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Sales Enablement AI

Proposal drafts in minutes instead of hours. Lead scoring that reflects your actual close patterns, not a vendor's generic model. Deal-desk workflows that surface risk before the forecast call. All running on your data, with the CRM and conversation history that defines your business staying inside your boundary.

Why generic sales AI vendors underperform on real pipelines

Off-the-shelf sales AI tools are trained on aggregate data across thousands of companies. They're good at generic patterns (response templates, meeting summaries, basic lead scoring) and bad at your specific sales motion — the sector-specific proof points that close your deals, the voice your best AE uses in a follow-up, the objection handling that actually works for your ICP. The stuff that differentiates good reps from great ones is the stuff generic vendors can't see.

A private sales AI system trained and conditioned on your winning proposals, your conversation recordings, your closed-won vs closed-lost patterns — that's where the leverage actually lives.

What we build for sales teams

01. Proposal and SOW generation

RAG over your proposal library, case studies, pricing references, and successful past deals. An AE describes the opportunity; the system drafts a proposal in your firm's voice, with the right case studies pulled automatically, pricing scoped to the deal size, and the competitive positioning that actually won similar deals. Typical time savings: 40–60% on proposal creation, with higher quality because the best language from prior wins gets reused. See RAG pipelines.

02. Lead scoring with your actual close patterns

Trained on your historical pipeline — which leads became opportunities, which opportunities closed, what the shape of a winning deal looked like at each stage. Scores incoming leads against that baseline, not an industry-average one. Outputs: numerical score, top three contributing factors, and the next recommended action. Integrates with Salesforce, HubSpot, Pipedrive, or whatever CRM you run.

03. Deal-desk automation and risk surfacing

Every opportunity above a threshold gets an AI-generated risk read: missing stakeholders, stalled activity, competitor mentions in call transcripts, pricing outside historical win-rate bands. Flagged to the deal-desk or sales manager before the forecast call, not after. Multi-agent architecture (one agent monitors activity, another benchmarks against historical deals, a third drafts the risk memo) — see multi-agent systems.

04. Pipeline intelligence and forecast hygiene

Aggregate dashboards with honest signals: deals past their historical median close time, opps where the last customer activity is older than average, forecast categories that are drifting from reality. The AI does the pattern-matching; your RevOps team acts on it.

05. Outbound personalization (when appropriate)

Cold-email and LinkedIn-outreach drafting conditioned on ICP fit and recent company signals. We build this carefully — the failure mode of AI outbound is obvious spammy slop that hurts your brand. Our default is quality-over-volume, with human review gates on every send.

Why "private" matters for sales AI

Sales data is acutely sensitive: customer lists, pricing, contract terms, competitive intelligence, conversation recordings, personnel dynamics. Handing that to a third-party cloud AI vendor for training-adjacent processing is a risk a lot of sales leaders haven't fully thought through. Local deployment of the sales AI stack keeps it inside the same boundary as your CRM. See local AI deployment.

Integration targets

Pricing

POC ($25K–$60K, 4–6 weeks): proposal generation OR lead scoring OR deal-desk automation shipped on one use case, integrated with one CRM. Production build ($75K+, 8–16 weeks): full pipeline, multi-team rollout, admin UI, 30-day stabilization.

Frequently asked questions

How is this different from Gong, Outreach, or Chorus?

Those products specialize in conversation intelligence and sequenced outreach — excellent tools in their lane. What we build is complementary, not competing: proposal generation and lead scoring on your pipeline data, using your winning patterns, running on your infrastructure. Many of our deployments coexist with Gong or similar tools (we ingest their transcripts; they ingest our intelligence).

Do you train a custom model on our deal data?

Usually not — fine-tuning adds cost and maintenance complexity that a RAG-based approach often matches in practice. Retrieving relevant past deals at query time and letting a strong base LLM reason over them is cheaper, faster to update, and more auditable. If the problem genuinely requires fine-tuning (very specific format constraints, low-resource vocabulary), we'll propose it — but we default to the simpler architecture that works.

Can this integrate with our existing CRM workflow?

Almost certainly. We've shipped against Salesforce, HubSpot, Pipedrive, and custom CRMs. The AI layer sits alongside the CRM; reps keep using the CRM UI they already know, with AI-drafted content appearing where they need it (proposal templates, opportunity notes, email drafts). No forklift upgrade.

What about data security — we can't have customer lists in an external AI vendor's prompt logs.

That's the core reason we default to local deployment for sales AI. Customer data, contract terms, and pipeline activity stay inside your boundary — no prompts or completions hit a third-party service. When cloud deployment is acceptable for your risk posture, we'll tell you; when it isn't, local is the answer.

How long before a new rep sees value?

Proposal generation and research synthesis provide same-day value. Lead scoring benefits compound over 1–2 quarters as the model sees more of your pipeline. Deal-desk automation kicks in immediately for new opportunities above the threshold.

Ready to start?

Book a free 30-minute AI Readiness Assessment. No pitch deck. No retainer ask. Just a working session to map your stack and surface the two or three highest-ROI AI interventions for your situation.

Book free assessment Email Marc directly