Fraud & Risk Intelligence
Real-time anomaly detection at federal scale. Ensemble and streaming pipelines integrated with existing infrastructure: sub-second scoring, monitoring, and incident runbooks.
6 weeks · POC to production
Capabilities
Production systems,not prototypes.
AI consulting and data science consulting across fraud, ML infrastructure, LLM deployment, legacy modernization, database performance, and agentic development. Each scoped to ship deployable output.
ML Infrastructure & MLOps
End-to-end pipelines from ingestion to serving. Feature stores, drift monitoring, CI/CD, and petabyte-throughput architecture.
10TB+ daily throughput
LLM & Retrieval Systems
Governed retrieval pipelines with evaluation harnesses, guardrails, and cost controls, not wrapper applications or open-ended fine-tuning.
Eval harness from sprint one
Database Performance
48-hour read-only Postgres/Supabase audit with prioritized findings, EXPLAIN plans, and engineering walkthrough. Productized entry point at $2,500.
60% p95 latency reduction
Agentic Development
Orchestrated agent workflows, full-stack applications, and AI-assisted engineering pipelines, delivered to production with the Production Standard.
One principal, end-to-end
Legacy Modernization
Legacy code run through AI-assisted engineering: mapping brittle processes, refactoring incrementally, and modernizing platforms to production-ready systems without a multi-year rewrite.
Incremental paths · not rip-and-replace
Document Intelligence
Extraction and structuring for contracts, scientific documents, and regulated workflows, with human-in-the-loop QA and warehouse-ready output.
Biotech & federal NDA
Geospatial Analytics
GPS traces, address geocoding, trade-area modeling, and spatial pipelines that prioritize product growth, market expansion, and site selection.
Location intelligence · production ETL
Time Series Analytics
Demand forecasting, KPI anomaly detection, and monitored pipelines: predictions and alerts that refresh on schedule, not one-off notebook charts.
Forecasts · alerts · drift monitoring
Data & ML Pipelines
Production data pipelines and advanced ML/AI pipelines in Python and SQL—ETL, features, training, serving, and MLOps your team operates.
Python · SQL · orchestration
Small Business AI Packages
Fixed-scope packages for property managers, local trades (HVAC, plumbing, landscaping), and niche e-commerce—lead intake, internal knowledge search, and automated reporting in days.
2–5 days · niche time leaks
Productized entry
DB Optimizer: 48-hour Postgres audit
Prove capability before a larger engagement. Founding price $2,500.
Engagements
Any project.Agentic delivery.
PrismBase accepts a limited number of engagements each quarter. Every mandate is principal-led agentic development, from architecture through production deployment, with the Production Standard contracted in writing.
I
Diagnostic Audit
Database performance & architecture review
A focused, read-only examination of your Postgres or Supabase estate. Prioritized findings and a remediation roadmap, delivered with engineering precision within forty-eight hours.
- Query plan analysis and index architecture
- Connection pooling and contention review
- Prioritized remediation sequence
- Principal-led engineering walkthrough
II
Production Mandate
Any project, agentic delivery
End-to-end agentic development for any production system, autonomous agents, legacy platform modernization, full-stack applications, fraud detection, retrieval pipelines, MLOps infrastructure. Fixed scope. Six to ten weeks to deployment.
- Discovery through production deployment
- Governance, monitoring, and incident runbooks
- Model cards and evaluation harnesses
- Complete intellectual property transfer
III
Principal Embed
Ongoing strategic partnership
The principal embedded within your organization, architecting roadmaps, shipping systems, and elevating your team's capability over a sustained engagement.
- Executive-aligned roadmap and prioritization
- Continuous architecture and hands-on delivery
- Team mentorship and capability building
- Production ownership across initiatives
The Production Standard
The Production Standard
Six commitments contracted in writing on every engagement.
01
Deploy or redirect
If AI isn't the right tool, we say so in week one and redirect budget to what will ship. No POC theater.
02
Metrics before models
Business KPIs locked in discovery. Model selection follows the metric, not the hype cycle.
03
Governance from day one
Model cards, access controls, evaluation harnesses, and audit trails, not a compliance bolt-on at go-live.
04
Production artifacts
Deployed code, monitoring dashboards, CI/CD pipelines, and incident runbooks. Not recommendations for someone else to implement.
05
6-week production target
Scoped projects designed for production in 6–10 weeks. Boutiques move; global programs wait for steering committees.
06
Capability transfer
Your team can operate, extend, and maintain what we build. IP and documentation transfer is included, not upsold.
Every scoped project includes a production deliverable checklist signed off before close. If we can't commit to deployable output, we won't take the engagement.
Selective intake · Q2 2026
Your next system shouldship to production.
Agentic development from architecture through deployment, with the Production Standard contracted in writing.