01
Qualification precision
Of the leads flagged as priority, how many actually advanced or closed.
Why the other one is not enough
Against "qualified leads", which goes up simply by lowering the threshold and says nothing.
AI for sales and prospecting
A system that flags leads as "hot" without anyone comparing that flag to what happened next is not qualifying: it is guessing confidently. We measure qualification against real closes in the CRM.
4–8 weeks to production · 1 week to validate qualification against your history · 48 hours to a proposal with fixed scope and price
- Lead qualification
- Automated follow-up
- Scheduling
- CRM updates
- HubSpot
- Salesforce
Built on
An engineering team that has already been on the other side.
Ninety seconds: who we are, how we work and what you get at the end.
The same sourced answer on every channel
WhatsApp, the website or email land in the same system: it understands the question, searches your documents, answers with a citation and hands over to a person when unsure.
Example: a rep needs a quote. The system builds it from current prices and what that customer already bought.
WhatsApp connected
Web connected
Email connected
01 Channels
02Understands
Reads the message
Detects the entityaccount = northern_distributor
Picks the sourcePrice_list_2026.xlsx
What should I quote Northern Distributor?
03Searches
94 of 100finds the right chunk
142 mstakes
Searches by meaning and by exact words, and filters by the tag before choosing.
04Answers with a source
They bought three times in 2025 at 12 % off. List price $2.4M; I propose $2.1M. Source: CRM plus price list, row 41.
answers with a source · 91 of 100
05Hands over
- Not enough backing
- A person on the team is notified
- Full conversation attached
- Human answer logged
Swipe to follow the flow →
The five holes in a commercial funnel
01
Leads going cold on slow follow-up
The first to respond usually wins, and the second touch rarely lands on time when it depends on somebody remembering.
02
Hours qualifying instead of hours closing
The expensive rep doing the cheap work is the most common waste in a sales team.
03
Manual scheduling and no-shows
Every meeting that is not confirmed is rep time that does not come back.
04
Long cycles from delays at the top
What is lost early in the funnel does not get recovered later with better closing.
05
A stale CRM
The conversation happened, the record does not reflect it, and the quarter’s forecast gets built on data nobody maintained.
01
The technical part
Qualifying is a prediction, and predictions get validated
When a system flags a lead as priority it is making a prediction about the future. And a prediction can be scored: take the CRM history, run qualification over leads whose outcome is already known, and compare. If the system would have prioritised what actually closed, it works. If not, what it adds is a label with the language of certainty over a hunch, and the team stops believing it within two weeks.
That validation against history is cheap — a week of work on data the company already has — and almost no vendor does it, because it forces you to show a number that might come out badly.
The second decision is where the system hands off. The agent converses, qualifies, books, confirms and writes to the CRM. It does not touch the closing conversation: the handoff is explicit and the context of what was said reaches the rep before the meeting, which is what turns the agent into an ally rather than a gate that gets in the way.
How it is measured
What gets reported, and what does not
02
Follow-up coverage
What share of leads received the contact they were due, inside the defined window.
Why the other one is not enough
Against "messages sent", which grows with volume even if nobody reads them.
03
Meetings that happened
Booked meetings that actually took place, not bookings alone.
Why the other one is not enough
No-shows are the hidden cost of badly built automated scheduling.
04
CRM record accuracy
On a reviewed sample: whether what got written reflects what happened in the conversation.
Why the other one is not enough
A CRM full of invented data is worse than an empty one, because the forecast gets built on top of it.
What we will not build
We do not build mass unsolicited prospecting systems. Beyond burning the company’s email domain and WhatsApp number, it is the fast route to a block you do not come back from.
And if the commercial team gets few leads a month, the problem is demand generation rather than qualification, and a qualification agent over an empty funnel has nothing to qualify. We say so on the first call when that is the case.
Related services
AI agents
Agents that do the work, not just talk about it
LangGraph orchestration, durable state and human approval on steps with consequences.
AI for marketing
AI for marketing
Segmentation and content at scale, with cost per asset under control.
AI chatbots and assistants
Chatbots for businesses of every size
With the evaluation set run before it ever reaches production.
Integrations and APIs
Connecting the systems that do not talk today
MCP servers against the systems already in operation.
Key takeaways
Four things before the call
- 01
The trap in this space is measuring leads generated instead of qualified leads that closed. The first always goes up; the second is what the business cares about.
- 02
Qualification gets validated against CRM history before it is allowed to decide anything: if the system would have flagged what actually closed, it works; if not, it is a layer of noise wearing the language of certainty.
- 03
Automated follow-up pays where humans fail on volume: the leads that go cold because nobody remembered. It does not pay by replacing the closing conversation.
- 04
If the system does not write to the CRM, it is not worth having. Automatic record updates are half the value and the half most projects leave undone.
This is where the data comes in
An AI system is worth what its sources are worth. These are the standard connectors; anything with an API or a database connects the same way, and what has no API is handled by file.
SAP
Enterprise ERP
Oracle
ERP and database
NetSuite
Cloud ERP
Salesforce
CRM and service
HubSpot
CRM and marketing
PostgreSQL
Database and pgvector
Microsoft SQL
Database
Snowflake
Data warehouse
BigQuery
Google data warehouse
Databricks
Data platform
Redshift
AWS data warehouse
Synapse
Azure data warehouse
Supabase
Managed Postgres
Workday
Payroll and HR
QuickBooks
Accounting
Sage
Accounting and ERP
Xero
Cloud accounting
Shopify
Catalogue and orders
WooCommerce
Catalogue and orders
Magento
Catalogue and orders
Stripe
Payments and subscriptions
Google Drive
Documents and folders
CSV y Excel
Flat files
Nothing in this category
A RAG system in production: three years, two million users
We built and operated the assistant for a loyalty platform serving more than two million active users across ten countries.
We built the full pipeline: document ingestion and normalization, chunking, embedding generation, vector store on Azure AI Search and Pinecone, and retrieval with grounded generation on LangChain.
We held it above 99% availability for three years.
RAG systems →Frequently asked questions
01 How do you know the system’s qualification is any good?
It gets validated against history before it operates. We take a set of leads whose outcome is already known, run qualification as if they were new, and compare against what actually happened. A number comes out: what share of the ones the system would have prioritised went on to advance. That number is handed over before anything is connected, and if it is bad we say so, because it beats the sales team discovering in month two that the label means nothing.
02 Does the agent talk to the prospect, or only to us?
Both configurations exist and the choice is yours. It can run inward — qualifying, prioritising and preparing context so the rep enters the conversation ready — or outward on WhatsApp or your site, answering, resolving questions and booking. In either case it does not touch the closing conversation, and the handoff to the person is explicit and carries the context of what was said.
03 Does it write to our CRM?
Yes, and for us it is not optional. An agent that converses and leaves no record forces the rep to transcribe, which is the work you were trying to remove. It writes the interaction, updates the stage and leaves notes, against HubSpot, Salesforce, Pipedrive or whatever you run, over API or an MCP server. And it gets measured: on a reviewed sample, whether what was written reflects what happened.
04 Does it work for a small sales team?
It works if there is lead volume, not if there are many reps. A team of three with four hundred leads a month gains more than one of fifteen with fifty large accounts and year-long cycles. In high-ticket consultative selling the bottleneck is the relationship, not the follow-up, and this adds little there — we say that before quoting.
05 What if we already have a site chatbot that does not convert?
Common, and it is almost never the model. The usual causes are that it is not connected to the CRM so the conversation is lost, that it cannot book so the interested person has to start over, or that it does not know when to hand off and frustrates exactly the person who was ready to buy. The diagnostic takes a week, returns the failures ranked by impact with each fix and its effort, and if it does not reach three actionable findings, it is not billed.
06 How long does it take and how is it priced?
Four to eight weeks to production depending on how many integrations are involved, with something running from week one. It is quoted with fixed scope, price and date in a proposal 48 hours after the first call. The qualification validation against history can be run first, as a diagnostic, with its cost credited if you go ahead with the project.
Fifteen minutes. A concrete answer.
It gets fixed, it gets built, or it is not worth it. And if the diagnostic does not reach three actionable findings, it is not charged.