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AurumGene

TECHNOLOGY & SAAS

Technology leaders are asked to grow revenue while holding costs flat. We help you decide where Agentforce belongs across marketing, sales, support and customer success, prove the case and govern the programme.

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The pressure

Growth targets remain. The budget to hire towards them does not.

  • Pipeline quality over quantity

    Sales teams chase leads that will never close while good ones wait. Qualification varies by person and by day.

  • Support that scales with headcount

    Every new customer adds tickets. Senior engineers answer questions the documentation already covers.

  • Renewal risk seen too late

    Usage, support and commercial signals sit in different tools. Customer success learns of trouble at the renewal conversation.

  • A crowded, overlapping tool estate

    Each team has bought its own AI features. Nobody owns the whole picture, the cost or the risk.

Our advisory approach

Four stages, each tied to growth efficiency.

  1. 01

    Discover

    We review the customer lifecycle from first touch to renewal, the data behind it and the AI already in use.

    Outputs

    • AI and agent readiness assessment
    • Revenue data map
    • Opportunity longlist

  2. 02

    Define

    We prioritise use cases by their effect on growth efficiency, and retire pilots that will not scale.

    Outputs

    • Prioritised use case portfolio
    • Business case
    • Success measures

  3. 03

    Design

    We shape the revenue operating model, guardrails and roadmap for Agentforce across marketing, sales, support and success.

    Outputs

    • Target operating model
    • Agent governance framework
    • Phased roadmap

  4. 04

    Direct

    We guide, govern and assure the work your teams and chosen partners carry out, at the pace a technology business expects.

    Outputs

    • Independent programme assurance
    • Value realisation tracking
    • Executive reporting

Where agents fit

Each one pairs an agent with a clear point of human control.

  • Inbound lead qualification

    An agent engages inbound enquiries, asks qualifying questions and books meetings. Sales sets the criteria and takes over every qualified conversation.

  • Technical support resolution

    An agent resolves common product questions from documentation and past cases. Complex or high-severity issues go straight to engineers.

  • Renewal risk signals

    An agent combines usage, support and commercial data in Data 360 to flag accounts at risk. The customer success manager decides the response.

  • Expansion opportunity spotting

    An agent identifies accounts whose usage suggests a need for more. Account managers choose which to pursue and how.

  • Customer onboarding guide

    An agent walks new customers through set-up steps and answers early questions. Onboarding specialists step in when progress stalls.

  • Deal desk assistant

    An agent answers sales questions on pricing and terms in Slack, from approved policy. Non-standard terms still need deal desk approval.

Outcomes

Agreed with revenue and finance leaders before work starts.

Targets are set with each client against their own baseline.

  • Qualified pipeline

    The volume and quality of opportunities passed to sales.

  • Speed to lead

    How quickly an inbound enquiry receives a meaningful response.

  • Support deflection

    The share of support requests resolved without an engineer.

  • Retention

    How much recurring revenue is kept at renewal.

  • Expansion revenue

    Growth in revenue from existing customers.

  • Value against business case

    Realised benefit compared with the approved case, reviewed at each stage.

Engagement pattern

Illustrative

A scaling software company protecting its renewals

Situation

A growing software business has added customers faster than it has added support and success staff. Usage data lives in the product, tickets in a support tool and contracts in the sales system. Renewal surprises are becoming common, and each team has started its own AI experiment. The executive team wants a single plan with clear ownership.

How we would approach it

  1. 01We would review lifecycle data, current AI experiments and team capacity.
  2. 02We would prioritise use cases across support, success and sales.
  3. 03We would set out a business case, ownership model and phased roadmap.
  4. 04We would then assure the work carried out by the company and its chosen partners.

What we would measure

  • Retention
  • Support deflection
  • Early risk detection
  • Customer success capacity

An illustrative scenario describing a typical situation. It is not a client case study and reports no results.

A first conversation is enough to tell whether we can help.

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