We build systems that cut your AI costs >70%.
We audit your AI infrastructure, find where you're burning money on models that don't need to be frontier, and rebuild those workflows to run locally — on your network, under your control — for a fraction of the cost.
Most teams are stuck at the expensive end.
The gap between frontier AI inference and local deterministic code isn't incremental — it's orders of magnitude. We move your workflows toward the right.
Audit. Propose. Build.
Three phases, no wasted motion. We don't guess — we measure first, then fix exactly what's costing you.
The numbers aren't incremental.
These aren't optimizations at the margins. When 85% of your AI spend is on work that doesn't need a model, the savings look like this.
Engineers, analysts, and practitioners are all arriving at the same conclusion.
Independently, across industries, without coordination — people who build AI systems for a living keep finding the same thing: most of the spend is on work that doesn't need a model. The fix is architectural.
Most people building AI agents obsess over which model to use. The model is the easy part. Routing is what actually kills you in production.
There are 3 routing strategies worth knowing:
zero cost, zero latency, deterministic
handles ~40–50% of traffic
semantic → embedding similarity
catches the messy middle ground (~35–40%)
llm-as-router → genuine ambiguity only
should fire 10–20% max
The worst agentic systems I've seen have one thing in common: they use an LLM for everything. Date parsing. Math. Format validation. Lookups. Transformations.
→ SQL: fetches the data (not the model)
→ Regex: validates the format (not the model)
→ API call: books the meeting (not the model)
→ Rule engine: enforces business logic (not the model)
Senior Rule: if the answer is deterministic, don't ask a probabilistic system. You'll pay 10× the cost for 80% of the reliability.
Google's AI team just published how they achieved 6× faster ML migration. Their architecture:
Not AI judgment. Code.
Playbooks → version-controllable policy
Not prompts. Code.
Validation → algorithmic gradient ascent
Not model self-assessment. Code.
Let the LLM do what it's good at — reasoning, pattern recognition, extraction. But when it comes to the governance decision — that decision gets made by explicit, version-controlled, inspectable code.
Unpopular opinion: The best AI implementation I've seen this year wasn't GPT or Claude.
It was a rules-based classifier routing support tickets at a 50-person company.
Total API cost: $0/month.
Not everything needs an LLM. Sometimes the boring solution is the profitable one.
We run 12 AI agents in production with zero employees. The harness is 90% of the work. Constraint files that define scope, guardrails, and tool access per agent before the first token generates.
Prompts get you the demo. The harness gets you through month two.
AIs aren't good rule followers. The older the rule in the context window, the less priority it gets. The best way to enforce rules is with external tools that communicate failure to the AI. Acceptance testers. Linters. Dependency checkers.
Productivity gains come from disengagement from the code. Let the AI worry about the code. You worry about the quality metrics.
The focus on Gen AI is focused entirely on the wrong place. It's transformational for back office, for rote tasks, for boring, for B2B — data cleansing, swivel chair processes. But it's continually pitched as a consumer solution. It's all backwards. People don't want a 3D avatar. They want supplier forms automated.
Public posts reproduced with attribution. Links to originals on each post.
Spending $10,000+/month on AI? We should talk.
If your AI bill is at that level, there's almost certainly significant waste we can find. The audit is free. The savings are real.