Your reps never learn to prompt.
The AI comes to them.
Faster's agents work your book on their own and come to a person only when a decision needs one. Underneath, a multi-model harness gives each step of a task the right model — and keeps your data inside your own instance.
ScrollReactive AI waits for a prompt.
Faster works, then asks.
Agents work through the whole book. When something needs a human — a judgment call, a customer relationship, a decision with money on it — the agent stops, explains the situation, lays out the options, and waits. Your rep answers in one tap and moves on.
When a judgment call comes up — a credit, a backorder, a new buyer — it lands here with two options.
- 02Quote #10492 sent with contract pricing✓
- 04Stark ATS sheet reconciled — 212 SKUs current✓
- 0738 stale accounts got a tailored note✓
- 06Redwood PO entered from a PDF — 14 lines✓
You ask, it answers, then nothing happens until you ask again. The work moves at the speed of your prompts.
The agent works on its own, comes to you only when it needs a decision, and keeps going the moment you answer.
Spin up an agent for the tedious stuff.
On a schedule, or the moment something changes.
Every agent runs inside Faster's secure enclave, on your data, in your environment. Any rep can create one for the boring, repetitive work: pick a trigger, describe the job in plain English, done. No ticket, no engineer.
Pick a trigger, describe the job in plain English, done. It runs in your secure enclave from then on. No ticket, no engineer.
On a schedule
Send the open-order report to your top 20 accounts
Pull yesterday's orders into the CRM, flag anything odd
Reconcile commissions against paid invoices
On an event
Nudge every account that reorders this SKU
Re-quote open quotes, flag any margin under 18%
Tell every buyer who was waiting on it
Enter the order, draft the confirmation
Why not just give everyone ChatGPT?
One model reading everything is slow, expensive and exposed.
Faster breaks each task into steps, gives each step the model that's best at it, and shows each model only the slice it needs.
- Step 01Transcribe last callGemini 3.8 Flash · Google
- Step 02Extract order historyQwen3.7 Plus · Open-weight
- Step 03Check price book & ATSGPT-5.6 Sol · OpenAI
- Step 04Reason: what to pitchClaude Opus 5 · Anthropic
- Step 05Draft the follow-upClaude Sonnet 5 · Anthropic
- Step 06Classify open claimsGemma 4 31B · Google · open
Prep a rep for a customer visit — six steps. Everything in one frontier model's context on every step, against the harness sending each step only what it needs, to the model suited to it.
Each dot is one model doing the whole task. Up is better work; right is more expensive. The harness lands top-left: $0.28 for a 22× saving at the highest score.
List prices per million tokens, September 2026. Single-model points are the whole task sent to that model with everything in context. Your mix of tasks lands at a different ratio; the shape of the chart doesn't change.
Open-weight models on an isolated instance we run for you do the bulk of the work. Nothing leaves.
One minimized, encrypted slice — no names, prices or IDs it doesn't need.
Each provider sees one step. The answer is reassembled back inside the enclave.