Applied AI for
B2B Revenue Teams

Imagine giving every seller the instincts of your best performer. Next Quarter brings together seller context, market signals and applied AI to uncover opportunities, recommend the next move and help every revenue conversation land with impact.

YOUR BUSINESS CONTEXTACCOUNT AND MARKET SIGNALSPitch DecksCase StudiesOfferingsBattle CardsCRM ContextSales CollateralPOV DecksIntent SignalsCXO ProfilesHiring SignalsTech StackEarnings CallsPublic FilingsIndustry TrendsWHERE TO FOCUSStrategic InitiativesWHO TO ENGAGEDecision MakersWHAT TO SAYMeeting Prep

Q Pilot brings two kinds of intelligence together. From your own business it learns sales collateral, pitch decks, case studies, offerings, POV decks, battle cards, brochures and CRM context. From outside it tracks buyer intent, CXO profiles, hiring signals, technology stack, LinkedIn activity, industry trends, leadership changes, news, podcasts, interviews, press releases, quarterly results, earnings calls, MD&A and public filings. The Q Pilot Context Engine connects both worlds continuously and turns them into three answers for revenue teams: where to focus, who to engage and what to say.

Trusted by successful Revenue teams

Cockroach Labs Siemens Capgemini Mastek JAS Worldwide Black & Veatch Wipro Apexon Coforge Lonza Capsugel CriticalRiver Tazmanian

Applied AI for the teams that carry B2B revenue

Four teams. One shared need: credible context that turns into action.

The gap

Revenue teams need intelligence, not more data

Most systems record what already happened. Next Quarter helps teams understand what is changing, why it matters and what to do next.

  • Strategic and Key Account Teams
  • Sales Teams
  • Marketing Teams
  • AI Transformation Teams

Account plans are being completed. Account strategy is still fragmented.

Critical seller context lives across presentations, CRM records, meeting notes and individual experience. Plans are assembled for reviews rather than used to guide weekly decisions. Stakeholder coverage remains shallow, whitespace is difficult to substantiate and collaboration depends too heavily on the account manager.

Presentations CRM records Meeting notes Individual experience

Replace quarterly form filling with credible, current intelligence that keeps account strategy alive between reviews.

A full territory does not tell a seller where to spend the next hour.

Account lists rarely reveal which companies are actively considering change, which quiet accounts contain relevant opportunities, where recently won customers may expand or which stakeholders can move a decision forward. Sellers lose time assembling research and still enter conversations without a compelling reason to engage.

Fit Timing Change Stakeholders

Direct seller attention towards the accounts where fit, timing, change and stakeholder context create a credible reason to act now.

ABM and events create activity. Revenue impact remains difficult to prove.

Account tiers are often subjective. Intent signals do not consistently become messages or plays. Channel plans, content and sales actions are developed separately. Events add high costs, complex coordination, slow follow up and attribution that frequently stops at registrations, scans and meetings.

Audience Message Activation Measurement

Connect audience choice, message, activation, sales action and measurement around a shared view of the account.

AI experimentation is growing faster than measurable adoption.

Transformation leaders are expected to modernise sales, marketing and finance while navigating fragmented data, governance requirements, specialist skills, resistance to change and unclear return. Building internally can demand significant upfront cost before the organisation knows which workflows will create value.

Data Governance Skills Adoption

Begin with measurable business outcomes and proven revenue workflows, then scale with governance, context and adoption built into the programme.

Context in. Clear next moves out.

Next Quarter connects the seller context you already own with the market signals you are missing, then applies AI to turn both into decisions your teams can act on.

See It on Your Accounts
01

Unified account context

Customer records, meeting history, documents and market signals are brought together so every team works from the same current picture of the account.

02

Signals that explain change

Leadership moves, strategic initiatives, financial shifts and stakeholder activity are interpreted, not just collected, so teams understand why an account is moving.

03

Recommended next moves

Applied AI turns context into a specific action: the account to prioritise, the stakeholder to engage, the message that fits and the reason it is relevant now.

04

Adoption inside the workflow

Guidance appears where revenue work already happens, so account planning, prospecting, campaigns and event follow up all improve without new process overhead.

Don’t take our word for it. Real teams. Real momentum. Real talk.

Customer story

“Our teams stopped rebuilding account research and started arriving with a point of view. The context is already there, and the recommended next move is specific enough to act on.”
Global revenue leadership
Enterprise technology services

Pipeline coverage

Median change in qualified coverage on the accounts a team prioritised in Next Quarter, measured over the two quarters after rollout and aggregated across enterprise deployments.

18%

average lift in qualified pipeline coverage on prioritised accounts

Research time saved

Self-reported time previously spent assembling account context and pre-call research, averaged across surveyed sellers in active deployments.

6 hrs

of account research time saved per seller each week

Win rate

Relative improvement in closed-won rate on renewal and expansion opportunities versus the prior comparable period, aggregated across enterprise customers.

11%

improvement in win rate across strategic account renewals and expansions

Time to adoption

Typical elapsed time from kickoff to sustained weekly use inside existing CRM and account-planning workflows. Varies with data readiness and scope.

60 days

to measurable adoption inside existing revenue workflows

Thinking for revenue leaders

View all insights

FAQ

Common questions from revenue leaders

What is Q-Pilot and how does it help B2B revenue teams? +

Q-Pilot is an Applied AI platform that helps B2B revenue teams understand accounts, prioritize opportunities, engage the right stakeholders, and take the right next action. It combines external market signals with your CRM, account context, and company capabilities to answer four critical questions: Where should I focus? Why now? Who should I engage? What should I do next?

Who is Q-Pilot built for? +

Q-Pilot is built for B2B revenue teams across Sales, Strategic Accounts, Marketing, and Revenue Operations. It helps sellers research and prioritize accounts, helps leaders identify opportunities and guide execution, and gives Marketing and RevOps teams richer account context for targeting, planning, and orchestration.

What problems does Q-Pilot help revenue teams solve? +

Q-Pilot helps reduce the manual work required to understand an account and decide what to do next. Teams can use it for account prioritization, account research, opportunity discovery, stakeholder intelligence, Meeting Prep, personalized outreach, Sales Playbooks, Pitch Decks, and strategic account planning. The goal is simple: spend less time finding and interpreting information, and more time acting on it.

What data sources does Q-Pilot use to generate account intelligence? +

Q-Pilot can combine multiple external and internal sources, including company news, financial filings, earnings commentary, leadership changes, hiring activity, intent signals, technology signals, market developments, and other relevant business information. It can also incorporate your CRM data, sales collateral, case studies, product information, account plans, opportunities, and customer context. This allows Q-Pilot to interpret external signals through the lens of what your organization actually sells.

How does Q-Pilot triangulate internal context and external signals? +

Q-Pilot evaluates signals together rather than treating each one in isolation. For example, a leadership change, rising intent, new hiring activity, and a strategic initiative mentioned in an earnings call may become significantly more meaningful when combined with an existing CRM opportunity and a relevant capability your company offers. Q-Pilot connects those signals to help determine why something matters, where an opportunity may exist, and what action to take next.

How accurate and current is Q-Pilot’s intelligence? +

Q-Pilot is designed to improve confidence by using multiple signals, supporting evidence, and source context rather than relying on a single data point. Different sources refresh at different frequencies, so Q-Pilot can distinguish between fast-moving events such as leadership changes and news, and longer-term strategic context such as transformation initiatives. The objective is to provide evidence-backed intelligence that sellers can confidently use in customer conversations.

Can I verify the sources behind Q-Pilot’s recommendations? +

Yes. Where source information is available, Q-Pilot can surface the evidence behind an insight or recommendation. This gives users greater transparency into what Q-Pilot found, where the information came from, and why it influenced the recommendation. That traceability helps sellers validate important insights before using them in outreach, meetings, or account strategy.

How is Q-Pilot different from ChatGPT or other general-purpose AI tools? +

General-purpose AI tools are designed to answer a broad range of questions. Q-Pilot is purpose-built for B2B revenue workflows. It combines external account intelligence with your CRM context, company capabilities, sales content, and go-to-market priorities. So instead of simply answering, “What is happening at this company?” Q-Pilot helps answer, “What is happening that matters to us, who should we engage, and what should we do next?”

How is Q-Pilot different from traditional sales intelligence, intent, and contact data platforms? +

Traditional sales intelligence platforms typically surface individual signals such as contacts, intent, company data, or recent events. Q-Pilot connects those signals with your CRM context, account priorities, and what your organization sells to explain why something matters and what to do next. Instead of stopping at “this account is showing intent,” Q-Pilot helps teams understand what may be driving it, where an opportunity exists, who to engage, and how to act.

How do sellers use Q-Pilot in their day-to-day workflow? +

Sellers can use Q-Pilot throughout the sales cycle, from deciding which accounts to focus on to preparing for executive conversations. Typical workflows include account research, opportunity discovery, stakeholder intelligence, Meeting Prep, personalized outreach, Sales Playbooks, Pitch Decks, and account planning. Q-Pilot is designed to reduce research and preparation time while helping sellers enter every interaction with stronger context.

Does Q-Pilot integrate with our CRM and existing revenue technology stack? +

Yes. Q-Pilot is designed to work alongside the systems your revenue team already uses. CRM data such as opportunities, contacts, products, account history, and deal stages can provide additional context to external signals, helping Q-Pilot generate more relevant recommendations. Depending on the deployment, Q-Pilot can also support APIs, MCP-based connectivity, and other enterprise integration approaches.

How quickly can our team get started with Q-Pilot? +

Teams can begin with a focused set of target accounts and priority workflows rather than implementing everything at once. Q-Pilot can incorporate your existing sales content and account context first, with CRM and additional integrations introduced as needed. This makes it possible to validate value quickly, then expand across more accounts, teams, and use cases over time.

How does Q-Pilot pricing work? +

Q-Pilot pricing is based on the scope of your deployment and how your team plans to use the platform. Factors can include account coverage, users, intelligence refresh requirements, Meeting Preps, Pitch Decks, specialized workflows, integrations, and overall program scale. Customers can start with a focused use case and expand as adoption grows. Contact our team for pricing based on your accounts, teams, and revenue objectives.

How does Next Quarter protect our data, and is our information used to train public AI models? +

Q-Pilot is designed with enterprise data security and governance in mind. Customer data is handled within the agreed deployment and access model, with controls that can include authentication, permissions, data separation, encryption, and integration governance. Next Quarter can also work with Security, IT, Legal, and Procurement teams to review data handling, AI usage, retention, architecture, and applicable compliance requirements before deployment.

Can we validate Q-Pilot on our own target accounts before a broader rollout? +

Yes. Q-Pilot can be evaluated using the accounts your team already knows and cares about. A focused evaluation allows sellers to assess whether Q-Pilot uncovers relevant intelligence, identifies the right stakeholders, improves Meeting Prep and outreach, and recommends useful next actions. This gives your team a practical way to validate value before expanding Q-Pilot across additional accounts, teams, and revenue motions.

Your first account is on us

See what Q-Pilot can uncover. Your first account is on us.

We will use your real accounts to show the context you already own, the signals you are missing and the next moves your teams could be acting on this quarter.

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