About
An AI infrastructure advisory and implementation practice.
Hardpoint designs, implements and operationalizes enterprise AI systems across knowledge, workload routing, infrastructure and production operations — using telecom and contact-center operating discipline as prior art.
Where the discipline comes from
Familiar operating questions, different technology.
Hardpoint is built on more than fifteen years of experience in carrier-grade voice, contact center, enterprise implementation, and Professional Services environments. Enterprise AI is not telecom, but it repeats many of the same operating questions: classification, routing, policy, capacity, failover, observability, accounting, and ownership. Telecom provides useful prior art for designing systems that must survive production.
Different technologies. Familiar operating problems. That framing is a discipline, not a claim of protocol equivalence — tokens are not packets and providers are not carriers.
Prior enterprise experience across
Retail and franchise operations, financial services, higher education, and multi-site enterprise environments — sectors served during prior professional employment, where standardization, auditability and operability mattered more than being first to a new technology. These are anonymized prior engagements from that career, not Hardpoint clients.
Operating model
Principal-led engagements, explicit scope, written artifacts, and direct technical ownership. Specialist collaborators may be introduced only when the engagement requires them and their responsibilities are disclosed.
Data control
Hardpoint designs for customer-controlled knowledge, credentials, repositories, and runtime state. Persistent custody of production data is avoided unless an engagement explicitly requires and defines it.
Accountability
Tom Melchionna — Principal Consultant
Tom’s career progressed from Tier 2 VoIP and NOC operations through voice and data engineering, complex multi-site deployments, carrier coordination, and global Professional Services delivery. Hardpoint applies that operating experience to enterprise AI knowledge systems, workload routing, production infrastructure, and operational assurance.
Engagements are led directly by the principal. The person who scopes the work is the person who does it and signs the artifacts.
Prior professional experience—not Hardpoint client outcomes.
Principles
Six rules we do not trade away.
Measure before you architect
No routing, no consolidation and no migration recommendation until the traffic and the knowledge have been counted. Opinions are cheaper than telemetry and worth less.
Write it down
Every decision becomes a record: what was chosen, what was rejected, and what would change the answer. Undocumented architecture is a staffing risk.
Design for the operator
The person on call at 2am is the real user of an architecture. If it cannot be explained in a runbook, it is not finished.
Boring where it counts
Novel components get budgeted deliberately. Everywhere else we choose the option with the longest track record and the clearest failure behaviour.
Independence is structural
No AI-seat resale and no model-provider commissions. Recommendations are not shaped by reseller margin. Hardpoint sells engineering judgment, implementation, written artifacts, validation, and a clean operational handoff.
Exit as the goal
Engagements end with your team owning the system. A practice that needs to stay forever was never advisory.
The stack
Everything maps to four layers.
- 01Knowledge
How enterprise knowledge is represented, governed, versioned and made portable.
Open Knowledge Format implementation
- 02Traffic
How AI workloads are classified, routed, constrained by policy and measured.
AI traffic engineering
- 03Infrastructure
Where workloads execute: provider APIs, private infrastructure, local and cloud compute.
Architecture & productionization
- 04Operations
How the environment is observed, supported, recovered, documented and handed off.
Production operations & assurance
Bring us the messy architecture.
Hardpoint works with organizations that need to turn fragmented AI systems, knowledge, providers, infrastructure and operational requirements into something that can actually run in production.