Graduate your AI prototype into a production-grade system.
Glasspoint helps funded teams harden fragile AI prototypes and AI workflows into secure, scalable, observable systems ready for real users.
Best fit
Qualified teams with something real to harden.
The strongest fits already have a prototype, workflow, customer pressure, investor pressure, or internal operating pain that makes production readiness urgent.
prototype-to-production review
runtime risk assessment
security and cost posture
managed ops path after launch
Buyer paths
Two ways teams come to Glasspoint
Common prototype sources include Lovable, Base44, Bolt, Cursor, freelancers, internal hackathons, rushed MVPs, and early agency builds. The source matters less than whether the system can now survive production conditions.
Why prototypes break
The demo proves the idea. Production exposes the system.
Most failures are operating failures: the architecture has no owner, the model path has no guardrails, and the workflow ends before the business process does.
The runtime is invisible
There is no clear view into errors, latency, model behavior, cost spikes, or where the system is starting to drift.
Security arrives late
Access control, audit trails, sensitive data handling, and compliance questions get bolted on after the product already has users.
The economics collapse
Every request uses the same expensive path. No routing, no caching, no token budget, and no owner for the bill.
The workflow is unfinished
The AI produces an answer, but it does not route work, trigger approvals, update systems, or give operators a reliable next step.
What Glasspoint builds
Production AI systems with an operating model attached.
Glasspoint is not a general dev shop. We build the system, the controls, and the operating layer that lets cautious buyers trust AI in real workflows.
Production Graduation process
A sprint for the critical layer between prototype and product.
We keep what works, rebuild what breaks under pressure, and leave you with a system that has a runtime owner.
Book a Prototype-to-Production ReviewReview the prototype and operating risk
We inspect architecture, data flows, security posture, model behavior, cost structure, and where the current system breaks under real usage.
Rebuild the production layer
We harden the system around monitoring, fallback logic, access control, deployment discipline, cost controls, and human approval where needed.
Launch with a runtime owner
You leave with a production-grade system and a clear operating model: clean handoff, managed AI ops, or a phased expansion plan.
Methodology
Our work is guided by TRACE, our production-readiness lens for AI systems.
It stays in the background for now: a practical internal lens for reliability, cost, actionability, explainability, and observable system behavior.
Managed AI Ops
A managed runtime for teams that cannot babysit AI.
After graduation, Glasspoint can own the managed AI runtime for defined workflows: model routing, monitoring, cost control, incident handling, prompt governance, and approved enhancements.
Launchpad
For the first production workflow
- Monthly production health review
- Cost and reliability review
- Runtime monitoring
- Email support and incident triage
Growth
For teams scaling across operations
- Weekly production monitoring
- Model routing optimization
- Prompt and version governance
- Slack support and quarterly review
Scale
For mission-critical AI workflows
- Continuous runtime monitoring
- Named engineer
- SLA-backed incident response
- Architecture evolution planning
Supporting specialist pillars
Specialist support when the production path needs it.
Proof assets
Case studies coming after first approved delivery notes.
We will publish delivery notes and anonymized case studies only when clients approve them. No invented logos, no fabricated metrics.
Ready for real users
Find out what it takes to move from prototype to production.
Bring the prototype, the workflow, or the runtime concern. We'll tell you what needs to change, what can stay, and what the production path should look like.