Services
Enterprise AI services from strategy through production.
Pylon AI combines executive advisory, systems architecture, inference strategy, and forward-deployed engineering so enterprises can move from AI ambition to operating advantage — with speed and control.
AI Strategy & Roadmaps
Strategy without execution is aspiration. We build AI roadmaps that are immediately actionable — sequenced, resourced, and connected to the business outcomes that matter to your board and investors.
We work with CEO, CIO, CTO, CAIO, and CDO leadership to cut through vendor noise and produce a clear picture of where AI creates durable advantage and where it does not.
Discuss your AI strategyWhat's included
- AI maturity assessment across people, process, data, and technology
- Use-case portfolio scoring: value, feasibility, and strategic fit
- Operating model design: build, buy, and partner decisions
- Sequenced 12–24 month AI investment roadmap
- Executive and board presentation materials
- AI talent strategy and organizational design recommendations
- Vendor and platform selection framework
Who this is for
CEOs and C-suite leaders who need to move from AI interest to AI investment with confidence. CIOs and CTOs making platform bets. CAIOs building the organizational AI playbook.
Enterprise AI Architecture
AI systems that cannot survive enterprise security review, latency requirements, and cost scrutiny are not production systems — they are experiments. We design systems that are production-ready from the first architecture review.
Our architects have deep expertise across agentic orchestration, multi-model routing, RAG pipelines, vector stores, data integration, MCP protocols, and inference infrastructure across all major cloud platforms.
Review your architectureWhat's included
- End-to-end agentic and multi-model system architecture design
- RAG pipeline design: chunking, embedding, retrieval, and reranking strategies
- Model Context Protocol (MCP) server design and integration patterns
- Data pipeline and integration architecture for AI workloads
- Security, access control, and data governance design
- Latency, throughput, and cost optimization design
- Enterprise integration patterns: API, headless, and event-driven
- Architecture review and technical due diligence
Who this is for
CTOs and engineering leaders designing AI systems at scale. Teams that need a second opinion before committing to an architecture. Companies doing technical due diligence on AI investments.
Agentic AI & Automation
The value of AI is not in answering questions — it is in taking actions. Agentic systems that can reason, plan, call tools, delegate to sub-agents, and complete complex multi-step tasks represent the next frontier of enterprise AI value creation.
We design and build production-grade agentic systems using LangGraph, CrewAI, AutoGen, Semantic Kernel, Amazon Bedrock Agents, and custom MCP-based orchestration frameworks. Every system includes evaluation, human-in-the-loop controls, and observable state management.
Build your agentic systemWhat's included
- Agent architecture design: single-agent, multi-agent, and hierarchical patterns
- Tool and function design: API integration, database access, web actions
- Memory systems: short-term, episodic, and semantic memory design
- Human-in-the-loop and approval workflow design
- Agent evaluation frameworks: correctness, safety, and cost
- Workflow orchestration: conditional branching, parallelism, and error recovery
- MCP server development for enterprise tool integration
- Enterprise copilot development (internal and customer-facing)
Who this is for
Operations teams automating complex workflows. Product teams building AI-native products. Engineering teams adding agentic capabilities to existing platforms.
Inference Strategy
Model selection is one of the highest-leverage decisions in an enterprise AI program. The wrong choice costs millions in unnecessary compute spend, adds latency that kills user adoption, and creates vendor lock-in that limits your future flexibility.
We help enterprises design inference strategies that balance cost, latency, quality, and reliability across OpenAI, Anthropic, Google, AWS Bedrock, Meta open models, Mistral, and custom fine-tuned models — with routing logic that picks the right model for each request.
Optimize your inferenceWhat's included
- Model evaluation and selection across frontier and open-weight models
- Multi-model routing design: cost, quality, and latency-based routing
- Prompt engineering and optimization for cost and quality
- Fine-tuning strategy: when to fine-tune vs. RAG vs. few-shot
- Inference infrastructure design: managed APIs vs. self-hosted (vLLM, Together AI)
- GPU and silicon strategy for custom inference workloads
- Latency optimization: caching, batching, and streaming design
- Cost attribution and chargeback model design
Who this is for
Engineering and finance leaders managing AI infrastructure costs. CTOs choosing between managed and self-hosted inference. Teams scaling AI workloads and facing rising token spend.
Forward-Deployed Engineering
The best AI strategy document in the world does not ship software. Pylon AI engineers embed directly with your team — working in your codebase, with your security controls, within your sprint cadence — to move prototype to production with accountability for real outcomes.
We are builders first. Our forward-deployed engineers are senior practitioners who have shipped AI systems at enterprise scale — not junior consultants learning on your project.
Embed Pylon AI engineersWhat's included
- Sprint-based embedded engineering with weekly delivery milestones
- Prototype to production: integration, testing, and deployment engineering
- Enterprise integration: REST APIs, event systems, data pipelines
- Security hardening: prompt injection defense, data isolation, access controls
- Performance engineering: latency profiling, caching, and optimization
- Evaluation and testing frameworks for AI system quality
- Continuous deployment and monitoring setup
- Knowledge transfer and internal team upskilling throughout the engagement
Who this is for
Engineering teams that have validated an AI concept and need production-grade implementation. Organizations that lack the internal AI engineering depth to cross the prototype-to-production gap at speed.
Enterprise AI Security
AI systems introduce a new cybersecurity surface: prompt injection, data exfiltration, model and dataset supply-chain risk, agent privilege escalation, insecure tool use, and uncontrolled model outputs. We treat security as a day-one architecture requirement — not a pre-launch checklist — so AI infrastructure is designed from the ground up for enterprise security review, SOC 2 readiness, HIPAA-aligned controls where applicable, and regulated operating environments.
Our approach combines AI threat modeling, application security, cloud security, model governance, and runtime controls aligned to OWASP LLM guidance, MITRE ATLAS, NIST AI RMF, ISO/IEC 42001, and enterprise security review requirements.
Assess your AI security postureWhat's included
- Day-one secure-by-design architecture for LLM, RAG, MCP, and agentic systems
- AI threat modeling across models, data, tools, agents, cloud boundaries, and users
- Prompt injection, jailbreaking, insecure output handling, and data leakage controls
- Agent permission design: tool allowlists, scoped credentials, approvals, and sandboxing
- Model, prompt, dataset, and dependency supply-chain security review
- Private endpoints, VPC isolation, tenant boundaries, secrets, and identity architecture
- PII detection, redaction, retention, and sensitive-data handling patterns
- AI red-team testing, evals, guardrails, audit trails, and incident response playbooks
- Security control mapping to OWASP LLM, MITRE ATLAS, NIST AI RMF, ISO/IEC 42001, SOC 2 readiness, HIPAA-aligned controls where applicable, and EU AI Act readiness
Who this is for
CISOs, CTOs, CAIOs, platform teams, and risk leaders deploying copilots, RAG systems, model gateways, or autonomous agents that need to pass enterprise security review and operate safely in production.
AI Governance & Economics
AI without observability is a liability. AI without economic measurement is a budget line without accountability. We build the governance, observability, and economic measurement infrastructure that lets you manage AI as a controlled business capability rather than an experiment.
This includes the policies, controls, cost frameworks, and ROI measurement systems that satisfy legal, compliance, finance, and board-level requirements — including readiness assessment for Econa AI economic management.
Build your governance frameworkWhat's included
- AI governance framework: policies, controls, and escalation procedures
- Hallucination and output quality guardrails design and implementation
- AI observability stack: tracing, logging, evaluation, and alerting
- Cost attribution and cost-per-work-unit measurement
- ROI framework: productivity, cost reduction, and revenue impact measurement
- Bias, fairness, and explainability assessment for regulated use cases
- Audit trail and compliance documentation
- AI risk register and incident response playbooks
Who this is for
CAIOs and Chief Risk Officers building AI governance frameworks. CFOs and finance leaders who need economic accountability for AI spend. Legal and compliance teams responding to AI regulation.
Fractional CTO / CAIO / CDO
Senior AI leadership judgment is one of the scarcest resources in enterprise technology today. A fractional Pylon AI executive gives you that judgment immediately — at a cost structure that is rational before your AI program justifies a permanent hire.
We work as embedded executive partners: joining board calls, leading vendor negotiations, making architectural decisions, hiring recommendations, and representing AI strategy to investors and regulators. We are not advisors who show up once a month — we are operating partners.
Explore fractional leadershipWhat's included
- Fractional CTO: technology strategy, engineering leadership, and vendor decisions
- Fractional CAIO: AI program design, governance, and executive communication
- Fractional CDO: data strategy, data governance, and AI data readiness
- Board and investor presentations on AI strategy and progress
- Executive team AI education and capability building
- Vendor evaluation, negotiation, and contract review
- AI hiring plan and interview process design
- Transition to permanent hire: candidate profile, search support, and onboarding
Who this is for
CEOs and boards at companies where AI is strategic but a full-time executive hire is not yet warranted. Series B–D companies scaling AI before the function is mature. Enterprises bridging a leadership gap between departures and new hires.
AI Usage & Billing Economics
We have built B2B and B2B2C billing infrastructure for AI products from the ground up — token metering, tiered packaging, usage-based pricing, and financial reporting that maps AI consumption directly to revenue and cost. Most AI products bolt billing on as an afterthought; we design it as a first-class system.
Whether you are monetizing API access to external customers, allocating AI costs across internal business units, or building a developer platform with free tiers and upgrade flows — we have shipped the full stack and can do it again for you, faster.
Discuss AI billingWhat's included
- Token and API call metering with real-time attribution per customer, team, and feature
- Rate limiting, quota enforcement, and graceful degradation
- Flexible plan engine: flat, usage-based, pre-commit, bundled, and hybrid tiers
- Developer free tiers with upgrade flows and conversion tracking
- Discount, promo, and enterprise contract management
- Billing portal integration (Stripe, Zuora, Chargebee, custom)
- Per-customer cost reporting, chargeback, and showback dashboards
- AI cost forecasting and budget alerting
Who this is for
AI product companies monetizing API access to B2B or B2B2C customers. Enterprises allocating AI costs internally across business units. Platforms adding AI features with usage-based pricing. Anyone building an AI developer platform and needing metering from day one.
The right service for your stage, your team, and your objectives.
Tell us where you are and what you are trying to accomplish. We'll recommend the engagement that makes sense for your situation.