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AI Agent

Virtual Sales & Customer Support Assistant

A team of AI assistants that answers visitors, spots serious buyers, fills the CRM and books sales calls. Your sales team starts with context instead of cold research.

A salesperson at a laptop in an open office, talking through a qualified inquiry
Replies to website visitors
24/7
Target time to first replyTarget, not yet measured
< 2 min
Lower cost per serious leadTarget, not yet measured
60–70%
More chats turned into booked callsTarget, not yet measured
+27%

Key details

An AI assistant that handles the first step of the sales conversation.

Challenge
Give every website visitor personal help, day and night. At the same time, lower sales costs and turn more visitors into customers.
Solution
A team of AI assistants that answers sales and support questions. It uses the company's own knowledge and connects to the CRM and call booking.
Technologies & tools
AI that understands and writes natural language, a search over the company's own documents, cloud hosting, and a direct link to the CRM. Full details are in the technical section below.

The situation

Axiomra wanted a better way to respond to website visitors, without relying only on when salespeople are free. Serious buyers can arrive outside business hours. Meanwhile, the sales team spent hours researching companies, answering the same questions and typing details into the CRM.

The goal was an assistant that could handle the first step of a buyer conversation, all in one website visit.

It would learn Axiomra's services and the visitor's business. It would check whether the visitor is a good fit. Then it would guide good-fit buyers toward a sales call.

Support staff on headsets at their desks handling first responses by hand

The problem

The project tackled three sales problems that kept coming up:

  • Leads lost to slow replies. When questions arrive after hours, or the first reply is slow, visitors can leave before anyone from sales responds.
  • Sales time spent on poor-fit visitors. Researching and screening each visitor by hand makes every good lead more expensive. It also slows the flow of new deals.
  • Mixed first impressions. Different team members handle early conversations differently, so customers get an uneven experience.

What we built

We built an AI assistant that understands and writes natural language, and answers from Axiomra's own company knowledge. Behind the chat sits a team of AI assistants, each handling one job.

Together they answer questions, look up basic facts about the visitor's company and spot serious buyers. They also add the lead to the sales system automatically, suggest relevant success stories and set up the next sales step.

Before each reply, the assistant searches Axiomra's own documents for the most relevant information. This keeps answers based on real company content, for simple questions and detailed ones about services and use cases.

What the assistant does

Agent capabilities and the business value of each
CapabilityBusiness value
Answers questions about services, day and nightFewer leads lost after hours. Visitors get a reply right away.
Looks up basic facts about the visitor's companyMakes replies more personal, without hours of manual research.
Spots serious buyers by industry, company type and needsYour sales reps can focus on the leads that fit best.
Adds each lead to the sales system automaticallyLess typing into the CRM, and more consistent records.
Suggests relevant case studies and service pagesShows visitors proof that matches their needs.
Books calls based on each sales rep's availability and locationGets visitors from a first question to a booked call faster.

How it works, step by step

  1. 1

    A visitor starts a chat

    They ask a question, describe a business problem or share company details.

  2. 2

    The assistant works out what they need

    The AI reads the request and decides which information or task is needed.

  3. 3

    It finds the right answer

    It searches Axiomra's own documents for the most relevant information.

  4. 4

    It learns about the visitor's company

    It uses the company name and details to look up basic facts. These help personalize replies and sort the lead.

  5. 5

    It checks for a good fit

    It compares the visitor against fit criteria: industry, company type, use case and service needs.

  6. 6

    It updates the CRM and shares useful content

    Serious buyers' details go into the CRM automatically. The visitor also sees matching pages and success stories.

  7. 7

    It hands off to sales

    When it's a good fit, the visitor books a call with the right sales rep, based on availability and location.

Two people in a meeting room talking across a laptop, the kind of sales call the flow ends with

Before and after

The assistant turns your website from a page people read into one that actively sells. It handles the repetitive first step. Your sales team keeps discovery, advice, negotiation and relationships.

Outcome framework: traditional process compared with the AI-agent model
MetricTraditionalAI-Agent
How fast leads get a replyOften only during business hoursTarget, not yet measuredUnder 2 minutes, 24/7
Sorting leadsSales team reviews each one by handAI helps score each lead automatically
Entering leads into the CRMDone by handDone automatically, in a consistent format
Cost per serious leadHigher, because of research and screening timeTarget, not yet measured60–70% lower cost per serious lead
Chats that turn into booked callsCurrent rateTarget, not yet measured+27% more chats turned into booked calls

What changes for your team

  • Better leads: your sales team gets richer lead details, already organized and checked for fit.
  • Less screening work: the AI assistants take over routine screening, research and data entry.
  • Faster first sales call: a serious buyer can go from first question to a booked call in one visit.
  • Coverage in every time zone: questions are captured at any hour, not just during business hours.
  • Cleaner CRM records: leads flow straight into the CRM, so there is less retyping and fewer mistakes.
  • One consistent message: answers come from approved Axiomra content, so first conversations always sound the same.

Why this matters

For an AI company, this assistant does two jobs. It helps Axiomra sell better, and it shows Axiomra running AI agents in a real sales setting. The same setup can extend to onboarding, account support, proposals, chats in other languages and quotes.

What comes next

  • Sales chats in more languages, sent to the right team for each market.
  • Calendar links that show each sales rep's availability in real time.
  • Automatic meeting summaries and CRM follow-up tasks.
  • Follow-up messages triggered by each lead's interest and fit score.
  • A dashboard showing chats, what visitors ask about most, and how many become calls.
  • Controls so your staff make the final call on high-value or sensitive deals.
TagsAI AgentsSales AutomationAI ChatbotCRM IntegrationLead Qualification24/7 Support
Under the hood (for technical teams)

Implementation phases

  1. 01Analyze the sales journey, visitor intents, qualification rules and agentic AI use cases.
  2. 02Configure the application environment, supporting libraries and serverless architecture.
  3. 03Engineer GPT-4 prompts and conversation flows for Axiomra-specific sales interactions.
  4. 04Configure specialized agents for research, lead scoring, CRM creation and content recommendation.
  5. 05Prepare and vectorize Axiomra documents for semantic retrieval and grounded responses.
  6. 06Integrate external business services, including email workflows and Pipedrive CRM.
  7. 07Establish CI/CD using AWS SAM and deploy to an AWS serverless environment.

Technology stack

Technology stack by layer
LayerToolPurpose
Generative AIOpenAI GPT-4Conversation, reasoning, response generation and task orchestration.
EmbeddingsOpenAI EmbeddingsSemantic representation of Axiomra knowledge and user queries.
Vector searchPostgreSQL + pgvectorStores embeddings and runs similarity search across company content.
ComputeAWS LambdaServerless execution of AI and integration workflows.
StorageAmazon S3Document and application asset storage.
DatabaseAmazon RDSStructured application and workflow data.
InfrastructureAWS SAMServerless application definition, packaging and deployment.
DeliveryCI/CDAutomated build, testing and deployment workflow.
CRM integrationPipedrive APILead and opportunity creation, structured sales handoff.

More case studies

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