🤖 AI & Machine Learning (ML)

From Data to Decisions — Safe, Scalable, and Proven

AI & Machine Learning (ML) should turn data into dependable decisions—without risking privacy, compliance, or runaway cost.
SolveForce builds AI/ML as a system: governed data → reliable pipelines → feature stores → models (classical + deep + LLM) → guardrails for safety → MLOps for scale → evidence in your SIEM so you can prove quality and control.

This page connects to:

• Foundations → /solveforce-ai • Data → /etl-elt • /data-warehouse • Tokenization → /tokenization
• Search & RAG → /vector-databases • Security → /ai-cybersecurity • Ops → /siem-soar
• Platforms → /cloud • /kubernetes • /serverless • Edge → /edge-data-centers • RF → /private-5g
• Governance → /data-governance • Compliance → /hipaa • /pci-dss • /nist • /fedramp

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🎯 Outcomes (business-first, not model-first)

• Better decisions— forecasts, recommendations, anomaly alerts, and copilots that are traceable and auditable.
• Lower time-to-value— reusable data contracts and features shorten the path from idea to production.
• Predictable spend— token budgets, $/inference targets, and auto-scaling keep costs in check.
• Risk managed— privacy-by-design, policy-as-code, model guardrails, and continuous evidence.

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🧭 Architecture at a Glance (language-first AI)

Rails (Data & Events) → batch & streaming pipelines from apps, sensors, SaaS, and logs.
Semantics (Contracts & Labels) → schemas, units, and sensitivity (PII/PHI/PAN/CUI) defined in /data-governance.
Features & Models → feature store + model registry with signing and SBOMs.
Serving → APIs, batch scoring, streaming consumers, or guarded RAG with /vector-databases.
Safety & Security → policy gates, prompt/tool guardrails, DLP, key/secret custody, drift watchers.
Evidence → training lineage, evals, deployments, and actions streamed to /siem-soar.

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🧱 Core Capabilities

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🧩 Where AI Works Best (cross-sector)

• Sales & Service: lead scoring, churn, CSAT prediction; agent-assist copilots with guarded knowledge.
• Finance: fraud/risk signals, collections strategy, KYC/AML assist, treasury forecasting. → /finance-networks
• Healthcare: coding/denial insights, imaging triage, PHI-aware summarization, RPM anomaly alerts. → /healthcare-networks • /hipaa
• Manufacturing & Energy: vision QC, predictive maintenance, yield/energy optimization, DER & grid forecasts. → /industry-4-0-in-automation • /energy-and-utilities
• Logistics & Retail: ETA accuracy, slotting, demand & price elasticity, shrink detection, voice-of-customer. → /logistics • /retail
• Public Sector & Smart Cities: traffic optimization, incident triage, records summarization, call-center modernization. → /smart-cities • /government

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🔐 Security for AI (and AI for Security)

• For AI: dataset governance, PII minimization, vault-issued secrets, KMS/HSM keys, attested models, prompt/tool boundaries, request signing, rate limits, audit trails. → /key-management • /secrets-management • /ai-cybersecurity
• With AI: SOC copilots, anomaly triage, phishing/fraud classification, cloud drift detectors, policy explainers—all cited. → /siem-soar

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🧰 Solution Bundles (assemble to fit your needs)

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📐 SLO Guardrails (AI that’s measurable)

Domain	KPI / SLO	Target (Recommended)
RAG	Citation coverage	= 100%
	Refusal correctness	≥ 98%
NLP/LLM	p95 response latency (in-region)	≤ 2–6 s
Vision	p95 inference latency (edge)	≤ 10–20 ms
Forecasting/Anomaly	MAPE / Recall@fixed FP	≤ 5–12% / ≥ 85–95%
Data freshness	Source→feature→serve	≤ 1–60 s (stream) / ≤ 5–30 min (batch)
Drift detection	Detection→ticket	≤ 30–60 min
Security	Secrets via vault / long-lived keys	= 100% / = 0
Cost	$/question (LLM) within budget	±10%
Evidence	Train/eval/deploy logs to SIEM	≤ 60–120 s

When a guardrail trips, SOAR opens a case and runs mitigations (degrade to cached answers, roll back model, tighten retrieval filters, rotate keys), capturing artifacts. → /siem-soar

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✅ Acceptance Tests & Artifacts (we keep the receipts)

• Dataschema compat checks, lineage coverage %, DQ pass rates, PII scan reports.
• Modelsreproducible training hash, eval metrics vs gold sets, bias & privacy tests, approval records.
• Servingp95 latency under load, error rate, idempotency/DLQ behavior, rate-limit responses.
• RAGcitation set diffs, refusal ledger, hallucination red-team results.
• Securityvault access logs, KMS/HSM rotations, prompt/tool firewall logs.
• Cost$/inference, GPU utilization, token budgets; FinOps forecast accuracy (30/90d).
  All routed to /siem-soar and summarized for QBRs/audits.

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🛠️ Implementation Blueprint (no-surprise delivery)

1) Define decisions & KPIs — what business decisions need support? success metrics? (e.g., MAPE, recall, CSAT lift, $/question).
2) Inventory data — sources, contracts, sensitivity labels, residency & retention.
3) Stand up platform — pipelines, feature store, registry, serving (K8s/serverless), observability.
4) Build models — baseline + challenger; eval suites; model cards; bias & robustness tests.
5) Guardrails — prompt/tool firewalls, label/ACL pre-filters for retrieval, DLP, vault, KMS/HSM.
6) Pilot & rings — shadow → advisory → supervised automation → full automation; rollback & manual override paths.
7) Operate — drift & cost monitors, retraining cadence, FinOps reviews, incident runbooks; artifacts stored in Knowledge Hub.
→ Deep dives: /solveforce-ai • /vector-databases • /ai-cybersecurity

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📝 AI/ML Intake (copy–paste & fill)

• Use-cases & KPIs(forecasting, anomaly, vision, RAG, copilot; target metrics)
• Data sources(DB/CSV/SaaS/sensors/docs), sensitivity labels, residency/retention needs
• Latency & volume(QPS, batch windows, edge requirements, GPU needs)
• Security posture(IdP/SSO/MFA, vault/KMS/HSM, network perimeters, DLP)
• Compliance(SOC2/ISO/NIST/HIPAA/PCI/FedRAMP), BAAs/DPAs required
• Operations(managed vs co-managed, change windows, reporting cadence)
• Budget(ROM vs build-ready), token/$ targets, timeline & success criteria

We’ll return a design-to-quote with architecture, supplier options, SLO-mapped pricing, compliance overlays, and an evidence plan you can reuse in audits and QBRs.
Or skip ahead to /customized-quotes.

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📞 Let’s Turn Data into Decisions—Safely, Quickly, and With Proof

• Call: (888) 765-8301
• Email: contact@solveforce.com

From forecasts to RAG assistants, from edge vision to cloud platforms, we’ll build AI that earns trust—and keeps the receipts.

Key terms in plain language

Open a term for a concise explanation of language used on this page.

⏱️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.

🔀SD-WAN

Software-defined wide area networking. It manages multiple connections and chooses paths based on application needs, performance, and policy to improve resilience and control.

🧰Software as a Service (SaaS)

Software accessed as an online service instead of being installed and maintained entirely on the customer’s own computers or servers.

🛡️Cybersecurity

The practices and controls used to protect identities, devices, networks, applications, and data from unauthorized access, disruption, or manipulation.

✅Multi-Factor Authentication (MFA)

A login control requiring more than one form of verification, such as a password plus an authenticator app, security key, or biometric factor.

🔗API

An application programming interface is a defined way for software systems to exchange data or request functions from one another.

🤖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.