AI Architecture Consultation
Define system boundaries, stack choices, and delivery sequence before engineering costs spike.
Private AI Consulting
I help teams architect and implement private/on-prem AI systems, secure retrieval pipelines, and agentic workflows that survive real production constraints, not just demo-day conditions.
Services
Focused engagements for founders and teams that want experienced technical direction and implementation depth, without agency bloat.
Define system boundaries, stack choices, and delivery sequence before engineering costs spike.
Design secure deployments around your infrastructure, compliance posture, and data ownership requirements.
Build retrieval pipelines with reliable indexing, ranking, and evaluation loops you can trust in production.
Ship practical agents integrated into existing operations with guardrails, auditability, and fallback paths.
Paid Consultations
One primary next step: paid technical consultation. No coaching funnel, no discovery theater.
$250
Who it's for: Founders and operators who need high-leverage technical direction fast.
What we cover: Problem framing, architecture sanity check, and immediate risk review.
What you leave with: A practical next-step plan with stack and priority recommendations.
$500
Who it's for: Technical teams that need a deeper design review before implementation.
What we cover: Private AI setup, RAG design choices, agent boundaries, and production constraints.
What you leave with: A scoped architecture outline and decision log for execution.
$1,200
Who it's for: Companies with an existing build or roadmap that needs senior-level critique.
What we cover: 90-minute session plus async notes on reliability, security, and delivery risk.
What you leave with: A prioritized remediation and implementation plan.
Selected Work
Problem: Teams needed private knowledge retrieval without cloud lock-in.
Technical value: Built a local-first knowledge engine for deterministic offline retrieval in constrained environments.
Client relevance: Directly relevant to internal copilots and privacy-sensitive deployments.
Problem: RAG systems at scale require reliable, low-latency retrieval infrastructure.
Technical value: Designed decentralized vector infrastructure focused on retrieval quality and durability.
Client relevance: Relevant for AI products where ownership, performance, and uptime matter.
Problem: High-risk fintech environments needed stronger fraud detection and explainable outcomes.
Technical value: Implemented forensic analytics with anomaly detection, identity signals, and analyst review loops.
Client relevance: Shows security-first architecture for high-consequence AI systems.
Engagement Model
Step 1
We align on business objective, constraints, and what an effective build should look like.
Step 2
If there is fit, I provide a clear engagement scope with deliverables and decision boundaries.
Step 3
Serious projects only: fast decisions, technical ownership, and durable systems over hype.

About
I'm Sam Paniagua. I work with founders and teams that need rigorous architecture and hands-on AI implementation, from secure RAG systems to agent-enabled product workflows.
Clients hire me when they need direct technical judgment and someone who can move from system design to production delivery without hand-offs.
Contact
If you are planning a private AI build, secure retrieval system, or agent-driven workflow, start here.
Direct email: sam@hiveforensics.com
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