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.
0 wk
POC → production
0TB+
Daily throughput
0 hr
Audit delivery
Live production signal
0 wk
POC → production
0TB+
Daily throughput
0 hr
Audit delivery
Trusted sectors
0%
p95 latency cut (SaaS audit)
0 wk
POC → production (federal fraud)
0TB+
Daily throughput (federal fraud)
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. Cut idle spend, keep headroom, show the CFO the dollars.
Lead intake, missed-call text back, internal knowledge search, and automated reporting. Fixed scope, delivered in days, priced on a 30-minute call.
Target Industries
Production DS/ML across finance, federal, real estate, telecom, and higher education, plus operator pages for the workflows people actually search: MGAs, credit unions, lease abstraction, freight audit, RCM, staffing, construction ops, licensed iGaming, and FFL.
Operator verticals
Submission intake, ACORD extraction, claims fraud, and quote-to-bind acceleration for $30–200M GWP MGAs, without replacing the policy admin system.
Explore →Card and ACH fraud, BSA/AML alert triage, and KYC document intelligence for $200M–$5B asset credit unions, built to survive a NCUA exam.
Explore →Lease abstraction into Yardi or MRI, tenant-churn models, and acquisition DD extraction for 5k–50k unit operators, not listing-copy chatbots.
Explore →Freight bill audit, accessorial anomaly detection, and BOL/POD extraction for 50–500 person 3PLs and brokers. Document intelligence plus fraud-style scoring on invoices.
Explore →Commitment exception extraction, closing-package completeness, and stacking-order document intelligence for regional title companies and correspondent lenders.
Explore →Demand forecasting, fill-rate versus inventory tradeoffs, and EDI exception agents for $50–500M wholesale distributors still running ERP plus spreadsheets.
Explore →Denial prediction, prior-auth packet assembly, and payer-policy RAG for billing companies, DSOs, and specialty groups. Ops AI, not clinical diagnosis.
Explore →Resume and credential extraction, req-to-candidate ranking, and recruiter research agents for $20–200M staffing firms and PEOs, implemented in your ATS.
Explore →Geo-compliance, KYC/AML, bonus-abuse detection, and responsible-gaming retrieval for licensed sportsbook and iGaming operators. Ad channels are limited. Production systems are not.
Explore →Serialized inventory, ATF recordkeeping support, dealer-portal search, and compliant organic product pages for manufacturers and FFLs. Google will not run the ads. Operations still need systems.
Explore →Submittal review against specs, COI tracking, and RFI/change-order document agents for $50–500M specialty contractors, not takeoff software wars.
Explore →Proven depth
Fraud detection, risk modeling, and governed ML at transaction scale.
Explore →Federal-scale data systems, compliance posture, and high-throughput pipelines.
Explore →Portfolio analytics, valuation intelligence, and operational automation.
Explore →GPS traces, address geocoding, and spatial analytics for product growth and market expansion.
Explore →Large-scale data platforms, network analytics, and customer intelligence.
Explore →Institutional analytics, research computing, and governed campus AI.
Explore →Growth focus
Claims automation, underwriting intelligence, and fraud detection, built on the same real-time fraud and risk-modeling pipelines we shipped at federal financial scale (10TB+/day).
Explore →Production ML and governed Gen AI for regulated health and biotech teams.
Explore →Contract analysis, discovery workflows, and retrieval systems at enterprise scale.
Explore →Predictive maintenance, demand forecasting, and industrial data platforms.
Explore →Asset optimization, grid analytics, and governed AI in regulated infrastructure.
Explore →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+
$2,500 audit → $15k–$75k scoped → from $25k/mo embedded
Post-engagement
High dependency on firm to operate
Full IP transfer; your team owns it
Engagements
One ladder, same numbers on every page. Diagnostic audit, production mandate, principal embed, or a fixed-scope small-business package. Intake is selective. The Production Standard is contracted in writing.
$2,500
Database performance & architecture review
A focused, read-only examination of your Postgres or Supabase estate. Prioritized findings and a remediation roadmap, delivered within forty-eight hours.
$15k–$75k
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.
From $25k/month
Ongoing strategic partnership
The principal embedded within your organization, architecting roadmaps, shipping systems, and elevating your team's capability over a sustained engagement.
Quoted on a 30-minute call
Small-business automation, 2–5 days
Lead intake, internal knowledge search, and automated reporting. The price is fixed before work starts and set on a 30-minute discovery call. There is no published flat rate.
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 · 2026
Agentic development from architecture through deployment, with the Production Standard contracted in writing.