Revenue & Growth · Use Case Hub
Automate Sales Prospecting with AI
How to automate sales prospecting with AI across 42 industries. Workflow design, AI agents, governance, and the KPIs (qualified meetings, reply rate, pipeline created, and cost per opportunity) we report on weekly. Pick your industry below for a scoped engagement.
Early access: we work with a small first cohort. Engagements are scoped, priced, and shipped end-to-end by our team — not referred to third parties.
Primary outcome
build qualified pipeline without adding linear SDR headcount
What we ship
account research system, personalized outbound engine, scoring model, and meeting handoff workflow
KPIs we report on
qualified meetings, reply rate, pipeline created, and cost per opportunity
What "automating sales prospecting with AI" actually means
Automating sales prospecting with AI is not a single product you buy. It is a workflow you redesign around AI as the operating layer. The agent handles the high-volume, high-structure tasks. Humans handle edge cases, exceptions, and trust-sensitive decisions. The system is instrumented to measure qualified meetings, reply rate, pipeline created, and cost per opportunity and improve weekly.
What changes by industry is the systems the agent integrates with, the data it retrieves over, the controls it operates under, and the KPIs it has to defend. The architecture is similar; the integration and the controls are different.
The architecture we use for AI sales prospecting
- Frontier LLM — Claude, GPT-4-class, or Gemini. We benchmark candidates on a labelled test set during Discovery.
- Retrieval layer over your approved internal sources, with source citations on every output.
- Tool use for reads and writes against your operational stack (CRM, ERP, ticketing, data warehouse).
- Reviewer queue for low-confidence outputs. Confidence thresholds set per workflow.
- Evaluation harness — labelled test set, weekly accuracy reports, regression alerts.
- Versioned prompts and reviewer-action audit logs for traceability.
Pick your industry
42 industries. Each industry page is a scoped engagement with industry-specific systems, controls, and pricing.
Commerce
Energy
Financial Services
Automate Sales Prospecting in Banking
core banking · CRM · KYC platforms
Automate Sales Prospecting in Insurance
policy administration · claims platforms · broker portals
Automate Sales Prospecting in Wealth Management
portfolio management · CRM · financial planning tools
Automate Sales Prospecting in Payments
payment gateways · risk engines · merchant portals
Food and Agriculture
Food and Hospitality
Healthcare
Automate Sales Prospecting in Healthcare Providers
EHR · RCM · patient portals
Automate Sales Prospecting in Pharmaceuticals
CRM · medical information systems · safety databases
Automate Sales Prospecting in Medical Devices
QMS · CRM · field service platforms
Automate Sales Prospecting in Biotechnology
ELN · LIMS · clinical trial systems
Manufacturing and Industrial
Manufacturing and Mobility
Media
People Operations
Professional Services
Public and Knowledge Services
Public and Social Impact
Public Sector
Real Assets
Supply Chain
Technology
Technology and Communications
Travel and Hospitality
Frequently asked questions
How do you automate sales prospecting with AI?+
We map your existing sales prospecting workflow, identify high-volume and high-structure tasks, build an AI agent that handles those tasks, and route low-confidence cases to a human reviewer. The build connects to the systems your industry already runs on, runs against a labelled test set, and ships behind a reviewer queue before it sees production traffic. We measure qualified meetings, reply rate, pipeline created, and cost per opportunity from day one and improve weekly.
What is the best AI agent for sales prospecting?+
There is no single off-the-shelf "best" agent for sales prospecting — the right architecture depends on the systems and data of your industry. We typically combine a frontier LLM (Claude, GPT-4-class, or Gemini) with a retrieval layer over your approved sources, tool-use for your stack, and a reviewer queue. We benchmark candidates against a labelled test set during Discovery and pick the model with the best accuracy/cost ratio.
What does AI sales prospecting cost?+
Three phases, billed separately. Discovery sprint: $5k. Build engagement: $15k–$22k. Run retainer: $2k–$3k / mo. ~$25k–$45k typical year 1 (60% take the run option for ~6 months). Pricing varies slightly by industry — see the industry-specific pages below.
How long does it take to deploy AI sales prospecting?+
Thin-slice in production in ~6 weeks after Discovery, full Build phase over 6-8 weeks. By day 90, qualified meetings, reply rate, pipeline created, and cost per opportunity is instrumented and you have a baseline against which to expand to adjacent workflows.
Which industries do you build AI sales prospecting for?+
All 42 industries listed below. Each industry has its own scoped engagement page with industry-specific systems, controls, and KPIs. Common starting industries include Airlines, Airports, Hotels, Travel Agencies, Banking, and others.
What do we own, and what do you own?+
We own workflow design, prompts, retrieval architecture, evaluation harness, and weekly improvement. You own data access, policy, exception approval, and final commercial decisions. At the end of the engagement, every prompt, eval, and config is handed over — no lock-in.
Start the engagement
Book a discovery call for AI Sales Prospecting
Tell us about your workflow, the systems involved, and the KPI you want to move. We'll send a scoped statement of work within 5 business days.