} distinct projects spanning all
\Ntypes{} categories (Figure~\ref{fig:bytype}). M\&A integrations, plant construction, R\&D
programs and product launches dominate by count, while capex programs and M\&A dominate by
capital. The detection rate and associated capital are stable across fifteen years of filings
(Figure~\ref{fig:capex}), and projects distribute sensibly across sectors and technologies
(Figure~\ref{fig:sectortech}).

\begin{figure}[H]\centering
\includegraphics[width=\textwidth]{by_type.pdf}
\caption{Resolved projects by type, by count (left) and by associated announced capital
(right). A single controlled vocabulary of \Ntypes{} types is applied to every filing.}
\label{fig:bytype}
\end{figure}

\begin{figure}[H]\centering
\includegraphics[width=0.93\textwidth]{capex_year.pdf}
\caption{New projects detected per year (first mention) and the announced capital attached to
them. Coverage is consistent across the 2010--2026 filing window.}
\label{fig:capex}
\end{figure}

\begin{figure}[H]\centering
\includegraphics[width=\textwidth]{sector_tech.pdf}
\caption{Projects by GICS sector (left) and by technology tag (right). Technology tags such as
AI/ML, semiconductors, EV and battery storage are extracted from project descriptions.}
\label{fig:sectortech}
\end{figure}

\begin{table}[H]\centering\small
\caption{Largest detected M\&A / integration projects (by stated transaction value).}
\input{tables/top_ma.tex}
\label{tab:ma}
\end{table}

\noindent Table~\ref{tab:ma} shows the platform recovers real, correctly-valued transactions
(e.g.\ Microsoft--Nokia, Exxon--Pioneer, Nvidia--Mellanox), and Table~\ref{tab:cos} ranks the
most project-active issuers in the pilot.

\begin{table}[H]\centering\small
\caption{Most project-active companies in the pilot.}
\input{tables/top_companies.tex}
\label{tab:cos}
\end{table}

% ---------------------------------------------------------------------------
\section{The differentiator: project lifecycles}
The hard part is not finding a project once --- it is recognising the \emph{same} project across
quarters and years and stitching it into a timeline. SPID resolves mentions by company, type and
anchor (location or salient name token) and reconstructs a dated lifecycle for each
(Figure~\ref{fig:gantt}).

\begin{figure}[H]\centering
\includegraphics[width=\textwidth]{gantt.pdf}
\caption{Reconstructed lifecycles for the most-tracked projects in the pilot. Each bar spans the
first and last filing in which the project appears; the label reports the number of distinct
filings reconciled.}
\label{fig:gantt}
\end{figure}

\noindent A concrete example: Tesla's energy-storage program is reconciled across eighteen
filings (2021--2026); parsing the deployment figures embedded in each filing reproduces the
program's quarter-by-quarter ramp \emph{automatically} (Figure~\ref{fig:tesla}). This is the kind
of operating KPI a research analyst would normally assemble by hand.

\begin{figure}[H]\centering
\includegraphics[width=0.9\textwidth]{tesla_timeline.pdf}
\caption{Case study --- Tesla energy-storage deployments (GWh, as reported) extracted from the
project's reconciled timeline. SPID tracks not just that a project exists, but how its disclosed
metrics evolve.}
\label{fig:tesla}
\end{figure}

% ---------------------------------------------------------------------------
\section{Knowledge graph and retrieval}
Resolved projects are linked into a knowledge graph (\Nkgnodes{} nodes, \Nkgedges{} edges) with
typed relations \textsf{Company}\,$\to$\,\textsf{owns}\,$\to$\,\textsf{Project}, and
\textsf{Project}\,$\to$\,\textsf{uses}\,$\to$\,\textsf{Technology} /
\textsf{located\_in}\,$\to$\,\textsf{Location} / \textsf{partners\_with}\,$\to$\,\textsf{Partner};
it exports to Parquet and Neo4j Cypher. Figure~\ref{fig:kg} shows one company's slice.

\begin{figure}[H]\centering
\includegraphics[width=0.86\textwidth]{kg_nvda.pdf}
\caption{Knowledge-graph slice for NVIDIA: top projects and the technologies and locations they
connect to. The full graph spans every company and project in the database.}
\label{fig:kg}
\end{figure}

\noindent Every project is also embedded (384-dim, local model) so the database answers natural
queries directly. For instance:
\begin{itemize}[leftmargin=1.4em,itemsep=1pt]
\item \emph{``generative AI and machine learning investment''} $\rightarrow$ Salesforce, Meta and
Alphabet generative-AI programs (cosine 0.78--0.64);
\item \emph{``semiconductor fabrication plant capacity expansion''} $\rightarrow$ Micron wafer-fab,
Intel and TXN 300\,mm capacity projects;
\item structured filters compose with these (e.g.\ \texttt{type=ma\_integration},
\texttt{min\_amount=\$5bn}, \texttt{sector=Financials}, \texttt{year\_from=2024}).
\end{itemize}

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\section{Scaling potential}
The pilot deliberately covered \Ncompanies{} companies. The same pipeline runs unchanged on the
full corpus; it is incremental and resumable (stable mention IDs, \texttt{INSERT OR REPLACE}).

\begin{table}[H]\centering\small
\caption{From pilot to full corpus.}
\begin{tabular}{lrr}
\toprule
 & \textbf{This pilot} & \textbf{Full corpus (projected)} \\
\midrule
Companies              & \Ncompanies{}      & 500 \\
Filings covered        & $\sim$3{,}500 sec. & 134{,}124 filings \\
Sections processed     & 3{,}528            & $\sim$130{,}000 \\
Resolved projects      & \Nprojects{}       & $\sim$10{,}000--15{,}000 \\
\texttt{gpt-4o-mini} cost & $\approx$\$2.50 & $\approx$\$100--130 \\
\bottomrule
\end{tabular}
\end{table}

\noindent\textbf{Extensibility already wired in.} (i)~Additional forms --- S-1, DEF~14A, 20-F,
6-K, earnings transcripts and press releases --- plug into the same ingester; everything
downstream is form-agnostic, enabling month-by-month project tracking. (ii)~Embeddings can be
moved to pgvector / Qdrant / Weaviate by swapping one column. (iii)~The graph loads into Neo4j
from the generated Cypher. (iv)~A read-only FastAPI service exposes search, semantic search and
analytics over HTTP.

% ---------------------------------------------------------------------------
\section{Conclusion}
On a modest pilot and for a few dollars of compute, SPID reconstructs hundreds of real corporate
projects --- correctly named, valued, located, classified and tracked through time --- and makes
them searchable, analysable and linkable. Scaling to the full 500-company corpus is a
\$100-scale, single-day job; extending to the entire universe of \(\sim\)30{,}000 SEC filers is a
question of throughput, not of method. The proof of concept is complete and the platform is built.

\vfill
{\footnotesize\noindent\textit{Generated from the live SPID DuckDB
(\texttt{project-db/data/db/spid.duckdb}). All figures and statistics in this document are
produced directly from extracted data by \texttt{docs/make\_figures.py}.}}

\end{document}