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)
- ComputeGPU burn-in logs, PCIe/NVLink bandwidth tests, thermal envelopes.
- DataCDC parity checks, lineage graphs, immutability proofs.
- AI Modelsreproducibility hash, training run logs, fairness/bias audit outputs.
- RAG/VectorACL pre-filters, refusal/citation logs, embeddings checksum.
- Securitykey vault rotations, IAM/ZTNA admission logs, tokenization evidence.
- Continuitymodel 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.
Key terms in plain language
Open a term for a concise explanation of language used on this page.
Bandwidth
The amount of data a connection can carry in a given time, usually measured in Mbps or Gbps. More bandwidth supports more users, devices, and simultaneous applications.
Latency
The time it takes data to travel between two points. Lower latency improves voice, video meetings, cloud applications, gaming, and other real-time services.
Zero Trust
A security model that does not automatically trust a user or device because of its location. Access is continuously verified and limited to what is necessary.
Identity and Access Management (IAM)
The systems and policies that determine who a user is, what resources they may access, and how that access is authenticated and reviewed.
Colocation
Placing customer-owned servers and network equipment in a professionally operated data center that provides power, cooling, physical security, and connectivity.
Artificial Intelligence (AI)
Software designed to perform tasks involving prediction, classification, generation, reasoning, or decision support. Business use still requires clear data, governance, security, and human accountability.