Pylon AI

Use Cases

AI that solves real enterprise problems

Six battle-tested patterns we deploy with clients across industries. Each backed by proven architecture, vendor-agnostic tooling, and measurable outcomes.

Customer Support AI

Deflect 60–80% of tier-1 tickets without sacrificing CSAT

Legacy chatbots frustrate customers with rigid decision trees. Modern LLM-powered support agents understand natural language, retrieve accurate answers from your knowledge base, escalate complex cases to humans, and learn from every interaction.

The Problem

  • ▶Support volume growing faster than headcount
  • ▶Existing bots answer <30% of queries accurately
  • ▶Agents waste time on repetitive tier-1 queries
  • ▶Inconsistent answers damage brand trust
  • ▶No visibility into what customers actually ask

Our Approach

  • ✓RAG pipeline over your docs, FAQs, and ticket history
  • ✓Intent classification + confident escalation routing
  • ✓Human-in-the-loop for edge cases and high-stakes queries
  • ✓Omnichannel: web chat, email, Slack, WhatsApp
  • ✓Continuous feedback loop to improve grounding

Typical Outcomes

  • ✓60–80% deflection of tier-1 support volume
  • ✓CSAT maintained or improved vs. prior bot
  • ✓Average handle time cut 40% for escalated cases
  • ✓Agents focus on complex, high-value interactions
  • ✓12–16 week deployment to production
GPT-4o Claude 3.5 Sonnet Gemini 1.5 Pro LangChain Pinecone Weaviate Zendesk Intercom Salesforce Service Cloud
Knowledge Assistants

Turn locked institutional knowledge into an always-on expert

Enterprises lose millions annually to knowledge silos, onboarding friction, and expert bottlenecks. A well-architected knowledge assistant surfaces the right answer from thousands of internal documents, wikis, Slack threads, and databases—instantly.

The Problem

  • ▶Critical knowledge locked in PDFs, wikis, and experts' heads
  • ▶New hires take 6–12 months to reach full productivity
  • ▶Employees interrupt SMEs repeatedly for the same answers
  • ▶Compliance risk from outdated or inconsistent answers

Our Approach

  • ✓Multi-source ingestion: Confluence, SharePoint, Notion, Drive, Slack
  • ✓Hybrid search: semantic + keyword + metadata filters
  • ✓Citation-backed answers with source transparency
  • ✓Role-based access control so answers respect permissions
  • ✓Continuous re-indexing as documents update

Typical Outcomes

  • ✓40–60% reduction in internal information requests
  • ✓Onboarding time cut by 30–50%
  • ✓SMEs reclaim 5–10 hours per week
  • ✓Single source of truth replaces tribal knowledge
LangChain LlamaIndex OpenAI Embeddings Vertex AI Search Pinecone Weaviate Confluence SharePoint Notion
Document Intelligence

Extract, classify, and act on documents at enterprise scale

Contracts, invoices, regulatory filings, medical records—enterprises drown in unstructured documents. AI-powered document intelligence replaces manual review with structured extraction, anomaly detection, and automated workflows in minutes instead of days.

The Problem

  • ▶Thousands of documents reviewed manually every week
  • ▶High error rates in data extraction and classification
  • ▶Slow contract review delays revenue and procurement
  • ▶Compliance gaps from missed clauses or obligations

Our Approach

  • ✓Multi-modal extraction: PDFs, scans, handwriting, tables
  • ✓Schema-driven structured output with confidence scores
  • ✓Clause-level contract analysis and red-flag flagging
  • ✓Human review queue for low-confidence extractions
  • ✓Integration with existing ERP, CLM, and DMS systems

Typical Outcomes

  • ✓90%+ extraction accuracy on structured documents
  • ✓Contract review time cut from days to hours
  • ✓Manual review effort reduced 70–85%
  • ✓Auditable extraction with source traceability
Claude 3.5 Sonnet GPT-4 Vision Gemini 1.5 Pro AWS Textract Azure Document Intelligence LangChain Unstructured.io Docugami
Sales & RevOps AI

Give every rep the intelligence of your best closer

Revenue teams leave millions on the table because reps can’t process all available signals, personalize at scale, or get coaching in the moment. AI agents surface the right plays, draft the right message, and keep your pipeline data clean—automatically.

The Problem

  • ▶CRM data is stale, incomplete, or inconsistently entered
  • ▶Reps spend 60% of time on admin, not selling
  • ▶Forecast accuracy below 70% due to pipeline subjectivity
  • ▶Personalization at scale is impossible manually

Our Approach

  • ✓Auto-summarize calls, emails, and meetings into CRM
  • ✓ICP scoring and account prioritization from intent signals
  • ✓AI-generated personalized outreach and follow-ups
  • ✓Deal health scoring and churn risk signals in real-time
  • ✓Competitive battle-card retrieval during live calls

Typical Outcomes

  • ✓20–35% increase in rep-available selling time
  • ✓Forecast accuracy improved to 85%+
  • ✓Pipeline hygiene automated, CRM data quality up
  • ✓Outbound reply rates up 2–4x with personalization
GPT-4o Claude 3.5 Salesforce HubSpot Gong Chorus 6sense LangChain CrewAI
Developer Productivity

Ship faster with AI embedded in every stage of the SDLC

The best engineering teams aren’t just using Copilot for autocomplete. They’re deploying AI agents that review PRs, write tests, generate API documentation, flag security issues, and answer codebase questions—reducing cognitive load and accelerating delivery.

The Problem

  • ▶Engineers spend 30–40% of time on non-coding tasks
  • ▶Code review is a bottleneck for shipping velocity
  • ▶Legacy codebases are poorly documented and slow to onboard
  • ▶Test coverage is low; writing tests feels like overhead

Our Approach

  • ✓AI-assisted PR review: logic, security, and style feedback
  • ✓Automated test generation from function signatures + docstrings
  • ✓Codebase Q&A: “How does auth work?” answered in seconds
  • ✓API documentation auto-generated from code + diffs
  • ✓Security scanning with LLM-powered reasoning over CVEs

Typical Outcomes

  • ✓20–30% increase in features shipped per sprint
  • ✓PR review cycle time reduced 40%
  • ✓Test coverage increases without slowing velocity
  • ✓New engineers productive in weeks, not months
GitHub Copilot Claude for Code GPT-4o Amazon CodeWhisperer LangChain Tree-sitter GitHub Actions GitLab CI
Operations Automation

Automate the work that bogs down your best operators

From IT service management to finance operations and HR workflows, AI agents handle multi-step processes end-to-end—routing requests, gathering approvals, updating systems, and flagging exceptions—so your operators focus on high-judgment work.

The Problem

  • ▶High-volume, repetitive workflows drain skilled operators
  • ▶Process exceptions fall through the cracks without visibility
  • ▶RPA bots break on layout changes and edge cases
  • ▶Approval workflows span too many systems and too many people

Our Approach

  • ✓Agentic workflows that reason across tools, APIs, and context
  • ✓Natural-language task intake replaces rigid form-based triggers
  • ✓Exception detection with intelligent escalation
  • ✓Full audit trail for regulated and compliance-sensitive operations
  • ✓Integrations with ServiceNow, Workday, SAP, Jira, and more

Typical Outcomes

  • ✓50–75% reduction in manual task handling
  • ✓Process cycle time cut by 60% on average
  • ✓Error rates near zero on automated paths
  • ✓Ops team redirected to strategic, judgment-intensive work
CrewAI LangGraph AutoGen MCP ServiceNow Workday SAP Zapier Make
AI Billing & Monetization

Meter, price, and monetize AI consumption at any scale

Building an AI product means solving a hard financial engineering problem: how do you meter token usage, enforce quotas, package plans, and issue invoices without building a billing company? We’ve shipped this full stack for B2B and B2B2C products — developer tiers, pre-commit contracts, usage overages, discounts, and real-time cost attribution.

The Problem

  • ▶No way to meter token usage per customer, team, or feature
  • ▶Fixed pricing leaves revenue on the table as usage scales
  • ▶No visibility into which customers or features drive AI cost
  • ▶Billing infrastructure complexity blocks product launch
  • ▶Developer free tiers with no path to paid conversion

Our Approach

  • ✓Real-time token metering with per-customer attribution
  • ✓Flexible plan engine: flat, usage-based, bundled, pre-commit, hybrid
  • ✓Developer tiers with quota enforcement and upgrade flows
  • ✓Discount, promo, and enterprise contract management
  • ✓Stripe / Zuora / Chargebee integration or custom billing backend

Typical Outcomes

  • ✓Usage-based pricing live within weeks, not quarters
  • ✓Accurate per-customer AI cost and margin reporting
  • ✓Revenue scales with consumption — no pricing ceiling
  • ✓Billing becomes a growth lever, not an operational bottleneck
Stripe Zuora Chargebee Token Metering Usage-Based Pricing Pre-Commit Plans B2B2C Billing Cost Attribution

Ready to explore a use case for your business?

We’ll scope a tailored approach, identify the right technology, and outline a path from proof-of-concept to production—typically in our first conversation.

Schedule a Discovery Call Explore Our Services