SolveForce Intelligent Infrastructure
AI & Machine Learning aren’t just algorithms — they’re architectures of intelligence. SolveForce designs and delivers AI-ready infrastructure that spans compute, connectivity, data pipelines, and compliance guardrails, giving enterprises the full loop: model → data → inference → evidence.
Quote output: AI architecture deck + GPU/CPU BoM + data/SLO guardrails + acceptance tests + cloud/provider options + compliance overlays + SIEM evidence plan.
🎯 What You Get in a SolveForce AI/ML Quote
- Compute rails — bare-metal GPU clusters, hyperconverged fabric (VM/K8s), edge inference nodes.
- Data fabrics — pipelines (ETL/ELT, CDC), warehouses/lakes, vector databases for RAG.
- Security guardrails — tokenization, IAM, ZTNA, key custody, zero-trust enclaves for model training.
- Provider diversity — cloud GPU vs on-prem GPU vs colocation, hybrid AI bursts.
- SLO-mapped pricing — training throughput, inference latency, accuracy guardrails, evidence capture.
- Compliance overlays — HIPAA (medical AI), PCI (fintech AI), SOC2/NIST (governance), FedRAMP (gov/defense).
- Acceptance plan — training reproducibility, drift detection, lineage evidence, RAG citation refusal tests.
🛣️ Quote Process for AI/ML
- Scope & Intake (Day 0–3) — use-case definition: model training, inference, RAG, IoT/edge AI.
- Discovery & Supplier Graph (Day 3–10) — GPU availability, cloud vs edge economics, data gravity.
- Design-to-Quote (Day 7–14) — architecture deck: compute, storage, fabric, AI lifecycle guardrails.
- Review & refine (Day 14–20) — cost vs performance, cloud/hybrid splits, model/data SLOs.
- Finalize & order (Day 20+) — GPU orders, colocation racks, private cloud AI footprint, acceptance artifacts.
📐 Global AI/ML SLO Guardrails
| Domain | KPI / SLO (p95 unless noted) | Target (typical) |
|---|---|---|
| Training | GPU utilization | ≥ 80–90% |
| Inference | Latency (edge→core) | ≤ 10–50 ms |
| Data | CDC parity / lineage | = 100% |
| Vector DB | Query latency | ≤ 25 ms |
| Model Trust | Drift detection cycle | ≤ 24 h |
| Security | Key rotation / vault access | ≤ 60 s |
| Evidence | RAG citation logs | 100% logged |
| Continuity | Model restore (Tier-1) | ≤ 15 min |
🧪 Acceptance Evidence (AI-specific)
- Compute: GPU burn-in logs, PCIe/NVLink bandwidth tests, thermal envelopes.
- Data: CDC parity checks, lineage graphs, immutability proofs.
- AI Models: reproducibility hash, training run logs, fairness/bias audit outputs.
- RAG/Vector: ACL pre-filters, refusal/citation logs, embeddings checksum.
- Security: key vault rotations, IAM/ZTNA admission logs, tokenization evidence.
- Continuity: model snapshot restore timings, failover tests, DR checkpoints.
All evidence streams into SIEM/SOAR, included in your quote.
🔗 Related SolveForce Services (AI Hub)
AI & Machine Learning tie into:
- Data/AI → /data-warehouse, /etl-elt, /vector-databases
- Compute → /bare-metal-gpu, /dedicated-servers, /kubernetes
- Security → /ztna, /tokenization, /key-management
- IoT/Edge → /suite-of-internet-of-things-iot, /edge-computing
- Cloud → /public-cloud, /private-cloud, /hybrid-cloud
- Compliance → /hipaa, /pci-dss, /soc2, /fedramp
📝 AI/ML Quote Intake
Use Case — training, inference, RAG, IoT/edge AI, automation, analytics
Compute — GPU nodes (type/qty), CPU support, RAM, NVMe vs SAN, edge vs core
Data — sources (DB/CSV/docs), pipeline type (CDC/ETL/ELT), warehouse vs lake vs vector DB
Cloud/Infra — public/private/hybrid, regions, colocation vs hyperscale GPU
Security — tokenization, IAM/PAM, ZTNA, DLP, key/vault custody
Compliance — HIPAA/PCI/SOC2/NIST/FedRAMP/BAAs/DPAs
Continuity — model immutability, DR tiers, restore SLA
Ops — MSP/MSSP, SIEM/SOAR evidence, reporting cadence
Budget & Timeline — pilot vs enterprise rollout, SLO priorities
Email to contact@solveforce.com.
📞 Ready for an AI/ML Quote?
- Call: (888) 765-8301
- Email: contact@solveforce.com
SolveForce delivers AI-ready infrastructure with suppliers, architecture, compliance, and evidence — from A to Z.