Frontier-grade AI work, at a third of the token cost.
The same engine behind huSpace’s memory gives companies verifiable, cost-controlled intelligence — many models cross-check every output, and the memory graph feeds in only what matters.
The cost of getting it wrong
Run a serious AI workload today and three problems compound: spend, trust, and memory.
Runaway token spend
Stuffing context windows and re-running long tasks turns a pilot into a five-figure monthly bill — fast.
Unverifiable output
One model grading its own work can’t catch its own blind spots. At scale, silent errors are expensive.
No shared memory
Every session starts cold. Knowledge doesn’t accumulate across people, tools, or time.
Hand it the work no single chat could finish.
Give huSpace a sprawling objective and it decomposes the work across coordinating instances — they message each other, divide the load, check each other’s output, and deliver the whole package: deep reports, board-ready decks, quantitative models, and design & engineering drafts — while optimizing the processes around them.
Reports & analysis
Long-form research and decision memos, sourced and cross-checked before they land.
Decks & presentations
Board-ready slides assembled from the work — narrative, charts and notes included.
Quantitative modeling
Financial and operational models built, stress-tested by a second instance, and explained.
Design & engineering
Architectures, specs and design drafts — produced, reviewed and iterated as a team.
Instances that coordinate
Real communication between huSpace instances across the business — handoffs, not silos.
Process optimization
It maps how work actually flows and collapses the busywork — fewer steps, one owner.
How we cut cost without cutting quality
Orchestration + a memory graph: several models cross-check each other while the graph keeps the context lean. Same quality bar — a fraction of the spend.
See the engine, not just the claims
The same memory graph and multi-model verification your team would run — live, and inspectable.
huSpace vs the usual options
| huSpace | Raw frontier API | Single-vendor suite | |
|---|---|---|---|
| Token spend at scale | Low — graph feeds only what matters | Very high | High |
| Output verification | ✓ multi-model cross-check | None | Single model |
| Long-term memory | Org-wide knowledge graph | None | Limited |
| Vendor lock-in | None — model-agnostic | High | High |
| Audit log of every action | ✓ | ✗ | Partial |
| Permission tiers (in code) | ✓ | ✗ | ✗ |
| Data residency & no-train | ✓ | Varies | Varies |
Built for trust at scale
Control and accountability are in the architecture, not in a policy doc.
Permission tiers
Read is silent; send asks first; pay or act externally needs an explicit yes — enforced in code, never by a prompt.
Full audit log
Every action and every model call is logged and attributable — what happened, why, and which model.
Your data stays yours
GDPR-aligned, EU-based, no training on your data, memory you can inspect and delete on request.
Where teams put it to work
Support automation
Resolve and draft with full account memory — verified before it reaches the customer.
Knowledge operations
One durable, permissioned memory across docs, tools and people — no more cold starts.
Executive intelligence
Briefings and analysis cross-checked by several models, at a fraction of the token cost.
Run a pilot with huSpace.
Tell us your workload — we’ll show you the cost and quality on your own data.