1A🛡
How do I control what data AI can see?
Define exactly what data and systems AI and agents can access—and what stays private.
Data access control
Public web
Internal docs
Customer data
Finance systems
U · UNIFIED — MULTIPLAYER AI
One shared URL. Everyone sees the same task, progress, decisions, and context. Teammates can correct the agent, approve a step, or hand the Session to someone else while the work is still running.
Five enterprise AI challenges. One platform.
Learn more →
2Q$
How do I stop overpaying on AI?
Qua’s Answer Waterfall finds the lowest-cost path for every question.
Average savings
≈80%
lower total cost vs. uncontrolled AI spend
3Q📖
How do I reuse what my company already knows?
Qua searches your private knowledge first. Most answers come from what you already own.
Private knowledge
Answer found in 2 sources
Est. cost$0.0021
4U👥
How do I let teams work together with AI?
Agent Sessions turn AI into a shared workspace—see it, edit it, approve it, together.
KKarthik edited2m ago
NNatasha added input3m ago
AAlex approved5m ago
5A⚙
How do I keep control no matter which models I use?
Use any model, in any infrastructure. One policy layer stays in control.
OpenAI
Anthropic
Meta Llama
Mistral
Your model
Q · QUANTIFIED — HOW QUA DECIDES
How Qua works: The Answer Waterfall
The most expensive tier sits at the top. Qua resolves every question as far down the waterfall as it can — 84% never reach the most expensive tier, and Open Models are only ever used when you ask for them.
01
Only if it is worth it.
The highest-capability models, for complex reasoning and premium tasks — reached only when the expected-value gate clears.
Pro Models · premium02
Can a cheaper model do it?
Optimized for speed and cost — the cheapest model with a proven acceptance rate for the task class.
Fast Models · metered03
Do you want to run it on an open model?
Open-weight and open-source models, available whenever you ask for one. Qua never routes here on its own.
By request only · never auto-selected04
Does your company already know?
Searches connected company data and approved workplace sources — including org-verified answers — then a cited answer.
≈95%+ cheaper than external models*
In-perimeter · ~$005
Do you already know this?
Retrieves what you saved or connected — your verified answers, scoped knowledge, and instant deterministic resolution.
$0 · instantResolve low. Stay private. Climb only when it pays.
Q · QUANTIFIED — SEE IT RUN
Watch a question find its price.
Every session, priced honestly
Below is a fully modeled 30-day scenario for a 380-seat enterprise — our Meridian Group demo dataset. The same 286,400 queries are priced three ways: what they cost through Qua's Answer Waterfall, what an all-open-source setup would cost, and what routing everything to a premium model would cost. Every figure on this page reconciles — totals, tiers, and daily series all add up.
89.4%
below all-premium routing
84%
of queries resolved without reaching Pro Models
$1.65M
modeled 30-day savings vs. frontier
$0.96
blended cost per 1M tokens
The daily cost gap
Qua actual — $194.8K
All open-source — $561.8K
All frontier — $1.84M
Where queries actually resolve
Pro Models · 16% · 45,824 queries
Fast Models · 22% · 63,008 queries
Open Models · by request — 0 in this scenario
Enterprise Search · 30% · 85,920 queries
Personal Knowledge · 32% · 91,648 queries
84% of work never reaches the most expensive tier.
Why routing matters
Frontier models process 6% of the tokens but half the bill — routing is the entire game.
Illustrative modeled scenario (Meridian Group demo dataset), not customer results. The open-source and all-premium figures are modeled counterfactuals at list rates; only the Qua figure represents an actual invoice in the scenario. Labor-value figures, where shown, use a blended $85/hr rate.
One measured number.
5×lower AI spend vs. uncontrolled use*
*Measured routing outcome, varies by workload. Independent research on model routing reports 40–98% cost reduction: RouteLLM (UC Berkeley, ICLR 2025) measured 85% at 95% of GPT-4 quality; FrugalGPT (Stanford) up to 98%.
Three guarantees.
Lower cost with every reuseVerified knowledge is retrieved and reused, reducing repeated model work and lowering the cost of future answers.
100% cost and source visibilityEvery answer shows what it cost, which sources were used, and how the response was generated.
Your enterprise data stays yoursYour data is never used to train shared models.
Clear claims, not magic numbers
Show the buyer exactly where the savings come from.
Across the full route
≈80%Qua's documented 5× total-spend reduction is equivalent to roughly 80% lower spend versus an ungoverned baseline. It should be presented as a measured routing outcome, not as a universal RAG claim.
On repeated input context
Up to 90%Major model platforms document input-token savings of up to 90% when repeated context qualifies for prompt caching. Retrieval and output generation still carry cost.
A · AUTONOMY — YOUR MODELS, YOUR WALLS
Cloud, your VPC, or air-gapped — only the trust boundary changes.
Qua is model-agnostic and hosting-agnostic: closed commercial models, open-weight models, or your own fine-tunes — running in Qua cloud, inside your VPC, or fully on-prem.
Zero-cost-tier answers — Personal Knowledge and Enterprise Search — are composed from your own knowledge and never leave your perimeter.
Neutrality is the business model: Qua never trains shared models on your knowledge, and admins see outcomes — never prompt content.
Qua cloud
Your VPC
On-prem / air-gapped
Identical behavior. Only the trust boundary changes.
Don’t rent your answers. Own them.
QUA — Quantified · Unified · Autonomy
Every query priced. Every team in one workspace. Every model yours.
QUA is QA with U in the middle — quality assurance for AI work, with your people in the loop.