01
AI Cost & Quality Diagnostic
A bounded five-day evidence package for one working AI workflow.
Company
Practical AI engineering, without provider loyalty.
Residual Forge helps small software teams and AI-heavy operators make measured decisions about cost, quality, routing, privacy, and implementation.
Company thesis
Measure first. Implement what survives.
AI stacks accumulate hidden cost through oversized models, repeated context, uncontrolled output, retries, weak routing, and poor observability. Residual Forge turns that ambiguity into comparable evidence—and keeps quality as the non-negotiable gate.
Offer ladder
02
Optimization implementation
Separately scoped routing, caching, output-control, retry, batching, and observability changes.
03
Private local AI deployment
Controlled inference, access control, private retrieval, citations, evaluation, backup, and runbooks.
04
Forge Meter
A future provider-neutral product shaped by patterns found in paid diagnostics.
Operating principles
Provider-neutral
OpenAI, Anthropic, Google, local models, or a mix—the workload decides.
No invented proof
Synthetic results are labeled. Customer claims require customer evidence and permission.
Private by design
Use sanitized inputs and approved transfer paths; expose no more data than the work requires.
Human control
Consequential production changes and external actions require explicit approval.
Residual Forge / founding cohort
Know which AI costs are buying quality—and which are just waste.
Start a fit conversation →
Tell us which AI workflow you run repeatedly and where cost, latency, or quality is creating friction.