15+
AI Agents Built
3x
Pipeline Growth
40%
Time Saved
24h
Support Response
The Challenge
Sales reps spend only 28% of their time actually selling. The rest goes to researching prospects, entering data into the CRM, writing emails, preparing for calls, updating pipeline stages, and generating reports. Each of these tasks requires switching between multiple tools — LinkedIn, the CRM, email, the company website, news sources, competitive intelligence tools. By the time a rep has researched a prospect, personalised their outreach, and logged everything in the CRM, an hour has passed for a single contact. This is not a productivity problem — it is a systemic failure to leverage AI for the tasks that AI can do better and faster than humans.
What I Offer
I build AI agents for sales teams — not chatbots, not simple automations, but autonomous agents that can reason, plan, and execute multi-step tasks on behalf of your sales reps. Using LangChain's agent framework, these AI agents can research a prospect across multiple sources, synthesise the findings, draft a personalised email, enrich the CRM record, and schedule follow-up tasks — all from a single instruction.
The difference between an AI agent and simple automation is autonomy and reasoning. An automation follows a fixed script. An agent decides which tools to use, in what order, based on the situation. If the prospect's LinkedIn is uninformative, the agent tries their company website. If the company just raised funding, the agent adjusts the outreach angle. This adaptive intelligence is what makes AI agents transformative for sales.
Prospect Research
The agent researches prospects across LinkedIn, company websites, news, and industry databases u2014 synthesising a briefing document with key talking points.
Lead Enrichment
Automatically enrich CRM records with company size, industry, technology stack, recent news, funding status, and contact details from multiple data sources.
Personalised Outreach Drafting
Draft personalised emails and LinkedIn messages based on the research findings u2014 referencing specific company events, challenges, or achievements.
Pipeline Management
Update deal stages, create follow-up tasks, log activities, and flag stalled deals based on CRM data analysis.
Meeting Preparation
Generate pre-call briefs with prospect background, relevant case studies, likely objections, and suggested talking points.
Competitive Intelligence
Monitor competitor activity u2014 pricing changes, product launches, customer wins u2014 and alert reps when competitive intelligence is relevant to their deals.
What Makes AI Agents Different from Automation
Traditional automation follows a script: when X happens, do Y. An AI agent is fundamentally different. It receives a goal ("Research this prospect and draft an outreach email"), then autonomously decides how to achieve it. It selects which tools to use, in which order, based on what information is available and what is missing. If one data source comes up empty, it tries another. If it discovers something unexpected (the company just had layoffs), it adjusts its approach.
This autonomous reasoning capability makes AI agents dramatically more useful for sales tasks than traditional automation. Sales is inherently variable — every prospect is different, every company has a unique situation, and effective selling requires adapting to context. AI agents handle this variability in a way that scripted automation cannot.
How Sales AI Agents Work in Practice
Autonomous Prospect Research
A sales rep identifies a prospect they want to reach out to. Instead of spending 30-45 minutes researching across multiple platforms, they give the AI agent the prospect's name and company. The agent autonomously searches LinkedIn for the person's role, background, and recent activity. It visits the company website for product information, leadership team, and recent news. It checks Crunchbase or PitchBook for funding and growth data. It scans recent press releases and industry news. It then synthesises all findings into a concise briefing: company overview, recent developments, likely pain points, connection opportunities, and recommended outreach angle. Research that took 30-45 minutes now takes 2-3 minutes.
Personalised Outreach at Scale
Generic outreach gets ignored. Personalised outreach that references the prospect's specific situation gets responses. The problem is that true personalisation requires research — which limits how many personalised touches a rep can produce per day. The AI agent solves this by drafting outreach messages grounded in its research. Not "Dear [Name], I noticed your company is in [Industry]" generic personalisation — but "I saw your team just launched the enterprise tier last month. Companies at that growth stage often face [specific challenge]. Here is how we helped [similar company] handle that." This level of personalisation, at scale, is transformative for response rates.
CRM Data Enrichment
CRM data decays at 30% per year. Contact details change, companies pivot, people switch roles. The AI agent can systematically enrich your CRM records — updating company information, verifying contact details, adding technology stack data, and flagging records that need attention. This runs continuously in the background, keeping your database current without any rep effort. Enriched data improves segmentation, scoring, and targeting accuracy for the entire team.
Meeting Preparation Briefs
Before every call or meeting, reps should review the prospect's background, previous interactions, and prepare relevant talking points. In practice, many reps wing it because preparation takes too long. The AI agent generates pre-call briefs automatically: pulling CRM history, recent prospect activity, relevant case studies from your library, likely objections based on the prospect's profile, and suggested questions aligned with your sales methodology. The brief is delivered to the rep's inbox or Slack 30 minutes before the meeting.
Pipeline Health Monitoring
The agent monitors your sales pipeline and flags issues: deals stalled at a stage longer than average, high-value opportunities without recent activity, upcoming close dates without next steps scheduled, and deals missing critical qualification data. Rather than waiting for a weekly pipeline review to discover problems, reps and managers receive proactive alerts when deals need attention.
Impact on Sales Metrics
- 40% more selling time by automating research, data entry, and email drafting
- 3x improvement in personalised outreach volume per rep per day
- 2x higher email response rates with research-backed personalisation
- CRM data accuracy improvement from continuous enrichment
- Better pipeline visibility with proactive stalled-deal detection
- Faster ramp time for new reps who have an AI research assistant from day one
Ready to give your sales team an AI advantage? Contact me for a free sales process review, or book a call to discuss your team's needs.
Why Choose Me
Agent Architecture Expertise
I build AI agents using LangChain's agent framework with tool-use, memory, and planning capabilities. These are not prompt-and-respond chatbots u2014 they are autonomous systems that reason through multi-step tasks.
CRM Integration
Agents read from and write to your CRM (Salesforce, HubSpot, Pipedrive, Close) as naturally as a human rep would. Research, enrichment, and activity logging happen directly in your system of record.
Sales Methodology Alignment
I configure agents to align with your sales methodology u2014 MEDDIC, BANT, Challenger, SPIN u2014 so research and qualification follow the framework your team already uses.
My Process
A proven approach from concept to delivery.
Sales Process Review
I review your sales workflow, CRM setup, outreach process, and rep time allocation to identify the highest-impact agent capabilities.
Agent Design
I design the agent's tool set, reasoning prompts, and output formats u2014 calibrated to your sales methodology, ICP, and CRM structure.
Build and Test
I build the agent, connect to your data sources and CRM, and test with real prospect scenarios from your pipeline.
Deploy and Train
I deploy the agent, train your sales team on effective prompting, and monitor output quality for the first month with iterative refinement.
Technologies & Tools
Results That Speak
Client project: A 12-person B2B SaaS sales team was spending an estimated 15 hours per week per rep on prospect research, CRM data entry, and email drafting. Outreach was lightly personalised at best, and CRM data quality was poor — 40% of records had outdated information.
Result: The AI agent reduced research and admin time by 40%, freeing each rep to have 8-10 more quality conversations per week. Personalised outreach volume tripled, and email response rates increased from 3.5% to 8.2%. CRM enrichment brought data accuracy above 90%. The team generated 35% more pipeline in the first quarter after deployment.
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