Stop rebuilding every proposal from scratch
An AI co-pilot for sales engineers and account executives: drafts proposals from your capability catalog, past proposals, and prospect context. You review and refine; the assistant handles the assembly.
Discuss a pilot →Quick facts
- Business size
- B2B operations producing > 20 proposals / month
- Timeline
- 4-6 weeks test, 2-4 months pilot, 4-6 months production
- Budget range
- Pilot from €30k.
- Hardware
- Cloud-based; self-hosted for confidential.
- Data needed
- Past proposals, capability catalog, pricing rules, prospect / opportunity data from CRM.
- Evolution
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- Genesis
- Custom-built
- Product
- Commodity
No product gives you this. We assemble and train it around you.
What this scale means
Further right means more proven and cheaper. Further left means newer and riskier. Here is the test for each step.
- Commodity
- You could get the result yourself from a ready service, with almost no work. We rarely take these on.
- Product
- A vendor already sells this result turnkey, like shelf recognition from Trax or document reading from ABBYY. If one of them fits you, use it. You come to us when it does not: when it has to run on your own servers, cost less, or fit systems the product cannot reach.
- Custom-built
- Vendors sell only the parts. A tool like Tableau hands you charts, but the dashboards and metrics for your business still have to be built. That build is the work, and that is us.
- Genesis
- The approach exists but does not work reliably yet. You are betting on it maturing, so it costs more and carries more risk.
Expected outcomes
The Problem
Sales engineers and account executives in B2B operations spend a large slice of their time on proposal assembly. A typical proposal pulls from past similar proposals, the capability catalog, pricing rules, and prospect-specific notes, assembled by hand in Word or PowerPoint. The work is high-skill but repetitive: product descriptions get pasted again, capabilities get mapped to similar customer needs, pricing tiers get tailored for each opportunity.
The cost: a senior sales engineer’s time goes to assembly when their value is in selling, shaping strategy, and closing. Sales velocity slows. Proposal quality drifts as time pressure grows, and less-experienced AEs produce inconsistent proposals.
An AI co-pilot changes the work pattern. The AI assembles a first draft from the company’s capability catalog, past proposals, and the prospect context (from CRM). The sales engineer reviews, refines, shapes the strategic elements, and ships.
What the Solution Does
A proposal-drafting AI co-pilot integrated with your CRM and document infrastructure.
- Prospect context: read opportunity data from CRM (prospect, industry, requirements, sales-cycle stage).
- Capability matching: match prospect needs to your capability catalog.
- Past-proposal mining: find similar past proposals and reuse relevant sections.
- Pricing logic: apply pricing rules and discounting strategy per opportunity.
- Draft assembly: produce a structured proposal document.
- Review surface: the sales engineer edits, shapes strategic elements, and finalizes.
Where It Fits
This makes sense if you…
- Operate B2B sales producing more than 20 proposals per month
- Have a defined capability catalog and pricing rules
- Have a CRM with structured opportunity data
- See real cost from sales-engineer time on assembly
- Want consistency across team-generated proposals
This is probably not the right time if you…
- Have small proposal volume
- Have highly unique proposals where reuse adds little value
- Lack the structured data (capabilities, pricing, past proposals) the AI needs
Business Value
Time recovery. Proposal drafting time drops by around 60-80%, depending on reuse. Sales engineers move from assembly to selling.
Coverage of capabilities. The AI does not forget the lesser-known capabilities that could fit a prospect. Catalog coverage in proposals lifts by around 30-50%, by our count.
Consistency. Capability descriptions, pricing logic, and structural templates stay uniform across the team.
Faster turnaround. Proposals go out in hours, where they used to take days. Sales velocity improves.
How It Works
1. Capability catalog
We work with you to structure your capability data: descriptions, use cases, the customer types each one fits, and pricing tiers. This is the canonical source the AI uses.
2. Past-proposal mining
Past proposals get indexed and embedded (turned into searchable vectors). The AI finds relevant past sections for the current opportunity.
3. CRM integration
Opportunity data is read from your CRM (HubSpot, Salesforce, or custom). The AI uses it for personalization and capability matching.
4. Draft generation
The LLM drafts structured sections: executive summary, problem statement, proposed solution, capability mapping, pricing, timeline, and terms.
5. Review and refinement
The sales engineer reviews in their preferred document tool (Word, Google Docs, or a custom proposal platform). Edits are captured for retraining.
6. Continuous improvement
Edit patterns feed back. The AI gets better at the company’s voice and at typical customization needs.
Stack
LLM-backed (OpenAI, Anthropic, or self-hosted). RAG (the model looks up your catalog and past proposals before it writes) over the capability catalog and past proposals. CRM integration. Datapipe for retraining. Document-output integration (Word, Google Docs, PowerPoint, or a custom CPQ).
What You Need to Make This Work
Data. Capability catalog (structured or convertible), past proposals (200+ ideal), CRM data, pricing rules.
Integrations. CRM read access. Document output (Word, Google Docs, or custom). Optional: integration with CPQ.
Hardware. Cloud or self-hosted for confidential.
Team. Sales-ops lead. Sales engineers for review and edit-pattern feedback. Pilot AE group.
Implementation Roadmap
1. Test (4-6 weeks)
Structure the capability catalog. Index past proposals. Build first-draft generation. Pilot with sales engineers. Output: working draft generation with measured time savings.
2. Pilot (2-4 months)
Production deployment for one sales team. Wire up the CRM. Build the review workflow. Output: a working production deployment.
3. Production (4-6 months)
Full rollout. Continuous improvement.
Keep in Mind
- Capability catalog quality is the ceiling. An outdated or incomplete catalog produces bad drafts.
- Sales engineer trust is the rate-limiter. Bad drafts kill adoption. We tune conservatively at first.
- Strategic elements stay human. The AI assembles. The sales engineer shapes strategy, win-themes, and executive-relationship language. We leave room for human input by design.
- Confidentiality matters. Past proposals and pricing are sensitive. A self-hosted LLM is available for data-residency constraints.
- CRM data quality matters. Bad opportunity data produces bad personalization.
FAQ
Can the AI generate the entire proposal end-to-end?
Technically yes, but in practice we advise against it. Strategic elements (positioning, win-themes, executive language) benefit from human authorship. The right division of labor: the AI handles assembly, and humans handle strategy.
How does this differ from commercial proposal automation (PandaDoc, Loopio, etc.)?
Commercial platforms work well for template management and workflow. Our approach fits when you need deeper LLM-driven content generation, integration with non-standard CRM or capability data, fully on-prem deployment, or heavy customization beyond template selection.
Can the AI respond to RFP-style questionnaires?
Yes, that is a related capability. See technical-doc-analysis for RFP analysis. This use case handles the response-drafting side.
Confidential proposals?
Self-hosted LLM deployment supported.
Ready to Discuss?
If you operate B2B sales producing meaningful proposal volume, this is a high-impact pilot. We will walk through your capability data, past proposals, and CRM, and tell you what to expect.