PrismBase AI · Principal-Led Production Intelligence
Stalled ML and agent initiatives taken from pilot to production: federal fraud at 10TB+/day, Postgres audits in 48 hours, governed LLM pipelines in weeks.
6 wk
POC → production
10TB+
Daily throughput
48 hr
Audit delivery
Live production signal
6 wk
POC → production
10TB+
Daily throughput
48 hr
Audit delivery
Trusted sectors
0%
Infrastructure cost reduction
0%
Downtime eliminated
0TB+
Daily throughput
0 yr
Years in data · 6 in DS/ML/AI
Capabilities
Production systems across AI consulting, data science consulting, legacy modernization, and data architecture.
Discuss a mandateOrchestrated agent workflows and autonomous systems: applications, integrations, and engineering pipelines delivered to production.
AI-assisted analysis and refactoring of legacy codebases: untangling old processes, modernizing platforms, and shipping production-ready systems without rip-and-replace.
Real-time anomaly detection at federal scale. Ensemble and deep learning systems integrated with existing infrastructure.
End-to-end pipelines from ingestion to serving. Feature stores, MLOps, and monitoring at petabyte throughput.
Governed retrieval pipelines with evaluation harnesses, guardrails, and cost controls, not wrapper applications.
Extraction and structuring for contracts, scientific documents, and regulated workflows with human-in-the-loop QA.
GPS traces, address geocoding, trade-area analysis, and spatial pipelines for product growth and market expansion decisions.
Demand forecasting, anomaly detection on KPIs, and production pipelines that keep predictions accurate over time.
Production ETL, feature pipelines, and ML/AI training and serving in Python and SQL—with monitoring and runbooks.
Workload analysis and rightsizing that converts infrastructure spend into strategic capacity.
What you actually receive
Every engagement ships production artifacts, prioritized reports, deployed pipelines, monitoring dashboards, and runbooks your team owns. Start with a 48-hour DB audit to prove it before you scale.
EXPLAIN plans with severity-ranked findings
CI/CD pipelines and drift monitoring
Model cards, eval harnesses, incident runbooks
DB Optimizer · Sample excerpt
Postgres Performance Audit
14
Slow queries
7
Index recs
60%
Est. p95 gain
Missing composite index on orders(user_id, created_at)
Seq scan · 847ms avg · 12k calls/hr
Connection pool max_connections undersized
Pool wait p99 · 2.4s under load
Stale statistics on analytics_events
Suboptimal planner · 3 queries >500ms
Client-identifying details redacted · B2B SaaS engagement · 48-hour audit
Selected work
6 weeks
POC to production
Federal FinTech
Deployed an ensemble ML pipeline processing full transaction volume in real time with sub-second scoring, monitoring, and incident runbooks.
Legacy rule-based fraud detection missed sophisticated patterns across multi-terabyte daily transaction flows. Prior AI consultancies delivered strategy decks; internal POCs stalled for months.
60%
p95 latency reduction
B2B SaaS
48-hour DB Optimizer audit surfaced three missing indexes and connection pool misconfiguration. Fixes shipped within a week of the report.
API latency spiked under load. Engineering suspected infrastructure scaling but lacked visibility into query-level bottlenecks across Postgres.
6 months
Saved vs. original plan
B2B Software
Week-one assessment redirected budget to a simpler retrieval pipeline with eval harness, guardrails, and cost controls. Shipped to production in eight weeks.
Growth-stage startup planned a custom fine-tuned LLM for internal knowledge search. Budget and timeline were open-ended; no evaluation harness or governance plan existed.
Production
Under active NDA
Biotech / Life Sciences
Shipped a governed document-intelligence and data pipeline under NDA: structured extraction, human-in-the-loop QA, audit trails, and handoff runbooks. Client identity and proprietary science remain confidential.
A biotech team needed production AI to structure scientific and operational data from lab outputs and regulatory documents, not another notebook prototype. Prior efforts stalled on governance, IP sensitivity, and integration with existing R&D tools.
6 weeks
To a live expansion dashboard
Multi-location Retail (anonymized)
We fixed the address data, figured out where each store's customers actually come from, and built a weekly ranked list of markets with strong demand where the brand was still under-served. Growth and real estate stopped debating conflicting decks; they opened one dashboard.
A national retailer with 200+ locations had customer addresses and sales by store, but no trusted answer to "where should we grow next?" Each region used different spreadsheets. Some teams drew arbitrary circles on a map. Roughly one in five customer addresses couldn't be placed on a map reliably. Expansion meetings took weeks to prepare, and leadership still disagreed on priorities.
The Production Standard
Six commitments contracted in writing on every engagement.
01
If AI isn't the right tool, we say so in week one and redirect budget to what will ship. No POC theater.
02
Business KPIs locked in discovery. Model selection follows the metric, not the hype cycle.
03
Model cards, access controls, evaluation harnesses, and audit trails, not a compliance bolt-on at go-live.
04
Deployed code, monitoring dashboards, CI/CD pipelines, and incident runbooks. Not recommendations for someone else to implement.
05
Scoped projects designed for production in 6–10 weeks. Boutiques move; global programs wait for steering committees.
06
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.
Why PrismBase
Buyers comparing McKinsey, Deloitte, or boutique AI shops often wait months for strategy, then hand off to another team to build. PrismBase contracts for deployable output (code, pipelines, and runbooks, in weeks.
Tier-1 programs
PrismBase
Who does the work
Partner + manager + analyst pyramid
Senior principal, hands-on
Time to first value
4–8 months (strategy → implementation)
48 hours (audit) to 6–10 weeks (production)
Engagement model
Separate strategy and build vendors
One team, one contract, end-to-end
Deliverables
Roadmaps, decks, reference architectures
Code, pipelines, monitoring, runbooks
Delivery model
Partner oversight with analyst execution
Principal architects, codes, and deploys
Program cost
$500K–$10M+
$2.5K audit → $15K+ scoped → $25K/mo embedded
Post-engagement
High dependency on firm to operate
Full IP transfer; your team owns it
Engagements
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.
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.
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.
Ongoing strategic partnership
The principal embedded within your organization, architecting roadmaps, shipping systems, and elevating your team's capability over a sustained engagement.
The Principal
Nine years in data, six in production DS/ML/AI—data and ML pipelines in Python and SQL, federal-scale fraud at 10TB+/day, geospatial and time series analytics, biotech R&D under NDA, and governed LLM deployments. Same principal from the first call through production.
Client perspective
PrismBase didn't hand us a slide deck. They shipped a fraud detection pipeline that processes our full transaction volume in real time. We went from months of stalled POCs to production in six weeks.
“The DB Optimizer audit found three missing indexes that were killing our API latency. We cut p95 response time by 60% within a week of implementing their recommendations.”
VP Engineering
B2B SaaS Platform
Enterprise SaaS
“Finally, an AI consultancy that tells you what won't work. They saved us six months by redirecting our LLM initiative toward a simpler retrieval pipeline that actually shipped.”
CTO
Growth-Stage Startup
B2B Software
Client names withheld under NDA where required. Read documented outcomes
Insights
Free evaluation
Five-minute MLOps maturity assessment, instant score, gap analysis, and prioritized recommendations. No sales call required.
Selective intake · Q2 2026
Agentic development from architecture through deployment, with the Production Standard contracted in writing.