🤖 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.

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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.

🧭 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.

🧱 Core Capabilities

1) Data Engineering for AI

  • CDC/ELT pipelines (dbt-ready), time-series ingestion, document parsing with layout retention. → /etl-elt
  • Contracts & DQ — schema compatibility gates; tests for completeness, uniqueness, ranges, and drift; lineage to the column. → /data-warehouse
  • Tokenization & chunking — sentence/section/AST-aware segmentation for text/code; labels propagate to tokens and chunks. → /tokenization

2) Feature & Model Platform

  • Feature store with versioning, freshness SLAs, training/serving parity.
  • Model registry with signatures, SBOMs, approvals; reproducible training; canary & shadow deploys.
  • Serving on Kubernetes (real-time) or serverless (bursty); GPU nodes and autoscale as needed. → /kubernetes • /serverless

3) Model Types We Productionize

  • Classical ML: regression, tree ensembles, GLMs for tabular decisions.
  • Time-series: forecasting, capacity & demand planning, anomaly detection.
  • Computer Vision: quality inspection, OCR, safety (PPE, proximity), document understanding.
  • NLP / LLMs: classification, summarization, extraction, and RAG assistants that cite or refuse. → /vector-databases • /solveforce-ai

4) Guardrails & Responsible AI

  • Cite-or-refuse: assistants must show sources or decline.
  • Prompt & tool firewalls: allow-listed functions, schema-validated arguments, jailbreak/exfil checks.
  • Privacy: DLP/tokenization; regional perimeters; purpose & retention controls. → /ai-cybersecurity • /dlp

5) MLOps & Observability

  • Pipelines: training jobs, eval suites, artifact tracking; GitOps for infra and config.
  • Monitoring: latency, throughput, error rate, feature drift, concept drift, and cost per decision.
  • Automation: /siem-soar runs safe playbooks (degrade model, roll back, rotate keys, pause routes).

🧩 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

🔐 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

🧰 Solution Bundles (assemble to fit your needs)

A) RAG Starter (Guarded Knowledge Assistants)

  • Corpus prep, tokenization & labels, vector DB, retrieval filters (labels/ACLs/region), cite-or-refuse responses, eval sets (factuality/citation/cost). → /vector-databases

B) Vision on the Edge

  • Edge GPU nodes, Private 5G/Wi-Fi layout, camera pipelines, on-box pre/post processing, cloud feedback loop with active learning; EHS & QC use-cases. → /edge-data-centers • /private-5g

C) Time-Series Forecasting & Anomaly

  • Data contracts for telemetry, seasonal/holiday features, probabilistic forecasts, drift watchers; integrates with SD-WAN or plant controls for safe actions. → /sd-wan

D) ML Platform on Kubernetes

  • Feature store + registry, policy controller for model admission, signed artifacts, canary/shadow, OTel traces, cost dashboards; GitOps end to end. → /kubernetes

E) Responsible AI & Compliance

  • Risk register for AI, model cards, dataset statements, DPIAs, human-in-the-loop gates, audit exports for SOC 2/ISO/NIST/HIPAA/PCI/FedRAMP. → /grc • /nist • /hipaa • /pci-dss • /fedramp

F) AI for Contact Centers

  • Intent classification, next-best action, PCI-safe redaction, sentiment & summarization; Teams/CRM/ITSM integrations; QoS and MOS SLOs. → /ccaas • /hosted-voice

📐 SLO Guardrails (AI that’s measurable)

DomainKPI / SLOTarget (Recommended)
RAGCitation coverage= 100%
Refusal correctness≥ 98%
NLP/LLMp95 response latency (in-region)≤ 2–6 s
Visionp95 inference latency (edge)≤ 10–20 ms
Forecasting/AnomalyMAPE / Recall@fixed FP≤ 5–12% / ≥ 85–95%
Data freshnessSource→feature→serve≤ 1–60 s (stream) / ≤ 5–30 min (batch)
Drift detectionDetection→ticket≤ 30–60 min
SecuritySecrets via vault / long-lived keys= 100% / = 0
Cost$/question (LLM) within budget±10%
EvidenceTrain/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

✅ Acceptance Tests & Artifacts (we keep the receipts)

  • Data: schema compat checks, lineage coverage %, DQ pass rates, PII scan reports.
  • Models: reproducible training hash, eval metrics vs gold sets, bias & privacy tests, approval records.
  • Serving: p95 latency under load, error rate, idempotency/DLQ behavior, rate-limit responses.
  • RAG: citation set diffs, refusal ledger, hallucination red-team results.
  • Security: vault 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.

🛠️ 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

📝 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.

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

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