AI SDR & lead qualification agent that grows pipeline without more headcount.
Research and qualify inbound or named leads, draft evidence-based outreach, and let sellers approve messages and account strategy.
Nothing is finalized until a human approves it.
Where the time and money actually go.
SDRs spend time gathering basic context and writing generic outreach; enrichment errors and high-volume automation damage brand and deliverability.
Prospects, SDRs, account executives and marketing operations
Trigger to act: Pipeline coverage is below target, inbound response is slow, SDR capacity is constrained, or a vertical campaign needs higher-quality personalization.
The result you can model before you sign.
Illustrative only: 5,000 leads/month × 2 percentage-point incremental qualified-meeting conversion × $180 expected contribution per qualified meeting = $18,000 monthly pipeline value before downstream win probability.
The outcome, plainly: Research and qualify inbound or named leads, draft evidence-based outreach, and let sellers approve messages and account strategy.
Inputs in. A cited, review-ready result out. Your expert decides.
A Tool-using workflow agent. Every material fact is grounded in an allowed source and returned with its identifier, with no invented data.
Claude Sonnet 4.6 or GPT-5.6 Terra for complex grounded work, selected by task-level evaluation; Gemini 2.5 Flash, GPT-5.4 mini or Claude Haiku 4.5 for high-volume routing and drafting. PostgreSQL + pgvector or a managed vector store; policy rules and evaluator service.
- Lead/account
- ICP criteria
- Source and consent
- Firmographic/product signals
- Prior engagement
- Approved proof points
AI-Native, not autonomous. Judgment stays with your people.
The machine does the work; the human’s role narrows to the one thing that matters, judgment. That constraint is what makes it safe to deploy.
Sellers approve outbound messages, qualification, pricing, claims, and account strategy; compliance controls govern consent, suppression, and jurisdictions.
A scorecard, not a demo. We baseline what breaks in production.
Every deployment ships with an evaluation suite. These are the numbers we baseline before launch and monitor after.
The category is crowded. Most of it isn’t built for your workflow.
Fixed-scope, tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in from day one. A price you own, not a subscription you rent.
It plugs into the stack you already run.
No rip-and-replace. Access is scoped to the minimum data necessary, isolated per tenant, and fully logged.
Transparent by design. The build price buys the workflow and the proof.
A fixed implementation fee plus a monthly bill that scales with volume and governance. No hidden seats.
- ✓ One process / scope
- ✓ Live workflow on your data
- ✓ Baseline evaluation suite
- ✓ Measured vs. current process
- ✓ Full scope & integration
- ✓ Human-review UI & audit trail
- ✓ Write-back to your systems
- ✓ Production evals & monitoring
- ✓ Multi-facility rollout
- ✓ Advanced security & compliance
- ✓ Custom control & escalation
- ✓ Dedicated evaluation program
10,000–35,000 workflow runs per month with modest document and model usage.
Planning assumptions, not vendor quotations. Your CRM and other platform licenses are separate and owned by you. Figures confirmed during scoping.
“His vast knowledge of technologies and a natural problem-solving mindset consistently lead us through complex challenges with clarity and confidence.”
Questions serious buyers ask.
Does the AI act on its own?
No. Sellers approve outbound messages, qualification, pricing, claims, and account strategy; compliance controls govern consent, suppression, and jurisdictions. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: no deceptive impersonation, fabricated research, spam, or messaging without lawful basis; no autonomous pricing/contract promise; no guarantee of pipeline.
How do you stop it inventing facts?
Every material claim is grounded in an allowed source record and returned with its source identifier. The system separates observed facts, model inference, and missing information, and routes to a human whenever confidence is low, evidence conflicts, or an adverse outcome is possible.
What does it cost to run each month?
A usage bill of roughly $400 to $3,100 per month (10,000 to 35,000 workflow runs per month with modest document and model usage), plus a $2,000 per month managed retainer for evaluation, monitoring and maintenance. Your existing platform licenses are separate and already yours. Exact figures are confirmed during scoping.
Do we need a ChatGPT or Claude subscription?
No consumer ChatGPT or Claude subscription is required for the production workflow. You need an approved API/cloud billing account. Workspace seats are optional for internal prototyping and administrator access.
How is this different from Salesforce Agentforce?
Tools like Salesforce Agentforce, HubSpot AI, and Outreach are broad platforms you adapt to. This is a fixed-scope implementation tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in, and a transparent price instead of a seat subscription.
How long until it is live, and how do we prove it works?
We baseline “Speed to lead” first, then measure against that baseline. You see the scorecard before expanding scope: the evaluation suite ships with the system, not as an afterthought.
Bring your real numbers. Leave with a fixed-scope plan.
A 30-minute engineering-led working session, no slideware. You leave with a sized opportunity estimate, a fixed-scope pilot plan, and the integration & human-review path mapped.
VP of Growth at ViitorCloud · senior delivery owner confirmed before paid work