An end-to-end agentic platform that ingests contract and grant opportunities, evaluates them against organizational priorities, and executes downstream actions including prioritization, routing, summarization, and analyst escalation.

Combines automated decision support with human oversight, enabling faster and more consistent pursuit workflows.

Business development teams face fragmented, high-volume opportunity streams with inconsistent structure and changing requirements.

• Traditional tools surface opportunities but don't actively move them through the pursuit process
• Missed high-fit opportunities due to manual triage bottlenecks
• Inefficient use of analyst time on low-value screening tasks

BOOM operates as a stateful orchestration layer that:
(1) continuously ingests and monitors opportunity sources,
(2) normalizes and semantically structures incoming data,
(3) retrieves relevant internal context from capabilities, offerings, and prior work,
(4) evaluates opportunity fit using proprietary scoring logic, and
(5) conditionally triggers downstream actions such as routing to teams, generating summaries, initiating document analysis, and preparing decision support outputs.

A production-grade decision intelligence RAG system engineered for high-reliability retrieval, grounded generation, and operational scalability. It supports corpus retrieval, document-scoped retrieval, and hybrid retrieval across persistent and session-level sources.

Critical documents, prior work artifacts, and institutional company IP were fragmented across disconnected databases and repositories, making knowledge hard to locate at decision time. That fragmentation slowed deliverable production, weakened proposal development velocity, and increased the risk of repeating work or missing high-value evidence during response windows.

The system unifies ingestion, retrieval, and generation in a single operational pipeline:
(1) query and document inputs are routed across corpus, document-only, or hybrid modes,
(2) multi-index retrieval materializes chunk/parent/neighbor context with metadata-aware reranking and diversification, and
(3) a managed LLM layer generates citation-grounded responses with streaming, retry/backoff resiliency, concurrency controls, token-budgeted context assembly, and telemetry for auditability.

A production qualitative research database that transforms unstructured evidence into a context-aware semantic knowledge layer for analysis, synthesis, and decision support.

Critical research evidence arrived in fragmented formats and channels, including federally mandated reports, interviews, survey instruments, and scraped media references. Manual cross-source synthesis was slow, inconsistent, and difficult to operationalize at scale.

The system was designed as a single qualitative intelligence workflow:
(1) ingest and normalize heterogeneous evidence sources into a shared, schema-flexible structure,
(2) preserve provenance and metadata at the source and record level for traceability,
(3) generate embeddings and vector indexes to enable semantic retrieval alongside structured filtering, and
(4) assemble context windows with entity tags and citation-linked references for reliable synthesis and reporting.

An operational intelligence platform combining machine learning, predictive workflow analytics, and resource-allocation visibility to improve delivery performance in multi-project environments.

Program leadership lacked timely integrated signals for workflow health and resource pressure. This increased exposure to burnout risk, workstream fragmentation, and poor coordination across interdependent project teams.

The system was built as a unified operational decision workflow:
(1) integrate workflow, staffing, and delivery signals into a shared operations model,
(2) map dependencies between workstream stages and resource capacity to expose coordination pressure points,
(3) apply forecasting, anomaly detection, and risk scoring for burnout, fragmentation, and handoff bottlenecks, and
(4) surface role-specific dashboards and alerting layers to support earlier intervention and allocation decisions.

Designed and implemented a predictive system that forecasts participation dynamics and outcome quality, then translates those forecasts into targeted intervention strategies. In federal participation-driven programs, this enables teams to anticipate variability, optimize resource allocation, and influence outcomes instead of reacting after trends emerge.

Organizations operating in uncertain, participation-driven federal environments lacked visibility into future demand and outcome distribution. This produced resource mismatches (overload vs. underutilization), ineffective or poorly timed outreach, and limited ability to correct course once trends appeared. Even where forecasts existed, there was no structured system for operational action.

The system was implemented as a closed-loop predictive decision workflow:
(1) model expected demand and outcome quality from historical program data, behavioral signals, and structural characteristics,
(2) calibrate probabilities and uncertainty ranges to support reliable deployment thresholds,
(3) diagnose key drivers with feature attribution and segment scenarios (low participation risk, low quality risk, imbalance), and
(4) activate targeted intervention strategies through decision rules that link forecasts directly to operational actions.

A published research project analyzing the economic impact of federal broadband funding through a custom mixed-methods framework that pairs regional econometric analysis with large-scale qualitative evidence synthesis.

Policy stakeholders needed causal, region-specific estimates of economic effects, but quantitative findings alone were insufficient for implementation interpretation. Existing analyses lacked a unified structure linking economic outcomes to on-the-ground qualitative evidence from communities and program actors.

The research used an integrated mixed-methods design:
(1) regional econometric modeling estimated effects across employment, income, and local business indicators,
(2) qualitative evidence was systematically indexed and analyzed to interpret mechanisms and implementation constraints, and
(3) both evidence streams were synthesized into a single policy analysis layer for stronger confidence, sensitivity testing, and applied guidance.

Combined listening-session NLP analysis with implementation scenario research into a single service layer for policy intelligence. The system synthesizes unstructured inputs, policy documents, and geography-linked infrastructure data to surface actionable implementation insights.

Public inputs and state implementation choices were being analyzed in silos, obscuring cross-signal patterns. Stakeholders lacked an integrated framework to connect thematic concerns, sentiment trends, geographic disparities, and likely policy outcome tradeoffs.

The system used a unified policy intelligence workflow:
(1) ingest comments, transcripts, and planning artifacts into a shared analysis layer,
(2) apply supervised and weakly supervised classification, semantic clustering, sentiment analysis, and topic modeling to surface dominant concerns and emerging issue clusters,
(3) link NLP outputs to geospatial coverage and demographic context for comparative scenario assessment, and
(4) communicate results through advanced visuals including choropleth and bivariate maps, Sankey flows, and stakeholder-topic network graphs.