Multi-Agent Moderation
50,000+ listings daily by role-specific agents — compliance, image quality, pricing. 70% fewer tokens than a monolithic approach.
Solutions
Thin, specialised models that match frontier quality at a fraction of the cost — from audit through to mass orchestration.
128–462×
Cheaper per conversation than frontier LLMs
Linear
Cost scaling — adding agents never compounds
40–60%
Cost reduction from every efficiency audit
∞
Thousands of concurrent agents on one grid
Our Process
Four phases from audit to orchestration.
01
Audit
Analyse AI pipeline — model choices, spend, latency, accuracy — plus operational workflows across HR, Admin, Development, and Design. Output: a prioritised roadmap targeting 40–60% cost reduction by replacing general-purpose LLMs with purpose-built thin models.
02
Decompose
Break workflows into distinct agent roles, each with a narrow scope and a purpose-built model. Every agent is independently deployable and improvable. Domain-specific data trains each model from day one — catalogues, queries, compliance rules, or clinical codes.
03
Deploy & Learn
Agents go live in your existing infrastructure with zero data leakage. All models run on your hardware — on-premise, VPC, or air-gapped. No third-party APIs, no training on your data. Each agent learns from every task, compounding accuracy without manual retraining. As demand grows, agents simply multiply — costs scale linearly, not exponentially.
04
Orchestrate
The orchestration layer — Kubernetes for thin AI agents — handles creation, deployment, steering, monitoring, and retirement of thousands of agents across a single grid. One control plane to route tasks, track performance, and optimise every agent in real time.
What We Audit
Five operational layers, from AI infrastructure to human workflows.
AI Pipeline
Model architecture, token spend, latency, accuracy benchmarks, hosting. Identifying where thin models replace frontier LLMs.
128–462× cost reductionHuman Resources
Recruitment screening, onboarding, employee queries, performance reviews. Automating repetitive HR workflows with specialised agents.
Hours saved per week per teamAdministration
Invoicing, reporting, compliance docs, scheduling, data entry. Agents handle structured admin tasks end-to-end.
40–60% cost reductionDevelopment
Code review triage, ticket classification, doc generation, test creation, bug analysis. Reducing developer overhead with lightweight agents.
Faster release cyclesSecurity & Compliance
Audit of data flows, vendor risk, model supply chain, and regulatory alignment. Identifying where frontier LLM usage exposes sensitive data and recommending on-premise alternatives.
100% data sovereigntyIndustry Applications
Real applications of purpose-driven agent systems in production.
Narrow-Agent Underwriting
Identity verification, risk scoring, policy matching as independent agents. Dramatically reduced error rates. Retains institutional knowledge beyond staff tenure.
Claims Adjudication
55 decision nodes, 6 hubs compiled into one 8B model. Failure rate 17% → 9%. Cost: <$0.001/claim. Runs on local hardware — no PHI leaves site.
Thousands of Concurrent Agents
Per-seller pricing, per-category inventory, moderation pool — each agent <$0.001/decision. Linear cost scaling at any volume.
Knowledge-Retaining Research
Collaborative agents screen filings, evaluate financial health, track sentiment — encoding proprietary criteria. System retains knowledge across personnel changes.
Ready to get started?
Tell us about your current AI stack and we will recommend the right approach — whether a full audit, a targeted deployment, or a proof of concept.
Get in touch →