Sales & Operations
CRM-Grounded Sales and Inquiry Agent
An agent that reads each new inquiry and replies using your real pricing, availability and past successful replies, with a person approving until trust is earned.
Who it is for
Typically a sales or operations lead at a business with long sales cycles and customized quotes, such as an event venue, a boutique agency or a B2B service provider, with one or two people manually triaging every inbound lead.
First working version
8 to 12 weeks
A planning estimate, firmed up once we see your real stack.
Foundation we would extend
Alfred OS
Our own Alfred OS, a multi-agent platform with a policy-check step before actions, is in active development. Its approach is what we'd extend here, not a finished product to resell. See our products
The problem
Who has this problem, and why it matters
Businesses with high-value inbound inquiries, such as weddings, events, B2B services, real estate and agencies, answer every lead by hand. Replies are slow, inconsistent and rarely use the business's own pricing and service details correctly.
Leads go cold while someone hunts for the right document or quote template. The business is usually losing deals to response time, not to the quality of its service.
Market
Where existing tools stop
AI sales assistant tools are everywhere. Many are chat widgets that answer FAQs but cannot check availability, build a real quote or write into the CRM. The ones that do integrate often rely on rigid, rule-based sequences rather than reading an inquiry and responding like a sharp salesperson.
The real gap is grounding: an agent that is actually right about pricing and availability, not just conversational. A confident wrong quote is worse than a slow right one.
This is our read of the market, not a formal study.
The solution
What we would build, in order
- 01
Knowledge layer
Build the agent's knowledge from the service catalog, pricing rules, FAQs and past successful replies.
- 02
Draft and approve
An agent inside the CRM reads each new inquiry and drafts a grounded, on-brand reply for a human to approve.
- 03
Live checks
Function calling into real systems to check availability, generate quotes and create follow-up tasks.
- 04
Gradual autonomy
Routine replies send on their own while anything unusual goes to a person.
- 05
Reporting
Response time, reply quality and lead-to-booking conversion tracked over time.
Architecture
How it works, end to end
Inquiry arrives
Form, email or message creates or matches a CRM record.
Retrieve what is true
The agent pulls the relevant catalog entries, pricing rules and similar past replies.
Check live systems
Tool calls confirm availability and build a quote from rules, not from the model's memory.
Policy check
A separate check blocks invented prices, off-limits promises and anything outside the agent's remit.
Human approval queue
A person reviews, edits or approves the draft. Autonomy is unlocked per reply type as trust grows.
Send and write back
The reply goes out and the CRM, follow-up tasks and reporting are updated.
Tech stack
What we would use, and why
CRM
Zoho, HubSpot or HighLevel APIs
The CRM stays the system of record. The agent works inside it.
Reasoning
LLM API with function calling
Lets the agent check live availability and build quotes instead of guessing.
Retrieval
Postgres with pgvector, or a vector database
Grounds replies in the business's own documents and keeps them current.
Guardrails
Deterministic policy checks before send
Rules the model cannot talk its way around, such as price limits and approval requirements.
Messaging
Email and calendar APIs
Sends replies and books calls from the same flow.
Evaluation
A test set of real past inquiries
Every prompt or document change is checked against known good replies before it ships.
Prototype vs production
A demo is not a product
You can prototype this yourself
- +A chat window over a handful of uploaded documents
- +Good for testing tone and the most common questions
- +A quick way to show the team what is possible
We encourage it. A working prototype is the best way to find out what you actually want.
What production needs
Retrieval that stays accurate
Pricing sheets and PDFs change. If the agent quotes last year's price, trust is gone after one mistake.
No invented prices or promises
Models fill gaps with confident guesses. Hard rules and live lookups have to cover anything the business could be held to.
Permissions and data scope
The agent should only see the records and documents it needs, nothing more.
CRM field mapping and deduplication
Real CRMs have messy fields and duplicate contacts. Write-back must not make the data worse.
Approval workflows
Who approves what, and what happens when no one does, determine whether leads still get answered.
Versioned prompts and regression tests
A small prompt change can quietly break a reply type. Each change is tested against real past inquiries.
Attachments and multi-language messages
Real inquiries include PDFs, photos and other languages.
Full logging
When a reply is questioned, you need to see what the agent said, what it read and why.
Our approach
Where we would start
We would start with the draft-and-approve flow before touching autonomy. Trust has to be earned on real inquiries, and a person approving every reply for the first weeks produces the evidence for what can safely be automated.
Grounding the agent against messy real-world documents, such as old PDFs and half-updated price sheets, is the slow part, so that comes before any interface work. We would build the evaluation set from the business's own past inquiries in the first phase and use it as the pass mark for every later change.
FAQ
Common questions
Will it send replies without anyone checking?+
Not at first. It drafts and a person approves. Autonomy is switched on one reply type at a time, only after the drafts for that type have been consistently right.
How do you stop it inventing prices?+
Prices come from rules and live lookups, not from the model's memory, and a separate policy check blocks any reply that states a number the system cannot trace to a source.
Does it work with our CRM?+
We would start with Zoho, HubSpot or HighLevel, which have the APIs this needs. Other CRMs are possible if they expose records, tasks and email through an API.
How long does a first version take?+
Our planning estimate is 8 to 12 weeks for a working draft-and-approve flow inside one CRM. We firm that up after seeing your documents and CRM setup.
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Bring your prototype
A half-formed idea, a working demo or a full spec. We will tell you honestly what is worth building first and what you can safely keep doing yourself. Typical response under 2 hours.