Engineering Overview

Engineering got mass leverage from AI. Your revenue team is still at 1x.

A single engineer with Copilot and Claude ships what used to take a team of five. Companies have restructured entire engineering orgs around AI-assisted development.

Revenue Operations

Now look at your revenue team. Still pulling CSVs. Still deduplicating in spreadsheets. Still building one campaign at a time — three weeks from brief to launch. Still Googling prospects ten minutes before a call.

Engineering went from 1x to 10x. Revenue stayed at 1x — with better dashboards.

What You Have After 30 Days

Not a Configured Tool. A Running Revenue Operation.

• 10 agents in production: Outbound, meeting briefs, CRM hygiene, inbound enrichment, competitive intel, ad optimization.
• Every record unified: CRM, support, calls, enrichment, and web signals normalized into a single Revenue Ontology.
• 150+ providers active: Data Waterfall configured for your segments. 95%+ email, 80%+ phone. 30-40% lower enrichment cost.
• Ontology already learning: Signal classifications calibrated. Scoring tuned. Schema evolving from how your team actually works.

Your team's time shifts from execution to strategy. The leverage is the same. Revenue teams just haven't had their Copilot moment yet. This is it.

Weekly Breakdown

WEEK 1: Discovery & Configure

• Map CRM schema. Connect integrations. Configure Ontology, signal taxonomy, pipeline stages. Set up Data Waterfall.

WEEK 2: Build & Validate

• Build first 3-5 agent workflows. Run outputs in staging. Review with your teams. Tune from feedback.

WEEK 3: Production

• Agents go live. FDE team monitors quality and accuracy daily. Issues fixed same-day.

ONGOING: Compound

• Monthly reviews. New agents deployed. Scoring tunes from conversions. Ontology evolves from drift detection.

Security and Compliance

Your Security Team Can Read the Code

• Open-source auditability isn't a feature. It's the security model.
• SOC 2 Type II: Audited annually by an independent third party to ensure ongoing compliance and operational integrity.
• GDPR compliant: DPAs, right to deletion, data portability, lawful basis documentation.
• Single-tenant: Isolated compute, storage, indexes, vector stores. No cross-tenant join paths.
• Network isolation: AWS PrivateLink. Data-plane traffic never touches public internet.
• Permission-aware: Agents inherit invoking user's permission scope.
• Full versioning: Point-in-time queries, diff inspection, full rollback.

Your team's time shifts from execution to strategy. The leverage is the same. Revenue teams just haven't had their Copilot moment yet. This is it.

Pricing

Outcome-scoped.

• Not per-seat.
• Not metered.
• Not a surprise.

What’s Not Included

• Per-seat fees.
• Per-call metering.
• Overage charges.
• Separate bills for support or onboarding.
• A pricing page with three tiers.
• Re-pricing when needs change.

What's Included

• Forward-deployed engineering team.
• Single-tenant infrastructure.
• All agent workflows in your SOW.
• Data Waterfall within your volume tier.
• All integrations.
• Ongoing optimization & monthly reviews.

A typical deployment handles the execution workload of 6-8 roles — and covers the backlog those hires would never get to. The platform doesn't ramp for 2 months. It doesn't turn over after 18 months. And it gets measurably better every quarter.

They Deployed It. It Worked.

Forward-deployed Engineers

Permanent infrastructure for your account. Not a 90-day implementation. Every enterprise customer gets an FDE team. Not professional services with an end date. Not a CSM who checks in quarterly. Engineers who live in your stack and stay with your account.

They configure your Ontology, build agent workflows, handle complex integrations, and tune scoring models from outcome data. Accountable for your results — month 1, month 6, month 12.

The gap between "we bought the tool" and "the tool works for us" is where most enterprise software dies. FDEs close that gap. Permanently. Your deployment doesn't stall.

FAQ

• How do you ensure data accuracy across multiple enrichment sources?
• What happens if an agent makes a mistake?
• How customizable are the agents?
• Do we need internal engineering resources to use Lantern?
• How does Lantern handle data privacy and compliance across regions?
• Can Lantern scale with our growth?
• What makes Lantern different from traditional RevOps tools?

Get Your GTM Audit

Get your GTM audit — we’ll show you exactly which roles agents can fill and how much budget that frees up. 30 minutes.