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AI That Pays for Itself, Not Just Demos Well

LLM integration, workflow automation, and machine learning pipelines wired into your real operations — scoped around measurable outcomes and delivered with the guardrails production AI demands.

48-hour proposal Fixed-price clarity 99.99% uptime SLA 24/7 support
0+AI systems in production
0+Hours automated per year for clients
0 daysMedian time to measurable ROI
0%Systems with human-in-the-loop guardrails
// What You Get

From AI Ambition to Working Automation

Most AI projects die between the demo and the deployment. Gleyon starts from the other end: we find the workflow burning the most hours, automate it with the right mix of LLMs, rules, and humans-in-the-loop, and measure the result in hours saved and tickets closed.

Everything ships with production guardrails — evaluation suites, cost monitoring, fallbacks, and audit logs — running on infrastructure we host and watch 24/7. One of our LLM-powered portals cut a client's support tickets by 52%.

  • LLM integration (Claude, GPT, open-source) grounded in your own data
  • Customer-facing AI assistants with human escalation that actually escalates
  • Workflow automation across CRMs, ERPs, email, and internal tools
  • RAG pipelines and semantic search over your documents and knowledge base
  • Evaluation suites, cost controls, and audit logs on every AI feature
  • Hosted and monitored on Gleyon infrastructure — no black boxes
AI and automation — neural network visualization
// Who This Is For

Built for Teams Where Hours, Not Hype, Are the Metric

Ops teams buried in manual work

Data entry, invoice processing, and copy-paste between systems eat your week — every step is automatable.

Support teams scaling triage

Ticket volume is growing faster than headcount — you need AI triage and drafting without losing answer quality.

Product teams adding AI features

Semantic search, chat, or summarization is on the roadmap — you need it shipped, evaluated, and cost-controlled.

Leadership chasing AI ROI

The board wants an AI story with numbers behind it — without hiring a research lab to get there.

// Capabilities

Production AI, End to End

Talk to an Architect
01

AI Assistants & Chatbots

Support and sales assistants grounded in your real knowledge base, with confidence thresholds and human handoff.

02

Workflow Automation

Document processing, data entry, approvals, and reporting automated across the tools you already run.

03

RAG & Semantic Search

Vector search and retrieval pipelines that make years of documents answerable in seconds.

04

ML Pipelines

Forecasting, classification, and recommendation models — trained, deployed, and retrained on schedule.

05

AI Guardrails & Evaluation

Eval suites, prompt regression testing, content filters, and cost ceilings before anything faces a customer.

06

LLM API Integration

Claude, GPT, and open-source models integrated with caching, fallbacks, and per-feature cost tracking.

// How We Work

A Process You Can Set Your Watch To

Four stages, weekly demos, zero surprises. You always know what shipped, what's next, and what it costs.

01

Identify

We map your workflows and rank automation candidates by hours burned, error cost, and feasibility — no science projects.

02

Pilot

A scoped pilot on real data in 2–4 weeks, with success metrics agreed before we start, not after.

03

Productionize

Guardrails, evaluations, monitoring, and fallbacks added; the pilot becomes a hardened service on Gleyon infrastructure.

04

Measure & Expand

Monthly reports in hours saved and error rates — and a ranked backlog of the next workflows worth automating.

// Engagement Models

Three Ways to Work With Us

AI Readiness Sprint

Find the Real Wins

Two weeks to map your workflows, rank AI use cases by ROI, and leave with a build-ready plan. Scoped proposal in 48 hours.

  • Use-case audit with every candidate ROI-ranked
  • Data readiness and integration assessment
  • Model/vendor-agnostic architecture recommendations
  • Cost and risk profile for each shortlisted use case
  • Build-ready roadmap you can execute with anyone
Start a Sprint
Automation Retainer

Keep It Compounding

Ongoing monitoring, iteration, and a ranked backlog of the next workflows worth automating — month after month.

  • Monitoring, drift alerts, and monthly eval reruns
  • Prompt and model upgrades as the landscape moves
  • Monthly reporting in hours saved and error rates
  • Ranked backlog of the next automation candidates
  • Cancel anytime with 30 days' notice
Start a Retainer
// Why Gleyon

The Difference, Line by Line

What mattersWith GleyonTypical vendor
Starting pointROI-ranked use cases from your workflowsDemo-driven hype and a slideware roadmap
AccuracyMeasured with evaluation suites on your data"Looks right" — vibes, not measurements
SafetyGuardrails and human-in-the-loop by designBolted on after the first incident
Lock-inModel-agnostic architecture — swap vendors freelyA single-vendor bet you can't unwind
DataYour data stays in your infrastructureShipped to who-knows-where for "training"
After launchMonitoring, drift detection, and retrainingAn abandoned pilot and an invoice
Gleyon automated our invoice processing pipeline and freed a five-person team from a week of manual entry every month. The system paid for itself inside a quarter — and the audit trail is better than the one we had with humans.
COO, logistics company — Invoice Processing Automation
// FAQ

AI & Automation Questions, Answered

Will AI answers be accurate enough for our customers?
That's why we ground every assistant in your verified content, set confidence thresholds, and route uncertain cases to humans. Before launch, each assistant passes an evaluation suite built from your real historical queries — and keeps passing it on every update.
Is our company data safe when used with LLMs?
Yes. We use API tiers with no-training guarantees, redact sensitive fields before calls, keep your knowledge base on Gleyon-hosted infrastructure, and log every interaction for audit. Where requirements demand it, we deploy open-source models fully within your perimeter.
How do we know the automation is actually saving money?
Every engagement starts with a baseline — hours spent, tickets handled, error rates — and reports against it monthly. If a pilot doesn't beat its agreed success metric, we tell you to stop, not to expand.
Do we need our own data science team to use AI?
No. Gleyon brings the AI engineering, evaluation, and MLOps expertise; your team brings domain knowledge. We build systems your existing engineers can operate, document every component, and train your staff during handover — a research lab is not a prerequisite for ROI.
Which AI models and vendors do you work with?
Claude, GPT, and leading open-source models — chosen per use case on accuracy, cost, and data requirements, not loyalty. Every architecture we ship is model-agnostic, so when a better or cheaper model appears, swapping it in is a configuration change, not a rebuild.
Free 30-Minute Architecture Review

Automate the Work That Shouldn't Need Humans.

Tell us where the hours go. We'll identify your highest-return automation, scope a pilot, and put a fixed price on it within 48 hours.