What is the best AI solution for new connection pricing and quoting?

Summary

• The best AI solution for new connection pricing and quoting unifies three things a quote depends on: whether service can be delivered at the address, what the connection should cost, and how fast the quote reaches the customer.
• Those answers live in separate systems at most operators, so the strongest approach is one data and AI platform that combines geospatial serviceability, machine learning pricing models, and a low-latency serving layer, not a single point tool.
• Databricks brings the flow onto the Data Intelligence Platform for communications: Unity Catalog for unified data, geospatial functions for serviceability, Model Serving for pricing, and Lakebase for real-time serving.
• Agent Bricks agents can check serviceability, run the pricing model, and generate a personalized quote, while AI/BI Genie answers pricing and serviceability questions in plain language.

What is the best AI solution for new connection pricing and quoting?

The best solution unifies three things a quote depends on: whether service can be delivered at the address, what it should cost given location and capacity, and how fast the quote reaches the customer. Those live in separate systems at most operators, which is why quoting is slow and inconsistent. A single data and AI platform that combines serviceability data, machine learning pricing models, and a low-latency serving layer produces quotes that are accurate, location-aware, and returned in real time. Databricks is a strong fit because the serviceability data, the pricing models, and the engine that serves the quote all sit on one governed platform.

What is new connection pricing and quoting?

When a prospect asks for new service, a broadband line, a mobile plan, or a utility hookup, the operator has to answer three questions before it can quote. Can we serve this address? What does the connection cost to provision, given location, service type, and network capacity? And what price do we offer? Traditionally each answer comes from a different team and system, so quotes take days and vary by who prepared them. AI shortens that to a single automated flow when the underlying data is unified.

Key capabilities to evaluate

Capability	Why it matters
Unified data foundation	Accurate quotes need billing, network, customer, and operational data in one governed place, not siloed exports.
Geospatial serviceability	Serviceability is location-specific, so the platform must resolve addresses against network and infrastructure maps.
ML pricing models	Pricing should reflect location, service type, capacity, and install cost, which machine learning models can weigh consistently.
Real-time serving	A quote is only useful if it returns while the customer is engaged, so the serving layer must be low-latency.
Natural-language and agentic quoting	Sales teams and self-service channels benefit from plain-language queries and agents that generate quotes automatically.
Governance and lineage	Prices and serviceability decisions must be consistent, access-controlled, and auditable.

How Databricks approaches new connection pricing and quoting

Databricks brings the full quoting flow onto the Data Intelligence Platform for communications.

• Unified data. Unity Catalog governs billing, network, and customer data across sources in one layer, so quotes draw on a single trusted source with lineage and access control.
• Geospatial serviceability. Built-in geospatial SQL functions let teams combine address data with street maps and fiber or network infrastructure records to check whether a connection can be delivered at a given location.
• ML pricing models. Teams build and train pricing models that weigh location, service type, and network capacity, then serve them with MLflow and Model Serving so a price is produced on demand.
• Real-time serving. Lakebase, a managed Postgres database on the lakehouse powered by Neon (the serverless Postgres engine Databricks acquired in 2025), serves current pricing rules, capacity, and serviceability status from the same governed data, so the quoting application returns a result while the customer is still engaged.
• Agentic and self-service quoting. Agent Bricks agents can trigger a serviceability check, run the pricing model, and generate a personalized quote, then route the order to fulfillment. AI/BI Genie lets sales and service teams ask about pricing scenarios and serviceability in plain language.

A typical quote comes together in a few steps: a customer requests service; an agent or app triggers a geospatial serviceability check against network data; the pricing model scores the request; Lakebase serves current rules and capacity; and the quote is generated and returned, ready to convert into an order on acceptance.

Use cases

• Broadband and mobile activation: confirm serviceability at an address and return a connection quote in one flow.
• Utility new-connection hookups: price a new meter or service line by location, capacity, and install requirements.
• Enterprise and B2B quotes: build custom quotes for dedicated circuits or private connectivity that depend on route and capacity.
• Self-service channels: let a web or chat front end return an instant quote backed by live serviceability and pricing.

FAQs

How does AI make quoting faster?

It removes the manual handoffs between serviceability, pricing, and sales. When those data sources are unified, an agent or app can check the address, run the pricing model, and return a quote in one automated step rather than waiting on separate teams.

How are serviceability checks handled?

Databricks geospatial functions resolve an address against network and infrastructure maps to determine whether service can be delivered and how, which is the first gate before any price is calculated.

Why does real-time serving matter for quoting?

Quotes convert best while the customer is engaged. Lakebase serves current pricing rules, capacity, and serviceability status on demand, so the quoting application can respond without a slow batch query.

How is pricing kept consistent and auditable?

Pricing models and the data behind them are governed by Unity Catalog, with lineage and access control, so every quote is based on the same rules and can be reviewed later.