🤖 SolveForce AI — The Pragmatics That Apply Meaning in Context

AI is pragmatics: it applies meaning in context, learns from recursion, and makes systems self-correcting.

At SolveForce, AI isn't a chatbot you deploy — it's the pragmatic layer that makes infrastructure adaptive, self-healing, and continuously improving. We architect AI as a feedback loop that collapses entropy and increases coherence.

Why AI is Pragmatics

In language, pragmatics is about using meaning in context: tone, intent, implication, recursion.
In infrastructure, AI enables:

• Context-aware decision-making (network path selection based on latency + cost + security)
• Anomaly detection (what's normal vs. abnormal for this user, this time, this app)
• Self-healing (auto-remediation of drift, misconfigurations, performance degradation)
• Continuous improvement (learning from past incidents to prevent future ones)

Without AI: Static rules, manual correlation, slow response.
With SolveForce AI: Systems that think, adapt, and get smarter over time.

Our AI Solutions

🧠 AI-Powered Infrastructure

Embed intelligence into connectivity, cloud, and security

• AI-Driven SD-WAN

  • Dynamic path selection (latency, jitter, packet loss)
  • Predictive failover (switch before circuit fails)
  • Application QoS (voice, video, data prioritization)

• AIOps (Artificial Intelligence for IT Operations)

  • Anomaly detection across logs, metrics, traces
  • Root cause analysis (RCA) automation
  • Auto-remediation (restart service, scale resources, reroute traffic)

• Network Performance Optimization

  • Traffic shaping based on real-time analysis
  • Congestion prediction and avoidance
  • WAN optimization (compression, deduplication, caching)

• Cloud Cost Optimization (FinOps AI)

  • Right-sizing recommendations (downsize overprovisioned VMs)
  • Reserved instance / savings plan suggestions
  • Anomaly detection (sudden spend spikes)

Platforms: Cisco Meraki (AI wireless), Juniper Mist (AI-driven networking), Dynatrace (AIOps), CloudHealth (FinOps)

🔒 AI-Powered Security

Detect threats that rules-based systems miss

• Behavioral Analytics (UEBA)

  • User and entity behavior analytics
  • Detect insider threats, compromised accounts
  • Risk scoring (login location, time, device)

• AI-Driven SIEM/SOAR

  • Correlation of millions of events
  • Automated playbooks (isolate host, block IP, revoke token)
  • Threat hunting with ML models

• NDR with ML (Network Detection & Response)

  • Detect lateral movement, C2 traffic, data exfiltration
  • Unsupervised learning (no predefined signatures)

• AI-Powered Email Security

  • Phishing detection beyond keyword matching
  • Business email compromise (BEC) detection
  • Deep fake voice/video detection

• Fraud Detection

  • Payment fraud, account takeover, bot attacks
  • Real-time scoring and blocking

Vendors: Darktrace, Vectra AI, CrowdStrike Falcon, Microsoft Sentinel, Splunk

💬 Conversational AI & Automation

Natural language interfaces for infrastructure and support

• AI Chatbots for IT Support

  • Password resets, ticket creation, KB search
  • Sentiment analysis and escalation
  • Platforms: Microsoft Power Virtual Agents, IBM Watson

• Voice AI for UCaaS

  • Call transcription and summarization
  • Sentiment analysis (customer satisfaction scoring)
  • Auto-attendant with NLP (natural language IVR)

• AI-Powered Contact Centers

  • Real-time agent assist (suggest responses, KB articles)
  • Call routing based on intent detection
  • Quality monitoring (compliance, tone, resolution)

Platforms: Five9 (AI Studio), Genesys (AI/ML engine), Amazon Connect (Lex + Contact Lens)

🤖 Machine Learning & Data Science

Build, train, deploy custom AI models

• AI/ML Platforms

  • AWS SageMaker — End-to-end ML workflow
  • Azure Machine Learning — AutoML, MLOps, GPU compute
  • Google Vertex AI — Unified ML platform
  • Databricks — Collaborative data science

• AI Infrastructure

  • GPU compute — NVIDIA A100, V100 for training
  • TPUs — Google's Tensor Processing Units
  • Bare metal AI servers — High-memory, NVMe storage

• AI Use Cases

  • Predictive maintenance (IoT sensors → failure prediction)
  • Computer vision (security cameras, quality inspection)
  • NLP (document classification, sentiment analysis)
  • Recommendation engines (e-commerce, content)
  • Forecasting (demand, inventory, capacity planning)

📊 AI for Business Intelligence

Turn data into actionable insights

• Predictive Analytics

  • Sales forecasting, churn prediction, demand planning
  • Customer lifetime value (CLV) modeling

• Anomaly Detection

  • Fraud, network intrusion, system failures
  • Outlier detection in metrics

• Natural Language Query (NLQ)

  • Ask questions in plain English
  • "What was our revenue last quarter in the Northeast region?"

Tools: Microsoft Power BI (AI visuals), Tableau (Einstein Discovery), ThoughtSpot (Search & AI)

🏭 AI for Industry 4.0

Smart manufacturing, IoT, and edge AI

• Predictive Maintenance

  • Sensor data → ML model → predict failures before they happen
  • Reduce downtime, optimize spare parts inventory

• Computer Vision for Quality Control

  • Detect defects in real-time on assembly line
  • Replace manual inspection

• Edge AI

  • Run inference on edge devices (cameras, gateways, robots)
  • Low-latency decisions without cloud round-trip

• Digital Twins

  • Virtual replicas of physical assets
  • Simulate scenarios, optimize operations

Platforms: NVIDIA Jetson (edge AI), AWS IoT Greengrass (edge ML), Azure Digital Twins

How AI Fits the Language-First Model

Result: AI systems that don't just predict — they understand context, enforce ethics, and improve coherence over time.

Industries We Serve

AI + the Other Four Pillars

AI enhances every layer:

• 🌐 Connectivity — SD-WAN path selection, traffic shaping, predictive failover
• 🖧 Networks & Data Centers — AIOps for network ops, capacity planning, anomaly detection
• ☁️ Cloud — FinOps cost optimization, auto-scaling, rightsizing recommendations
• 📞 Phone — Call transcription, sentiment analysis, NLP IVR
• 🔒 Security — UEBA, AI-driven SIEM, behavioral threat detection

Integration example: A manufacturer uses edge AI for real-time quality inspection, predictive maintenance models to forecast equipment failures, AIOps to optimize network performance, AI-driven SIEM to detect OT/IT security threats, and digital twins to simulate production changes.

Why Choose SolveForce for AI

✅ End-to-end ML infrastructure — From data ingestion to model deployment
✅ GPU/TPU compute — High-performance training and inference
✅ Industry-specific models — Pre-trained for healthcare, finance, manufacturing
✅ Ethical AI — Bias detection, explainability, compliance (GDPR, CCPA)
✅ MLOps maturity — CI/CD for models, versioning, monitoring, retraining
✅ Language-first design — AI as pragmatics, embedded in infrastructure

Get Started

Ready to make your infrastructure self-correcting and continuously improving?

📞 (888) 765-8301
✉️ contact@solveforce.com

Quick links:

• AI Readiness Assessment — Is your data ready for AI?
• AI Use Case Workshop — Identify high-value AI projects
• ML Platform Comparison — SageMaker vs Azure ML vs Vertex AI

Related Pages

• Connectivity — AI-driven SD-WAN
• Cloud — AI/ML platforms (SageMaker, Azure ML, Vertex AI)
• Security — AI-powered threat detection
• Networks & Data Centers — AIOps for network operations
• AI Services — Deep dive into machine learning solutions

SolveForce — AI as pragmatics. Systems that learn. Infrastructure that self-corrects.

📞 (888) 765-8301 • ✉️ contact@solveforce.com