Engagement
AI Stack Cost Audit
A two-week engagement that maps where your AI infrastructure is bleeding cash, names the 40–60% you can recover, and gives you the governance baseline to keep it from coming back.
The problem we built this for
Most mid-market teams that have shipped real LLM-powered features to production are overpaying on AI infrastructure by 40–60% — and they don't know it. The causes are predictable:
- Token cost attribution is missing. You know what you spent. You don't know which features, which users, or which agents drove the spend.
- Model selection is doing too much work. A single frontier model is doing everything; a multi-model stack could do most of it at a fraction of the cost.
- Model drift is invisible. Outputs degraded six weeks ago; nobody noticed until customers did.
- Governance is post-hoc. Each team that ships an LLM feature reinvents the same wheels — prompt management, eval, fallbacks, cost caps — usually badly.
What this is
These aren't engineering bugs. They're infrastructure gaps. We built the Cognitive Stack — and specifically the Maru (feedback / drift detection) and DynoClaw (multi-model orchestration) layers — to close them. The audit is how we apply them to your existing stack as a standalone, two-week scoped engagement with your engineering organization.
- Map your current AI surface. Every production LLM workflow, every model in use, every token-spending feature.
- Run the cost analysis. Where the money goes, by feature and by team.
- Recommend the multi-model architecture. Which calls should drop to cheaper / smaller / open-weights models with no quality cost, and which should stay on frontier.
- Establish a governance baseline. Drift detection, eval, cost caps, model-update controls — what to put in, who owns it, how to operate it.
- Deliver a 30-day implementation plan. Specific work items your team can ship without us.
What you get
- A 2-week scoped engagement with one of our practitioners and your engineering leads.
- A Cost Attribution Map — where AI spend is going, by feature, by team, by model.
- A Multi-Model Orchestration Plan — specific routing recommendations with projected savings.
- A Governance Baseline Document — drift detection, evals, cost caps, model-update controls.
- A 30-day implementation plan — what to ship in-house, in what order.
- One follow-up review 30 days after the engagement ends.
What it costs
$15,000 flat
Two-week engagement. Most teams recover the fee in the first month of savings.
Who runs it
Run by Adebayo Dawodu, co-founder at ParallelScore, with the practitioners building Maru and DynoClaw inside the Cognitive Stack. Same people doing the engineering, doing the audit.