Selling time is squeezed
Sellers spend much of the week on research, updates and internal requests. Time with customers is what remains.
AI FOR SALES
Sales leaders do not need another tool. They need selling work taken off their teams and a forecast they can believe. We start with the business case, then design and build the AI agents, assistants and integrations that deliver it, and measure the result.
The problem
The symptoms are familiar. The causes usually sit in process, data and ownership, so that is where we start.
The pipeline is hard to believe
Stages, dates and amounts drift from reality. Forecast calls become debates about data, not decisions about deals.
Leads wait too long
Enquiries arrive faster than the team can qualify them. Good prospects cool while poor ones consume attention.
AI pilots do not reach production
Experiments start without an owner, a baseline or a route to live use. Leaders cannot tell which ones deserve to scale.
Blueprint
We use this picture to agree scope with your leadership team, then we build to it. It shows where AI sits, what governs it and what it depends on.
05
Experience
Where buyers and sellers meet the AI.
- Website chat
- Slack channels
- Seller workspace
04
AI agents and assistants
The agents and assistants that take on defined selling work.
- Agentforce agents
- Custom MCP servers
- AI assistants for sellers
- Automated follow-up
03
Trust and guardrails
The rules that decide what AI may do alone.
- Permission rules
- Approval thresholds
- Audit trail
- Human handoff
02
Data
One governed view of accounts, activity and intent.
- Data 360
- Unified accounts
- Engagement history
- Intent signals
01
Systems of record
The sources the AI reads from and writes to.
- Agentforce Sales
- Pricing and quoting
- Finance and billing
- Contract records
Use cases
We assess each candidate for value, feasibility and risk, then build in the agreed order. These are the ones leaders ask about most.
Lead qualification
An AI agent answers enquiries, asks the qualifying questions and scores fit against agreed criteria. A seller takes over every qualified conversation.
Pipeline assistant
A custom MCP server lets an AI assistant read pipeline data and propose record updates under your permission rules. Sellers ask in plain language and approve each change.
Pipeline hygiene
An agent flags stale dates, missing next steps and stalled stages, and proposes corrections. The opportunity owner accepts or rejects each one.
Account research
An assistant prepares a briefing from account history, open cases and public information before a meeting. The seller decides what to use.
Quote support
An agent drafts a quote within approved price and discount rules. Anything outside those rules goes to a manager for approval.
Forecast and renewal signals
An agent compares committed deals and upcoming renewals with activity and history, and highlights the ones at risk. Sales leaders make the call.
Workflow
One example of how signal, data, AI and human judgement fit together.
- 01
Inbound enquiry
A prospect asks about pricing on the website.
- 02
Account context
The enquiry is matched to a known account and its history.
- 03
Qualification agent
Asks qualifying questions and scores fit against agreed criteria.
- 04
Account executive
Reviews the summary and accepts the handoff.
- 05
Qualified opportunity
A meeting is booked and the record is complete.
Illustrative views
Sample screens with sample values. We use views like these to agree what good looks like before we build.
Sales AI performance
Sample quarter
Leads qualified by AI agent
64%
Time to first response
3 min
Pipeline records complete
91%
Handoffs accepted by sellers
82%
Qualified leads handed to sellers
- Lead qualification78
- Pipeline assistant66
- Account research54
- Quote support41
Pipeline assistant
AI assistant
Account executive
Which of my open deals have had no activity in the last three weeks?
AI assistant
Four of your open opportunities have had no logged activity in that period. Two have close dates this month. I can move those two dates to next month and add a next step to each.
System. Record updates need the owner to approve. Proposed changes shown for review.
Account executive
Approve the first. Leave the second as it is, I am meeting them on Thursday.
AI assistant
Done. One opportunity updated and the change is logged against your name. The second is unchanged.
- Read pipeline within user permissions
- Found inactive opportunities
- Proposed record updates
- Applied the approved change
Both views are illustrative. Values are samples, not client results.
Outcomes
We agree baselines and targets with you at the start, then report against them. We do not quote improvement figures we cannot stand behind.
Targets are set with each client against their own baseline.
Selling time
The share of the working week sellers spend with customers and prospects.
Lead response time
How quickly a new enquiry receives a useful, qualified response.
Forecast accuracy
How closely the committed forecast matches what actually closes.
Pipeline data quality
The share of open opportunities with current dates, stages and next steps.
Renewal retention
The proportion of renewals secured, and how early risk is spotted.
Time to production
How long it takes to move from decision to a governed AI agent or assistant doing real work.
Approach
We advise and we build. Strategy and the business case come first. Design, build, launch and measurement follow.
01
Discover
We map how selling time is spent, where the pipeline loses credibility and what your data can support today.
Outputs
- Sales process review
- AI readiness assessment
- Opportunity longlist
02
Define
We prioritise the use cases, set the baselines and write the business case your board will ask for.
Outputs
- Prioritised use cases
- Business case
- Success measures
03
Design
We design and build the agents, assistants and integrations, with the guardrails that decide when a seller steps in.
Outputs
- Solution design
- Working agents and assistants
- Guardrail and approval rules
04
Direct
We launch with your sales teams, govern how the AI is used and measure value against the business case.
Outputs
- Launch and adoption plan
- Governance reviews
- Value tracking
Start with a conversation. We will tell you plainly whether there is a case.