AI Brain for B2B Sales
Senior knowhow in a system. Quotes at analyst speed.
The senior knowhow that shapes every quotation, encoded into a system that builds them. The data that flows through, turned into the analyst the sales team never had time to hire. Tailored to the company, integrated with its CRM, owned by its sales operation.
How this works
Capture. Automate. Analyse.
The shape of the engagement follows the natural sequence. We capture the senior knowhow into a structured form the system can use. We build the agentic quotation system that runs on it. And we layer the AI analyst agent on top of the data flowing through, so the operational intelligence shows up the moment there is enough data to mean anything.
Capture
We sit with the seniors who actually build quotes today. We map the data sources they reach for, the decisions they make, the edge cases they handle, the rules they apply without thinking. We turn it into a structured knowhow base the agentic system can act on. The company keeps it.
Automate
We build the tailored quotation tool. An agentic system that knows what to fetch and how to assemble it, integrated with your CRM (or running on Ravon CRM), with Bruin underneath as the data fabric. Every quote goes through it. Senior review is by exception, not by default.
Analyse
On top of the quotation data and the CRM, we deploy the AI analyst agent. Daily, weekly, monthly KPIs. Custom dashboards. Pipeline forecasting. Natural language Q&A on Slack, Teams, WhatsApp. Reports pushed to wherever the team already lives, on whatever cadence makes sense.
The Pattern
Capture the knowhow. Automate the quotations. Analyse the data the system creates.
Who this is for
B2B sales teams where every quotation is a project.
The companies we work with on this pod sell something where the price on the quote is the easy part. The hard part is the scoping that sits behind it: bills of materials, contractor selection, internal resource allocation, regulatory checks, comparable project lookup, risk loading. Every quote is, in effect, a small engineering exercise dressed up as a commercial document.
Strong fit
- B2B sales team that builds complex, multi input quotations against RFPs or scoped opportunities. Not single product price list selling.
- Quotation knowhow lives mainly in the heads of two to ten senior people, and that team is the bottleneck on how much the company can quote in a week.
- The company has at least some structured data to work from: catalogues, rate cards, BOMs, past quotes, an existing CRM, even if it is messy.
- Leadership wants real time visibility into the sales operation: pipeline, win rates, forecasts, segment performance, pushed to channels they actually read.
- Open to integrating with their existing CRM, or moving to Ravon CRM if their current setup is the bottleneck rather than the foundation.
Not the right fit
- Companies with simple, repeatable quotations that are essentially price list lookups. The engagement economics do not justify it.
- Companies with no structured data at all. There is nothing yet for the agentic system to fetch from.
- Companies looking for a configure it yourself SaaS quotation tool with no consulting and build layer.
- Companies whose senior team does not actually have proprietary quotation knowhow worth capturing. There is no asset to encode.
Phase 1 · Capture
The knowhow that lives in senior heads: into a system the company owns.
Most quotation tools assume the company already has its process written down somewhere. The reality is that the process lives in the heads of three or four people who have been doing it for years. Phase 1 is the work of getting it out of those heads and into a form the system can run on, without losing the judgement that made it worth capturing in the first place.
| Deliverable | What it includes |
|---|---|
| Senior knowhow extraction | Structured sessions with the people who actually build quotes today. We work shoulder to shoulder through real and recent quotes, mapping the decisions they make, the inputs they pull, the rules they apply, and the gut calls they default to without realising. |
| Data and source inventory | Where the inputs to a quote actually live: product catalogues, BOMs, contractor rates, internal labour rates, regulatory references, past project records, supplier price lists, CRM. What is structured, what is in spreadsheets, what is in PDFs, what is in nobody's hands at all. |
| Decision logic map | For each kind of quote: what factors drive what choices. Material selection logic. Contractor selection logic. Headcount logic. Margin policy. Risk loading. Discounting rules. Captured in a form the agentic system can reason from. |
| Edge case library | What seniors do when the standard path does not apply. The ten percent of quotes that consume ninety percent of senior time. Documented as patterns the system can recognise and either handle or escalate cleanly. |
| Quality and review criteria | What makes a good quote in this company. What gets red flagged for senior review. What can ship junior only. What cannot ship without compliance, legal, or technical sign off. And why. |
The Output
A structured, versioned knowhow base owned by the company. The senior team's playbook out of their heads and into a form that compounds instead of evaporates.
Phase 2 · Automate
The agentic system that builds quotations the way the senior team would.
Phase 2 puts the captured knowhow to work. We build the tailored quotation tool, an agentic system that takes an opportunity in, fetches the right inputs from the right places, applies the company's logic, and produces a quote in the company's format. Senior review becomes the exception. Sales velocity becomes structural.
What gets built
| Tailored quotation tool | Built around how this specific company quotes: its products, its costing model, its document format, its approval flow. Not a configurable template; a system shaped to the business. |
| Agentic data fetching | Multi agent backend that knows which catalogue, BOM, rate card, regulatory reference, or past project to pull from for any given input. Reaches across systems instead of forcing the salesperson to. |
| Senior style assembly | The system applies the decision logic captured in Phase 1: material selection, contractor scoping, headcount, margin, risk loading. The output reads the way a senior would have built it, because it was built from how a senior actually thinks. |
| Escalation by exception | When the system hits an edge case it does not have confidence on, it surfaces the specific decision to a senior, with the context already assembled. Senior time goes to the calls that genuinely need it, not to every quote. |
| Realtime sales assistance | A copilot the sales team uses during client conversations, pulling specs, checking scoping assumptions, drafting follow ups, surfacing comparable past deals, so the sales team is faster and more accurate in the moment. |
| Continuous improvement | Every quote that flows through the system, and every outcome that follows, makes the next one sharper. Win and loss data, pricing data, scoping data, all captured structurally and fed back into the knowhow base. |
Integrated with your CRM. Or running on Ravon CRM, with Bruin underneath.
The quotation system does not work in isolation. It needs to read from and write to wherever the company's deal data lives, and it needs a data fabric underneath that can handle the structured and unstructured inputs without falling over. We build for both options, and pick whichever one fits the company's existing posture.
| Existing CRM integration | Salesforce, HubSpot, Microsoft Dynamics, Pipedrive. The quotation tool slots in alongside what the team already uses, reading deal context and writing quotes back into the opportunity record. No replatform required. |
| Ravon CRM option | For companies whose existing CRM is the bottleneck rather than the foundation, we deploy on Ravon CRM, built natively around the agentic quotation system and the AI analyst layer, with the integrations and the data model already wired together. |
| Bruin as the data fabric | Sitting underneath both options, Bruin manages the data flow between the quotation system, the CRM, the source systems (catalogues, BOMs, rate cards, ERP), and the analyst layer above. The plumbing stays clean as the system grows. |
| Source system connectors | Catalogues, ERP, supplier feeds, regulatory databases, past project archives, wired in so the agentic system can fetch what it needs without anyone copy pasting between tabs. |
Phase 3 · Analyse
The AI analyst the sales operation never had time to hire.
Once quotations flow through the system, the data the system creates becomes the most valuable thing the sales operation owns. Win rates, pricing patterns, scoping assumptions, contractor performance, segment behaviour, all of it structured in one place. Phase 3 turns that data into intelligence the leadership can actually use, in the form they want it in.
| Deliverable | What it includes |
|---|---|
| Daily, weekly, monthly KPI reports | Auto generated by the analyst agent. Pushed to Slack, Teams, WhatsApp, or email, wherever the leadership already reads. The CFO does not have to ask; the report shows up. |
| Custom dashboards | Built around the KPIs this company actually runs on, not a generic CRM dashboard. Pipeline by segment, win rate by quote type, quote turnaround time, senior time per deal, anything the leadership wants to track. |
| Pipeline forecasting | Forecasts driven by the actual scoping and quotation data, not gut calls in a sales meeting. Confidence weighted, broken down by deal stage and segment, updated continuously. |
| Client segment intelligence | Which segments win at what price, which deals slip and why, which kinds of quotes lose to which competitors. The intelligence to actually shape go to market decisions, not just describe them after the fact. |
| Natural language Q&A | "What's our win rate on quotes over £500K this quarter?" "Which deals are most likely to close in the next 30 days?" "Why are we losing deals in segment X?" Asked on Slack, Teams, or WhatsApp. Answered with data, not guesses. |
The Compounding Effect
Every quote teaches the system. Every report teaches the leadership. The longer it runs, the bigger the gap with one running on intuition and spreadsheets.
What changes for the company
Faster quotes. Sharper sales. Visibility you can act on.
The point of this pod is not to install a tool. It is to change the economics of the sales operation: what it takes to quote, who has to be involved, how much the leadership can see, and where the team's time actually goes.
Quotation turnaround drops dramatically. Quotes that took days now take hours. Quotes that took hours go out in minutes. The sales team can chase more pipeline because each opportunity costs less to respond to.
Senior time goes back to senior work. Senior personnel are no longer the bottleneck on every non trivial quote. Their time goes to the edge cases that genuinely need them, and to revenue generating work. Not to checking margins on standard scopes.
Junior productivity arrives months earlier. New sales hires ramp against a system that already encodes the senior knowhow. They produce quality output from week one instead of waiting for tacit knowledge to seep in over a year.
The leadership sees the operation in real time. Daily, weekly, monthly. KPIs land in the channels the leadership already uses. Forecasts are credible because they are built from actual data. Strategy decisions stop running on month old reports.
Growth bottlenecks become visible. Where the company is losing deals, why, in which segments, against which competitors. The analyst surfaces it instead of nobody having time to look. Problems get caught when they are still fixable.
The knowhow becomes a company asset, not a personal one. When a senior leaves, the playbook stays. When the company hires, the new person plugs into a system, not into a folklore. The compounding goes to the company, not to the calendar of one or two people.
Expected outcomes
What good looks like: what we measure and where it lands.
The table below sets it out as concrete outcomes: what we track, and where well fitted engagements typically land within six to twelve months of full deployment. Specific targets are agreed in writing against the company's baseline, measured during Capture.
| Outcome | What we measure · Where it lands |
|---|---|
| Quotation turnaround time | Time from RFP in to quote out. Typical baseline: 2 to 5 days for complex quotations. Target: under 24 hours on routine work, often under 4 hours. Speed becomes the company's most repeatable sales lever. |
| Win rate on speed sensitive deals | Share of first touch RFPs the company wins where response time matters. Typical uplift: 5 to 15 percentage points, driven by being in front of the client first with a more complete and better targeted quote than the late competitor. |
| Senior time on quotations | Hours per week senior staff spend on quotation production. Typical reclaim: 60 to 80% of that time, with seniors only touching exceptions, complex deals, and judgement calls. Back on the work that actually grows the business. |
| Cost per quotation | Internal cost per quote, senior plus junior time at loaded rates. Typical reduction: 50 to 70%, with marginal cost approaching zero as quotation volume grows. Sales scales without proportional headcount cost. |
| Pipeline visibility for leadership | Where leadership can see pipeline, win rates, segment performance, and forecast accuracy. After Analyse: daily and weekly KPIs land in the channels the leadership team already uses. No hand built reports. |
| Key person risk on the quotation function | Share of quotations that depend on a specific senior individual to produce. Typical: from high concentration in two to ten heads to negligible. The brain holds the knowhow; the people manage the exceptions. |
Why Ravon
Three things we do that the alternatives do not.
We capture the knowhow first.
Most quotation tools assume the company's process is generic and start by configuring a template. We start by sitting with the senior people who actually build quotes today and getting their thinking out of their heads. The system that ships is built from how this specific team actually quotes. Not from a vendor's idea of how a sales team should work.
Tailored automation, not a SaaS.
The quotation tool we build is shaped around your products, your costing, your document format, your edge cases, your approval workflow. It integrates with your existing CRM, or runs on Ravon CRM if that fits better, with Bruin handling the data fabric underneath. The company gets a system that fits, owned by them, kept current.
The analyst agent is part of the build.
Most quotation projects stop at the automation. We layer the AI analyst agent on top: daily, weekly, monthly KPIs pushed to the channels the team uses, custom dashboards, pipeline forecasting, natural language Q&A. The data the system creates becomes intelligence the leadership can act on, not data sitting in a database nobody reads.
Why now
Once a competitor has the brain, the gap compounds every week.
The pattern we are seeing across B2B sales operations is consistent. The first company in a market that gets quotation automation and an AI analyst layer running starts quoting faster, scoping more accurately, and forecasting more credibly than anyone else. Within two to three quarters the gap is visible to clients, to talent, and to the rest of the leadership team.
The compounding is not subtle. Quotation velocity feeds win rate. Win rate feeds pipeline data. Pipeline data feeds the analyst layer. The analyst layer feeds strategy decisions. Each loop makes the next one tighter, and the company that has the loops running is operating on a different curve than the one that does not.
What happens to the companies that wait
Deals get lost on response time alone. The competitor with the brain is in front of the client first, with a more precise quote. The slower team's quote is judged against a sharper anchor and often arrives after the decision has already shifted.
Senior burnout and key person risk hit the same week. The two or three people who hold the quotation knowhow stay overloaded, ramp the rest of the team slowly, and become a single point of failure the leadership only fully appreciates when one of them leaves.
The forecasting gap opens up. The company without the analyst layer is forecasting on intuition while the competitor is forecasting on real time pipeline data. Capital allocation, hiring, and capacity planning decisions diverge accordingly.
The catch up bill arrives later, at premium price. Companies that wait will eventually buy this capability, from vendors with leverage, on timelines they no longer control.
The Choice
Build the brain now while the competitive window is still open. Or buy it back later, after a competitor has taught the market what fast quoting looks like.
How we work
Diagnosis before prescription.
Every engagement follows three phases — discovery and diagnostic, impact & scoping, and solution design with explicit checkpoints. See how we reduce delivery risk before you commit scope.
Start a discovery
Most engagements begin with a conversation about context.
We do not send a proposal before we understand the problem. Start by telling us about your decision context. We will identify the highest leverage intervention areas before any scope is agreed.